From 0b4894d8f5f0638ede331b6100077027f36d7f3f Mon Sep 17 00:00:00 2001 From: Yoshiki Sakamoto Date: Tue, 16 Sep 2025 13:13:08 +0900 Subject: [PATCH 01/18] =?UTF-8?q?.gitignore=E3=81=ABClaude=20Code=E3=81=AE?= =?UTF-8?q?=E8=A8=AD=E5=AE=9A=E3=83=95=E3=82=A1=E3=82=A4=E3=83=AB=E3=82=92?= =?UTF-8?q?=E8=BF=BD=E5=8A=A0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .gitignore | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/.gitignore b/.gitignore index 5faa289d..81b8e0e4 100644 --- a/.gitignore +++ b/.gitignore @@ -166,3 +166,7 @@ cython_debug/ # Jupyter book _build/ .python-version + +# Claude Code configuration +AGENTS.md +CLAUDE.md From 255a5c0f26a57b1241476ca907b2ba7261a0ad16 Mon Sep 17 00:00:00 2001 From: Yoshiki Sakamoto Date: Tue, 16 Sep 2025 15:48:46 +0900 Subject: [PATCH 02/18] Add initial documentation files for JijModeling API --- reference/.readthedocs.yaml | 19 ++++++++++++ reference/conf.py | 62 +++++++++++++++++++++++++++++++++++++ reference/index.rst | 38 +++++++++++++++++++++++ reference/requirements.txt | 7 +++++ 4 files changed, 126 insertions(+) create mode 100644 reference/.readthedocs.yaml create mode 100644 reference/conf.py create mode 100644 reference/index.rst create mode 100644 reference/requirements.txt diff --git a/reference/.readthedocs.yaml b/reference/.readthedocs.yaml new file mode 100644 index 00000000..696ba341 --- /dev/null +++ b/reference/.readthedocs.yaml @@ -0,0 +1,19 @@ +version: 2 + +build: + os: ubuntu-24.04 + tools: + python: "3.12" + jobs: + post_install: + - python -c "import jijmodeling, os; print('JijModeling path:', os.path.dirname(jijmodeling.__file__))" + - sphinx-apidoc -o apidocs/ --force --separate $(python -c "import jijmodeling, os; print(os.path.dirname(jijmodeling.__file__))") + +python: + install: + - requirements: requirements.txt + +sphinx: + configuration: conf.py + +formats: ["htmlzip", "pdf", "epub"] diff --git a/reference/conf.py b/reference/conf.py new file mode 100644 index 00000000..ab0ae56b --- /dev/null +++ b/reference/conf.py @@ -0,0 +1,62 @@ +from datetime import date + +# --- 基本 --- +project = "JijModeling API" +author = "Jij Inc." +copyright = f"{date.today().year}, {author}" +language = "en" + +# --- テーマ --- +html_theme = "sphinx_rtd_theme" + +# --- 拡張 --- +extensions = [ + "sphinx.ext.autodoc", + "sphinx.ext.autosummary", # モジュール一覧を自動生成 + "sphinx.ext.napoleon", # Google/Numpy 形式 docstring + "sphinx.ext.intersphinx", # 外部ドキュメントへのリンク + "sphinx.ext.mathjax", # 数式サポート + "sphinx.ext.doctest", # doctest サポート + "sphinx.ext.viewcode", # ソースコード参照リンク + "sphinx_autodoc_typehints", # 型ヒント表示 + "sphinx_copybutton", # コードブロックにコピーボタン + "sphinx_design", # 現代的なデザイン要素 +] + +# --- autodoc 詳細設定 --- +autodoc_default_options = { + "members": True, # クラスや関数のメンバーを自動表示 + "undoc-members": True, # docstringがないメンバーも表示 + "show-inheritance": True, # 継承関係を表示 + "inherited-members": True, # 継承されたメンバーも表示 +} + +# --- autodoc member の順序 --- +autodoc_member_order = "bysource" # ソースコード順で表示 + +# --- autosummary 設定 --- +autosummary_generate = True # ビルド時に個別ページを自動生成 +autosummary_generate_overwrite = True # 既存ファイルを上書き +autosummary_imported_members = True # インポートされたメンバーも含める +autosummary_ignore_module_all = False # __all__ を無視しない + +# --- 型ヒント設定 --- +typehints_defaults = "comma" # デフォルト値をカンマ区切りで表示 +typehints_use_signature = True # シグネチャに型ヒントを表示 +typehints_use_signature_return = True # 戻り値の型もシグネチャに表示 + +# --- HTML テーマ設定 --- +html_theme_options = { + "navigation_depth": 5, # ナビゲーションの深度 + "collapse_navigation": True, # ナビゲーションを折りたたみ可能に + "sticky_navigation": True, # ナビゲーションを固定 + "includehidden": True, # 隠れた目次も含める + "titles_only": False, # タイトルのみではなくサブヘッダーも表示 +} + +# インタースフィンクス（Python 標準ライブラリなどへリンク） +intersphinx_mapping = { + "python": ("https://docs.python.org/3", None), + "typing_extensions": ("https://typing-extensions.readthedocs.io/en/latest", None), + "numpy": ("https://numpy.org/doc/stable", None), +} diff --git a/reference/index.rst b/reference/index.rst new file mode 100644 index 00000000..450a9fc7 --- /dev/null +++ b/reference/index.rst @@ -0,0 +1,38 @@ +JijModeling API Reference +============================ + +**JijModeling** is a mathematical optimization modeling library that provides an intuitive interface for formulating optimization problems. + +Quick Start +=========== + +.. code-block:: python + + import jijmodeling as jm + + # Create a problem + problem = jm.Problem("TSP") + + # Define variables + x = jm.BinaryVar("x", shape=(10, 10)) + + # Add objective and constraints + problem += jm.sum((i, j), x[i, j]) # Objective + problem += jm.Constraint("example", x.sum() == 1) # Constraint + +API Documentation +================= + +Complete automatically generated API documentation: + +.. toctree:: + :maxdepth: 4 + + apidocs/modules + +Indices and Tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` diff --git a/reference/requirements.txt b/reference/requirements.txt new file mode 100644 index 00000000..422913b6 --- /dev/null +++ b/reference/requirements.txt @@ -0,0 +1,7 @@ +jijmodeling==1.13.2 +sphinx>=7.3,<9.0 +sphinx-autodoc-typehints>=1.24.0,<4.0 +sphinx-rtd-theme>=2.0.0,<3.0 +myst-parser>=3.0.0,<4.0 +sphinx-copybutton>=0.5.0,<1.0 +sphinx-design>=0.6.0,<1.0 From dcffde2de18bcb6bf18bf6debaebb805059cc3e2 Mon Sep 17 00:00:00 2001 From: Yoshiki Sakamoto Date: Tue, 16 Sep 2025 15:56:23 +0900 Subject: [PATCH 03/18] =?UTF-8?q?ReadTheDocs=E7=94=A8=E3=81=ABJijModeling?= =?UTF-8?q?=E3=83=91=E3=83=83=E3=82=B1=E3=83=BC=E3=82=B8=E3=81=AE=E3=82=A4?= =?UTF-8?q?=E3=83=B3=E3=83=9D=E3=83=BC=E3=83=88=E5=87=A6=E7=90=86=E3=82=92?= =?UTF-8?q?=E8=BF=BD=E5=8A=A0=E3=81=97=E3=80=81sphinx-apidoc=E3=81=AE?= =?UTF-8?q?=E5=AE=9F=E8=A1=8C=E6=96=B9=E6=B3=95=E3=82=92=E4=BF=AE=E6=AD=A3?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- reference/.readthedocs.yaml | 3 +-- reference/conf.py | 10 ++++++++++ 2 files changed, 11 insertions(+), 2 deletions(-) diff --git a/reference/.readthedocs.yaml b/reference/.readthedocs.yaml index 696ba341..4711ab83 100644 --- a/reference/.readthedocs.yaml +++ b/reference/.readthedocs.yaml @@ -6,8 +6,7 @@ build: python: "3.12" jobs: post_install: - - python -c "import jijmodeling, os; print('JijModeling path:', os.path.dirname(jijmodeling.__file__))" - - sphinx-apidoc -o apidocs/ --force --separate $(python -c "import jijmodeling, os; print(os.path.dirname(jijmodeling.__file__))") + - python -c "import jijmodeling, os, subprocess; jm_path = os.path.dirname(jijmodeling.__file__); print('JijModeling path:', jm_path); subprocess.call(['sphinx-apidoc', '-o', 'apidocs/', '--force', '--separate', jm_path])" python: install: diff --git a/reference/conf.py b/reference/conf.py index ab0ae56b..f6198279 100644 --- a/reference/conf.py +++ b/reference/conf.py @@ -1,4 +1,14 @@ from datetime import date +import os +import sys + +# JijModelingパッケージのパスをsys.pathに追加（ReadTheDocs対応） +try: + import jijmodeling + # パッケージが正常にインストールされている場合はそのまま使用 +except ImportError: + # インストールされていない場合の対処（通常は発生しない） + pass # --- 基本 --- project = "JijModeling API" From 6d51229eb995afb102f4dd860b34b3816e9207d4 Mon Sep 17 00:00:00 2001 From: Yoshiki Sakamoto Date: Tue, 16 Sep 2025 15:59:49 +0900 Subject: [PATCH 04/18] =?UTF-8?q?sphinx-apidoc=E3=82=B3=E3=83=9E=E3=83=B3?= =?UTF-8?q?=E3=83=89=E3=81=AE=E5=AE=9F=E8=A1=8C=E6=99=82=E3=81=ABJijModeli?= =?UTF-8?q?ng=E3=81=AE=E3=83=91=E3=82=B9=E3=82=92=E5=87=BA=E5=8A=9B?= =?UTF-8?q?=E3=81=99=E3=82=8B=E5=87=A6=E7=90=86=E3=82=92=E5=89=8A=E9=99=A4?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- reference/.readthedocs.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/reference/.readthedocs.yaml b/reference/.readthedocs.yaml index 4711ab83..26561783 100644 --- a/reference/.readthedocs.yaml +++ b/reference/.readthedocs.yaml @@ -6,7 +6,7 @@ build: python: "3.12" jobs: post_install: - - python -c "import jijmodeling, os, subprocess; jm_path = os.path.dirname(jijmodeling.__file__); print('JijModeling path:', jm_path); subprocess.call(['sphinx-apidoc', '-o', 'apidocs/', '--force', '--separate', jm_path])" + - python -c "import jijmodeling, subprocess, os; subprocess.call(['sphinx-apidoc', '-o', 'apidocs/', '--force', '--separate', os.path.dirname(jijmodeling.__file__)])" python: install: From 6307262e2478de0fc57c1187f4a6865234093d34 Mon Sep 17 00:00:00 2001 From: Yoshiki Sakamoto Date: Tue, 16 Sep 2025 16:01:30 +0900 Subject: [PATCH 05/18] =?UTF-8?q?sphinx-apidoc=E3=81=AE=E5=AE=9F=E8=A1=8C?= =?UTF-8?q?=E6=96=B9=E6=B3=95=E3=82=92=E4=BF=AE=E6=AD=A3=E3=81=97=E3=80=81?= =?UTF-8?q?JijModeling=E3=81=AE=E3=83=91=E3=82=B9=E3=82=92=E5=87=BA?= =?UTF-8?q?=E5=8A=9B=E3=81=99=E3=82=8B=E5=87=A6=E7=90=86=E3=82=92=E8=BF=BD?= =?UTF-8?q?=E5=8A=A0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- reference/.readthedocs.yaml | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/reference/.readthedocs.yaml b/reference/.readthedocs.yaml index 26561783..a6dd89f3 100644 --- a/reference/.readthedocs.yaml +++ b/reference/.readthedocs.yaml @@ -6,7 +6,10 @@ build: python: "3.12" jobs: post_install: - - python -c "import jijmodeling, subprocess, os; subprocess.call(['sphinx-apidoc', '-o', 'apidocs/', '--force', '--separate', os.path.dirname(jijmodeling.__file__)])" + - | + JM_PATH=$(python -c "import jijmodeling, os; print(os.path.dirname(jijmodeling.__file__))") + echo "JijModeling path: $JM_PATH" + sphinx-apidoc -o apidocs/ --force --separate "$JM_PATH" python: install: From dbab157a3faa111bcb6ac76fcae4a003bb280ead Mon Sep 17 00:00:00 2001 From: Yoshiki Sakamoto Date: Tue, 16 Sep 2025 17:31:02 +0900 Subject: [PATCH 06/18] =?UTF-8?q?ReadTheDocs=E7=94=A8=E3=81=AE=E8=A8=AD?= =?UTF-8?q?=E5=AE=9A=E3=82=92=E6=95=B4=E7=90=86=E3=81=97=E3=80=81=E4=B8=8D?= =?UTF-8?q?=E8=A6=81=E3=81=AA=E3=83=91=E3=82=B9=E5=87=BA=E5=8A=9B=E5=87=A6?= =?UTF-8?q?=E7=90=86=E3=82=92=E5=89=8A=E9=99=A4=E3=80=82API=E3=83=89?= =?UTF-8?q?=E3=82=AD=E3=83=A5=E3=83=A1=E3=83=B3=E3=83=88=E3=81=AE=E6=A7=8B?= =?UTF-8?q?=E6=88=90=E3=82=92=E6=94=B9=E5=96=84=E3=81=97=E3=80=81=E4=BE=9D?= =?UTF-8?q?=E5=AD=98=E9=96=A2=E4=BF=82=E3=82=92=E6=9B=B4=E6=96=B0=E3=80=82?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- reference/.readthedocs.yaml | 6 ---- reference/conf.py | 61 +++++++++------------------------- reference/index.rst | 66 +++++++++++++++++++++++++++---------- reference/requirements.txt | 3 -- 4 files changed, 64 insertions(+), 72 deletions(-) diff --git a/reference/.readthedocs.yaml b/reference/.readthedocs.yaml index a6dd89f3..0d2fa820 100644 --- a/reference/.readthedocs.yaml +++ b/reference/.readthedocs.yaml @@ -4,12 +4,6 @@ build: os: ubuntu-24.04 tools: python: "3.12" - jobs: - post_install: - - | - JM_PATH=$(python -c "import jijmodeling, os; print(os.path.dirname(jijmodeling.__file__))") - echo "JijModeling path: $JM_PATH" - sphinx-apidoc -o apidocs/ --force --separate "$JM_PATH" python: install: diff --git a/reference/conf.py b/reference/conf.py index f6198279..dba17da1 100644 --- a/reference/conf.py +++ b/reference/conf.py @@ -1,14 +1,4 @@ from datetime import date -import os -import sys - -# JijModelingパッケージのパスをsys.pathに追加（ReadTheDocs対応） -try: - import jijmodeling - # パッケージが正常にインストールされている場合はそのまま使用 -except ImportError: - # インストールされていない場合の対処（通常は発生しない） - pass # --- 基本 --- project = "JijModeling API" @@ -21,48 +11,29 @@ # --- 拡張 --- extensions = [ - "sphinx.ext.autodoc", - "sphinx.ext.autosummary", # モジュール一覧を自動生成 - "sphinx.ext.napoleon", # Google/Numpy 形式 docstring + "sphinx.ext.autodoc", # 自動API生成（標準） "sphinx.ext.intersphinx", # 外部ドキュメントへのリンク "sphinx.ext.mathjax", # 数式サポート - "sphinx.ext.doctest", # doctest サポート - "sphinx.ext.viewcode", # ソースコード参照リンク - "sphinx_autodoc_typehints", # 型ヒント表示 - "sphinx_copybutton", # コードブロックにコピーボタン - "sphinx_design", # 現代的なデザイン要素 + "myst_parser", # Markdown docstring サポート ] -# --- autodoc 詳細設定 --- +# --- Autodoc 設定 --- autodoc_default_options = { - "members": True, # クラスや関数のメンバーを自動表示 - "undoc-members": True, # docstringがないメンバーも表示 - "show-inheritance": True, # 継承関係を表示 - "inherited-members": True, # 継承されたメンバーも表示 + "members": True, + "undoc-members": True, + "show-inheritance": True, + "inherited-members": True, + "special-members": "__init__", } +autodoc_member_order = "bysource" -# --- autodoc member の順序 --- -autodoc_member_order = "bysource" # ソースコード順で表示 - -# --- autosummary 設定 --- -autosummary_generate = True # ビルド時に個別ページを自動生成 -autosummary_generate_overwrite = True # 既存ファイルを上書き -autosummary_imported_members = True # インポートされたメンバーも含める -autosummary_ignore_module_all = False # __all__ を無視しない - -# --- 型ヒント設定 --- -typehints_defaults = "comma" # デフォルト値をカンマ区切りで表示 -typehints_use_signature = True # シグネチャに型ヒントを表示 -typehints_use_signature_return = True # 戻り値の型もシグネチャに表示 - -# --- HTML テーマ設定 --- -html_theme_options = { - "navigation_depth": 5, # ナビゲーションの深度 - "collapse_navigation": True, # ナビゲーションを折りたたみ可能に - "sticky_navigation": True, # ナビゲーションを固定 - "includehidden": True, # 隠れた目次も含める - "titles_only": False, # タイトルのみではなくサブヘッダーも表示 -} +# --- MyST Parser 設定 --- +myst_enable_extensions = [ + "colon_fence", # ::: fence + "deflist", # definition lists + "html_admonition", # HTML admonitions +] +autodoc_docstring_signature = True # インタースフィンクス（Python 標準ライブラリなどへリンク） intersphinx_mapping = { diff --git a/reference/index.rst b/reference/index.rst index 450a9fc7..f4d81c13 100644 --- a/reference/index.rst +++ b/reference/index.rst @@ -3,34 +3,64 @@ JijModeling API Reference **JijModeling** is a mathematical optimization modeling library that provides an intuitive interface for formulating optimization problems. -Quick Start -=========== +Core API +======== -.. code-block:: python +The main ``jijmodeling`` module contains all the essential classes and functions for mathematical optimization modeling. - import jijmodeling as jm +.. automodule:: jijmodeling + :members: + :undoc-members: + :show-inheritance: + :special-members: __init__ - # Create a problem - problem = jm.Problem("TSP") +Dataset Module +============== - # Define variables - x = jm.BinaryVar("x", shape=(10, 10)) +Utilities for handling optimization data. - # Add objective and constraints - problem += jm.sum((i, j), x[i, j]) # Objective - problem += jm.Constraint("example", x.sum() == 1) # Constraint +.. automodule:: jijmodeling.dataset + :members: + :undoc-members: + :show-inheritance: -API Documentation -================= +Experimental Features +===================== -Complete automatically generated API documentation: +Experimental and preview features. -.. toctree:: - :maxdepth: 4 +.. automodule:: jijmodeling.experimental + :members: + :undoc-members: + :show-inheritance: - apidocs/modules +Range Operations +================ -Indices and Tables +Tools for working with variable ranges and indices. + +.. automodule:: jijmodeling.range + :members: + :undoc-members: + :show-inheritance: + +Range Size Utilities +--------------------- + +.. automodule:: jijmodeling.range.size + :members: + :undoc-members: + :show-inheritance: + +Range Value Utilities +---------------------- + +.. automodule:: jijmodeling.range.value + :members: + :undoc-members: + :show-inheritance: + +Indices and Search ================== * :ref:`genindex` diff --git a/reference/requirements.txt b/reference/requirements.txt index 422913b6..9a478c78 100644 --- a/reference/requirements.txt +++ b/reference/requirements.txt @@ -1,7 +1,4 @@ jijmodeling==1.13.2 sphinx>=7.3,<9.0 -sphinx-autodoc-typehints>=1.24.0,<4.0 sphinx-rtd-theme>=2.0.0,<3.0 myst-parser>=3.0.0,<4.0 -sphinx-copybutton>=0.5.0,<1.0 -sphinx-design>=0.6.0,<1.0 From 9ad72240824d5c5c15c10a0063d2a733067d0bb7 Mon Sep 17 00:00:00 2001 From: Yoshiki Sakamoto Date: Tue, 16 Sep 2025 17:34:21 +0900 Subject: [PATCH 07/18] =?UTF-8?q?ReadTheDocs=E8=A8=AD=E5=AE=9A=E3=82=92?= =?UTF-8?q?=E6=9B=B4=E6=96=B0=E3=81=97=E3=80=81requirements=E3=83=95?= =?UTF-8?q?=E3=82=A1=E3=82=A4=E3=83=AB=E3=81=AE=E3=83=91=E3=82=B9=E3=81=A8?= =?UTF-8?q?Sphinx=E3=81=AE=E8=A8=AD=E5=AE=9A=E3=83=95=E3=82=A1=E3=82=A4?= =?UTF-8?q?=E3=83=AB=E3=81=AE=E3=83=91=E3=82=B9=E3=82=92=E4=BF=AE=E6=AD=A3?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- reference/.readthedocs.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/reference/.readthedocs.yaml b/reference/.readthedocs.yaml index 0d2fa820..113d1b22 100644 --- a/reference/.readthedocs.yaml +++ b/reference/.readthedocs.yaml @@ -7,9 +7,9 @@ build: python: install: - - requirements: requirements.txt + - requirements: reference/requirements.txt sphinx: - configuration: conf.py + configuration: reference/conf.py formats: ["htmlzip", "pdf", "epub"] From 8645674b5d88b500ee534ecc35e8de8f949ffce9 Mon Sep 17 00:00:00 2001 From: Yoshiki Sakamoto Date: Wed, 17 Sep 2025 08:06:18 +0900 Subject: [PATCH 08/18] =?UTF-8?q?.gitignore=E3=81=ABSphinx=E3=83=93?= =?UTF-8?q?=E3=83=AB=E3=83=89=E7=94=A8=E3=81=AEapidoocs=E3=83=87=E3=82=A3?= =?UTF-8?q?=E3=83=AC=E3=82=AF=E3=83=88=E3=83=AA=E3=82=92=E8=BF=BD=E5=8A=A0?= =?UTF-8?q?=E3=81=97=E3=80=81Claude=20Code=E3=81=AE=E8=A8=AD=E5=AE=9A?= =?UTF-8?q?=E3=82=92=E6=9B=B4=E6=96=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .gitignore | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/.gitignore b/.gitignore index 81b8e0e4..8e52fcc5 100644 --- a/.gitignore +++ b/.gitignore @@ -167,6 +167,10 @@ cython_debug/ _build/ .python-version +# Sphinx build +apidocs/ + # Claude Code configuration AGENTS.md CLAUDE.md +.claude/ From 27d05504b4b4d4c8630ba241d86e32aa7dc3d44d Mon Sep 17 00:00:00 2001 From: Yoshiki Sakamoto Date: Wed, 17 Sep 2025 08:16:44 +0900 Subject: [PATCH 09/18] =?UTF-8?q?MyST=20Parser=E3=81=AE=E3=82=B5=E3=83=9D?= =?UTF-8?q?=E3=83=BC=E3=83=88=E3=82=92=E5=89=8A=E9=99=A4=E3=81=97=E3=80=81?= =?UTF-8?q?requirements.txt=E3=81=AESphinx=E3=81=A8sphinx-rtd-theme?= =?UTF-8?q?=E3=81=AE=E3=83=90=E3=83=BC=E3=82=B8=E3=83=A7=E3=83=B3=E3=82=92?= =?UTF-8?q?=E6=9B=B4=E6=96=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- reference/conf.py | 8 -------- reference/requirements.txt | 1 - 2 files changed, 9 deletions(-) diff --git a/reference/conf.py b/reference/conf.py index dba17da1..c845df07 100644 --- a/reference/conf.py +++ b/reference/conf.py @@ -14,7 +14,6 @@ "sphinx.ext.autodoc", # 自動API生成（標準） "sphinx.ext.intersphinx", # 外部ドキュメントへのリンク "sphinx.ext.mathjax", # 数式サポート - "myst_parser", # Markdown docstring サポート ] # --- Autodoc 設定 --- @@ -26,13 +25,6 @@ "special-members": "__init__", } autodoc_member_order = "bysource" - -# --- MyST Parser 設定 --- -myst_enable_extensions = [ - "colon_fence", # ::: fence - "deflist", # definition lists - "html_admonition", # HTML admonitions -] autodoc_docstring_signature = True # インタースフィンクス（Python 標準ライブラリなどへリンク） diff --git a/reference/requirements.txt b/reference/requirements.txt index 9a478c78..37965cd0 100644 --- a/reference/requirements.txt +++ b/reference/requirements.txt @@ -1,4 +1,3 @@ jijmodeling==1.13.2 sphinx>=7.3,<9.0 sphinx-rtd-theme>=2.0.0,<3.0 -myst-parser>=3.0.0,<4.0 From 0bb13d00a7c8b8aac1943652baa5ae5ee1c60075 Mon Sep 17 00:00:00 2001 From: Yoshiki Sakamoto Date: Wed, 17 Sep 2025 08:16:55 +0900 Subject: [PATCH 10/18] =?UTF-8?q?requirements.txt=E3=81=AESphinx=E3=81=A8s?= =?UTF-8?q?phinx-rtd-theme=E3=81=AE=E3=83=90=E3=83=BC=E3=82=B8=E3=83=A7?= =?UTF-8?q?=E3=83=B3=E3=82=92=E6=9B=B4=E6=96=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- reference/requirements.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/reference/requirements.txt b/reference/requirements.txt index 37965cd0..2b001e11 100644 --- a/reference/requirements.txt +++ b/reference/requirements.txt @@ -1,3 +1,3 @@ jijmodeling==1.13.2 -sphinx>=7.3,<9.0 -sphinx-rtd-theme>=2.0.0,<3.0 +sphinx>=8.0,<9.0 +sphinx-rtd-theme>=3.0,<4.0 From 9a1859b63da6ac4d47088110a0d08127f74c3675 Mon Sep 17 00:00:00 2001 From: Yoshiki Sakamoto Date: Wed, 17 Sep 2025 08:17:36 +0900 Subject: [PATCH 11/18] Refactor code structure for improved readability and maintainability --- apis/index.html | 7 - apis/jijmodeling.html | 6836 ------------------------ apis/jijmodeling/_jijmodeling.html | 7869 ---------------------------- apis/jijmodeling/dataset.html | 520 -- apis/jijmodeling/experimental.html | 1325 ----- apis/jijmodeling/range.html | 278 - apis/jijmodeling/range/size.html | 273 - apis/jijmodeling/range/value.html | 273 - apis/search.js | 46 - 9 files changed, 17427 deletions(-) delete mode 100644 apis/index.html delete mode 100644 apis/jijmodeling.html delete mode 100644 apis/jijmodeling/_jijmodeling.html delete mode 100644 apis/jijmodeling/dataset.html delete mode 100644 apis/jijmodeling/experimental.html delete mode 100644 apis/jijmodeling/range.html delete mode 100644 apis/jijmodeling/range/size.html delete mode 100644 apis/jijmodeling/range/value.html delete mode 100644 apis/search.js diff --git a/apis/index.html b/apis/index.html deleted file mode 100644 index 5945591a..00000000 --- a/apis/index.html +++ /dev/null @@ -1,7 +0,0 @@ - - - - - - - diff --git a/apis/jijmodeling.html b/apis/jijmodeling.html deleted file mode 100644 index 45e05b1d..00000000 --- a/apis/jijmodeling.html +++ /dev/null @@ -1,6836 +0,0 @@ - - - - - - - jijmodeling API documentation - - - - - - - - - - - - -
-
-

-jijmodeling

- - - - - - - 
1from ._jijmodeling import *  # type: ignore
-
- - -
-
-
- - class - ModelingError(builtins.Exception): - - -
- - -

Error while creating a model.

-
- - -
-
-
- - class - InterpreterError(builtins.RuntimeError): - - -
- - -

Error while interpreting the model.

-
- - -
-
-
- - class - ProtobufSerializationError(builtins.Exception): - - -
- - -

Failed to encode the object to a buffer.

-
- - -
-
-
- - class - ProtobufDeserializationError(builtins.Exception): - - -
- - -

Failed to decode the buffer to an instance.

-
- - -
-
-
- - class - DataType: - - -
- - - - -
-
- FLOAT = -DataType.FLOAT - - -
- - - - -
-
-
- INTEGER = -DataType.INTEGER - - -
- - - - -
-
-
-
- - class - NumberLit: - - -
- - -

A class for creating a number literal

- -

The NumberLit class is used to create a number literal. -Its instance is automatically generated by the return value of -arithmetic or mathematical functions taking a number literal and -an object defined by jijmodeling as arguments.

- -

Attributes

- - - -

Args

- -
    -
  • value (int | float): A numeric value.
    • -
- -

Examples

- -

Create a number literal with a integer value 123.

- -
-
>>> import jijmodeling as jm
->>> v = jm.NumberLit(123)
->>> assert v.value == 123
->>> assert v.dtype == jm.DataType.INTEGER
-
-
- -

Create a number literal with a float value 1.23.

- -
-
>>> import jijmodeling as jm
->>> v = jm.NumberLit(1.23)
->>> assert v.value == 1.23
->>> assert v.dtype == jm.DataType.FLOAT
-
-
-
- - -
-
- value - - -
- - - - -
-
-
- dtype - - -
- - - - -
-
-
-
- - class - Placeholder: - - -
- - -

A class for creating a placeholder

- -

The Placeholder class is used to create a placeholder. -It is a symbol to be replaced by a numerical value when you solve an optimization problem.

- -

The index operator ([]) of a placeholder with ndim >= 1 returns a Subscript object.

- -

Attributes

- -
    -
  • name (str): A name of the placeholder.
    • -
  • ndim (int): The number of dimensions of the placeholder.
    • -
  • shape (list of Optional[Expression], optional): The (partial) shape of the placeholder if given.
    • -
  • dtype (DataType, optional): The data type (DataType.INT or DataType.FLOAT) of the placeholder.
    • -
  • jagged (boolean, defaut: False): True if the placeholder will be treated as a jagged array in random data generation. Ignored for scalars.
    • -
  • description (str, optional): A description of the placeholder.
    • -
- -

Args

- -
    -
  • name (str): A name of the placeholder.
    • -
  • ndim (Optional[int]): The number of dimensions of the placeholder. Defaults to 0. The ndim must be set to a non-negative value and must concide with the length of shape if both specified. If None is given, you must specify shape explicitly and the length of shape will be used.
    • -
  • shape (list[Optional[Expression]], optional): The (partial) shape of the placeholder. Used for random data generation.
    • -
  • dtype (DataType, optional): The data type (DataType.INT or DataType.FLOAT) of the placeholder. Used for random data generation.
    • -
  • jagged (boolean, defaut: False): True if the placeholder will be treated as a jagged array in random data generation. Ignored for scalars.
    • -
  • latex (str, optional): A LaTeX-name of the placeholder to be represented in Jupyter notebook. -It is set to name by default.
    • -
  • description (str, optional): A description of the placeholder.
    • -
- -

Raises

- -
    -
  • TypeError: Raises if set a float value to ndim.
    • -
  • OverflowError: Raises if set a negative value to ndim.
    • -
- -

Examples

- -

Create a scalar (or ndim is 0) placeholder whose name is "a".

- -
-
>>> import jijmodeling as jm
->>> a = jm.Placeholder("a")
-
-
- -

Create a 2-dimensional placeholder whose name is "m".

- -
-
>>> import jijmodeling as jm
->>> m = jm.Placeholder("m", ndim=2)
-
-
- -

Create a 1-dimensional placeholder with the index of 123.

- -
-
>>> import jijmodeling as jm
->>> a = jm.Placeholder("a", ndim=2)
->>> a[123]
-Placeholder(name='a', ndim=2)[NumberLit(value=123)]
-
-
-
- - -
-
- - def - len_at(self, /, axis, *, latex=None, description=None): - - -
- - - - -
-
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- ndim - - -
- - - - -
-
-
- shape - - -
- - - - -
-
-
- description - - -
- - - - -
-
-
- name - - -
- - - - -
-
-
- dtype - - -
- - - - -
-
-
- jagged - - -
- - - - -
-
-
-
- - class - Range: - - -
- - -

A class representing a half-open interval.

- -

The Range class is used to represent a half-open interval [start, end). -This class does not have a constructor because it should be created by the Element class.

- -

Attributes

- -
    -
  • start: The lower bound of the range (inclusive).
    • -
  • end: The upper bound of the range (exclusive).
    • -
- -

Note

- -

This class does not contain any decision variable.

-
- - -
-
- start - - -
- - - - -
-
-
- end - - -
- - - - -
-
-
-
- - class - Element: - - -
- - -

A class for creating an element

- -

The Element class is used to create an element. -It is used in the following cases:

- -
    -
  • an index of summation $\displaystyle \sum$ (SumOp)
    • -
  • an index of product $\displaystyle \prod$ (ProdOp)
    • -
  • a bound variable of the universal quantifier $\forall$ (Forall)
    • -
- -

Elements specify a set to which they belong. The set can be:

- -
    -
  1. A half-open range, where the lower bound is included and the upper bound is excluded.
    2. -
  2. A Placeholder, Element, or Subscript object with ndim >= 1.
    3. -
- -

Ranges are generally specified with tuples as (start, end). For -convenience, passing a single number or scalar object as the argument is -interpreted as the end of a range starting from zero.

- -

The index operator ([]) of an element with ndim >= 1 returns a Subscript object.

- -

Attributes

- -
    -
  • name (str): A name of the element.
    • -
  • ndim (int): The number of dimensions of the element. The value is one less than the value of belong_to.ndim.
    • -
  • description (str): A description of the element.
    • -
  • belong_to: A set the element belongs to.
    • -
- -

Args

- -
    -
  • name (str): A name of the element.
    • -
  • belong_to: A set the element belongs to.
    • -
  • latex (str, optional): A LaTeX-name of the element to be represented in Jupyter notebook. -
      -
    • It is set to name by default.
      • -
    • -
  • description (str, optional): A description of the element.
    • -
- -

Examples

- -

Note that belong_to is a positional argument, not a keyword -argument, and so does not need to be written out. This is done in some -of these examples for clarity.

- -

Create an element that belongs to a half-open range.

- -
-
>>> import jijmodeling as jm
->>> i = jm.Element("i", belong_to=(0,10))
-
-
- -

If you pass a scalar as the belong_to argument, the set that the element belongs to is a range starting at 0 going up to that value.

- -
-
>>> import jijmodeling as jm
->>> i = jm.Element("i", 10)
->>> assert jm.is_same(i, jm.Element("i", belong_to=(0,10)))
-
-
- -

The applies not just to numbers, but certain scalars, like Placeholder (with ndim == 0).

- -
-
>>> import jijmodeling as jm
->>> n = jm.Placeholder("N")
->>> i = jm.Element("i", n)
->>> assert jm.is_same(i, jm.Element("i", belong_to=(0,n)))
-
-
- -

Create an element that belongs to a 1-dimensional placeholder.

- -
-
>>> import jijmodeling as jm
->>> E = jm.Placeholder("E", ndim=1)
->>> e = jm.Element("e", E)
-
-
- -

Create a 1-dimensional element with the index of 123.

- -
-
>>> import jijmodeling as jm
->>> a = jm.Placeholder("a", ndim=2)
->>> e = jm.Element("e", a)
->>> e[123]
-Element(name='e', belong_to=Placeholder(name='a', ndim=2))[NumberLit(value=123)]
-
-
-
- - -
-
- - def - len_at(self, /, axis, *, latex=None, description=None): - - -
- - - - -
-
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- name - - -
- - - - -
-
-
- ndim - - -
- - - - -
-
-
- belong_to - - -
- - - - -
-
-
- shape - - -
- - - - -
-
-
- description - - -
- - - - -
-
-
-
- - class - Subscript: - - -
- - -

A class for representing a subscripted variable

- -

The Subscript class is used to represent a variable with subscriptions.

- -

Attributes

- -
    -
  • variable: A variable that has subscripts.
    • -
  • subscripts (list): A list of subscripts.
    • -
  • ndim (int): The number of dimensions of the subscripted variable.
    • -
- -

Note

- -

The Subscript class does not have a constructor.

-
- - -
-
- - def - len_at(self, /, axis, *, latex=None, description=None): - - -
- - - - -
-
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- shape - - -
- - - - -
-
-
- ndim - - -
- - - - -
-
-
- subscripts - - -
- - - - -
-
-
- variable - - -
- - - - -
-
-
-
- - class - ArrayLength: - - -
- - -

A class for referring to the length of an array at a given axis.

- -

The ArrayLength class is to refer to the number of elements of an axis in an array.

- -

This class is not intended to be constructed directly. Instead, we -recommend using the len_at method of Placeholder, Element or -Subscript.

- -

Attributes

- -
    -
  • array: A variable with ndim >= 1.
    • -
  • axis: An axis index. A $n$-dimensional variable has $n$ axes. Axis 0 is the array's outermost axis and $n-1$ is the innermost.
    • -
  • description (str, optional): A description of the ArrayLength instance.
    • -
- -

Raises

- -

ModelingError: Raises if axis >= array.ndim.

- -

Examples

- -

Create a length of axis 0 in a 2-dimensional placeholder.

- -
-
>>> import jijmodeling as jm
->>> a = jm.Placeholder("a", ndim=2)
->>> N = a.len_at(0, latex="N")
-
-
-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- axis - - -
- - - - -
-
-
- array - - -
- - - - -
-
-
- description - - -
- - - - -
-
-
-
- - class - DummyIndexedVar: - - -
- - -

A class for representing a subscripted variable with dummy indices

- -

The DummyIndexedVar class is an intermediate representation to support syntactic sugar of sum/prod with slices.

- -

Note

- -

The DummyIndexedVar class does not have a constructor.

-
- - -
-
- - def - sum(self, /): - - -
- - -

Take a sum of the decision variable over the elements for which the slice is given and return a SumOp object.

- -

Returns

- -

SumOp: A SumOp object taken a sum of the decision variable over the elements for which the slice is given.

- -

Note

- -

An automatically created dummy index

- -
    -
  • has a name of the form __dummy_{decision_var.name}_{axis} where axis is the axis of the slice.
    • -
  • belongs to a range whose start defaults to 0 and end defaults to the length of the axis.
    • -
  • has description of the form dummy index at {axis} for {decision_var.name}.
    • -
  • has latex string of the form \\ast_{axis}.
    • -
- -

Examples

- -

Create a SumOp object taken a sum of the 2-dim binary variable over the 0-th elements for which the slice is given.

- -
-
>>> import jijmodeling as jm
->>> n = jm.Placeholder("n")
->>> x = jm.BinaryVar("x", shape=(n, n))
->>> i = jm.Element("__dummy_x_0", belong_to=n)
->>> j = jm.Element("j", belong_to=n)
->>> assert jm.is_same(x[:, j].sum(), jm.sum(i, x[i, j]))
-
-
-
- - -
-
-
- - def - prod(self, /): - - -
- - -

Take a prod of the decision variable over the elements for which the slice is given and return a ProdOp object.

- -

Returns

- -

ProdOp: A ProdOp object taken a prod of the decision variable over the elements for which the slice is given.

- -

Note

- -

An automatically created dummy index

- -
    -
  • has a name of the form __dummy_{decision_var.name}_{axis} where axis is the axis of the slice.
    • -
  • belongs to a range whose start defaults to 0 and end defaults to the length of the axis.
    • -
  • has description of the form dummy index at {axis} for {decision_var.name}.
    • -
  • has latex string of the form \\ast_{axis}.
    • -
- -

Examples

- -

Create a ProdOp object taken a prod of the 2-dim binary variable over the 0-th elements for which the slice is given.

- -
-
>>> import jijmodeling as jm
->>> n = jm.Placeholder("n")
->>> x = jm.BinaryVar("x", shape=(n, n))
->>> i = jm.Element("__dummy_x_0", belong_to=n)
->>> j = jm.Element("j", belong_to=n)
->>> assert jm.is_same(x[:, j].prod(), jm.prod(i, x[i, j]))
-
-
-
- - -
-
-
-
- - class - BinaryVar: - - -
- - -

A class for creating a binary variable

- -

The BinaryVar class is used to create a binary variable.

- -

The index operator ([]) of a binary variable with ndim >= 1 returns a Subscript object.

- -

Attributes

- -
    -
  • name (str): A name of the binary variable.
    • -
  • shape (tuple): A tuple with the size of each dimension of the binary variable. Empty if the variable is not multi-dimensional.
    • -
  • description (str): A description of the binary variable.
    • -
- -

Args

- -
    -
  • name (str): A name of the binary variable.
    • -
  • shape (list | tuple): A sequence with the size of each dimension of the binary variable. Defaults to an empty tuple (a scalar value). -
      -
    • Each item in shape must be a valid expression evaluating to a non-negative scalar.
      • -
    • -
  • latex (str, optional): A LaTeX-name of the binary variable to be represented in Jupyter notebook. -
      -
    • It is set to name by default.
      • -
    • -
  • description (str, optional): A description of the binary variable.
    • -
- -

Examples

- -

Create a scalar binary variable whose name is "z".

- -
-
>>> import jijmodeling as jm
->>> z = jm.BinaryVar("z")
-
-
- -

Create a 2-dimensional binary variable whose name is "x" and has a 2x2 shape.

- -
-
>>> import jijmodeling as jm
->>> x = jm.BinaryVar("x", shape=[2, 2])
-
-
- -

Create a 1-dimensional binary variable with the index of 123.

- -
-
>>> import jijmodeling as jm
->>> x = jm.BinaryVar("x", shape=[124])
->>> x[123]
-BinaryVar(name='x', shape=[NumberLit(value=124)])[NumberLit(value=123)]
-
-
-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- ndim - - -
- - - - -
-
-
- name - - -
- - - - -
-
-
- description - - -
- - - - -
-
-
- shape - - -
- - - - -
-
-
-
- - class - IntegerVar: - - -
- - -

A class for creating an integer variable

- -

The IntegerVar class is used to create an integer variable. -The lower and upper bounds of the variable can be specified by:

- -
    -
  • an integer value
    • -
  • a float value
    • -
  • a scalar expression that does not contains any decision variable
    • -
  • a Placeholder object whose dimensionality is equal to that of this variable.
    • -
  • a subscripted variable whose dimensionality is equal to that of this variable.
    • -
- -

The index operator ([]) of a variable with ndim >= 1 returns a Subscript object.

- -

Attributes

- -
    -
  • name (str): A name of the integer variable.
    • -
  • shape (tuple): A tuple with the size of each dimension of the integer variable. Empty if the variable is not multi-dimensional.
    • -
  • lower_bound: The lower bound of the variable.
    • -
  • upper_bound: The upper bound of the variable.
    • -
  • description (str): A description of the integer variable.
    • -
- -

Args

- -
    -
  • name (`str): A name of the integer variable.
    • -
  • shape (list | tuple): A sequence with the size of each dimension of the integer variable. Defaults to an empty tuple (a scalar value). -
      -
    • Each item in shape must be a valid expression evaluating to a non-negative scalar.
      • -
    • -
  • lower_bound: The lower bound of the variable.
    • -
  • upper_bound: The upper bound of the variable.
    • -
  • latex (str, optional): A LaTeX-name of the integer variable to be represented in Jupyter notebook. -
      -
    • It is set to name by default.
      • -
    • -
  • description (str, optional): A description of the integer variable.
    • -
- -

Raises

- -

ModelingError: Raises if a bound is a Placeholder or Subscript object whose ndim -is neither 0 nor the same value as ndim of the integer variable.

- -

Examples

- -

Create a scalar integer variable whose name is "z" and domain is [-1, 1].

- -
-
>>> import jijmodeling as jm
->>> z = jm.IntegerVar("z", lower_bound=-1, upper_bound=1)
-
-
- -

Create a 2-dimensional integer variable...

- -
    -
  • whose name is "x".
    • -
  • whose domain is [0, 2].
    • -
  • where each dimension has length 2 (making this a 2x2 matrix).
    • -
- -
-
>>> import jijmodeling as jm
->>> x = jm.IntegerVar("x", shape=[2, 2], lower_bound=0, upper_bound=2)
-
-
- -

Create a 1-dimensional integer variable with the index of 123.

- -
-
>>> import jijmodeling as jm
->>> x = jm.IntegerVar("x", shape=[124], lower_bound=0, upper_bound=2)
->>> x[123]
-IntegerVar(name='x', shape=[NumberLit(value=124)], lower_bound=NumberLit(value=0), upper_bound=NumberLit(value=2))[NumberLit(value=123)]
-
-
-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- name - - -
- - - - -
-
-
- ndim - - -
- - - - -
-
-
- upper_bound - - -
- - - - -
-
-
- shape - - -
- - - - -
-
-
- lower_bound - - -
- - - - -
-
-
- description - - -
- - - - -
-
-
-
- - class - ContinuousVar: - - -
- - -

A class for creating a continuous variable

- -

The ContinuousVar class is used to create a continuous variable. -The lower and upper bounds of the variable can be specified by:

- -
    -
  • an integer value
    • -
  • a float value
    • -
  • a scalar expression that does not contains any decision variable
    • -
  • a Placeholder object whose dimensionality is equal to that of this variable.
    • -
  • a subscripted variable whose dimensionality is equal to that of this variable.
    • -
- -

The index operator ([]) of a variable with ndim >= 1 returns a Subscript object.

- -

Attributes

- -
    -
  • name (str): A name of the continuous variable.
    • -
  • shape (tuple): A tuple with the size of each dimension of the integer variable. Empty if the variable is not multi-dimensional.
    • -
  • lower_bound: The lower bound of the variable.
    • -
  • upper_bound: The upper bound of the variable.
    • -
  • description (str): A description of the continuous variable.
    • -
- -

Args

- -
    -
  • name (str): A name of the continuous variable.
    • -
  • shape (list | tuple): A sequence with the size of each dimension of the continuous variable. Defaults to an empty tuple (a scalar value). -
      -
    • Each item in shape must be a valid expression evaluating to a non-negative scalar.
      • -
    • -
  • lower_bound: The lower bound of the variable.
    • -
  • upper_bound: The upper bound of the variable.
    • -
  • latex (str, optional): A LaTeX-name of the continuous variable to be represented in Jupyter notebook. -
      -
    • It is set to name by default.
      • -
    • -
  • description (str, optional): A description of the continuous variable.
    • -
- -

Raises

- -

ModelingError: Raises if a bound is a Placeholder or Subscript object whose ndim is neither 0 nor the same value as ndim of the continuous variable.

- -

Examples

- -

Create a scalar continuous variable whose name is "z" and domain is [-1, 1].

- -
-
>>> import jijmodeling as jm
->>> z = jm.ContinuousVar("z", lower_bound=-1, upper_bound=1)
-
-
- -

Create a 2-dimensional continuous variable...

- -
    -
  • whose name is "x".
    • -
  • whose domain is [0, 2].
    • -
  • where each dimension has length 2 (making this a 2x2 matrix).
    • -
- -
-
>>> import jijmodeling as jm
->>> x = jm.ContinuousVar("x", shape=[2, 2], lower_bound=0, upper_bound=2)
-
-
- -

Create a 1-dimensional continuous variable with the index of 123.

- -
-
>>> import jijmodeling as jm
->>> x = jm.ContinuousVar("x", shape=[124], lower_bound=0, upper_bound=2)
->>> x[123]
-ContinuousVar(name='x', shape=[NumberLit(value=124)], lower_bound=NumberLit(value=0), upper_bound=NumberLit(value=2))[NumberLit(value=123)]
-
-
-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- description - - -
- - - - -
-
-
- ndim - - -
- - - - -
-
-
- upper_bound - - -
- - - - -
-
-
- shape - - -
- - - - -
-
-
- name - - -
- - - - -
-
-
- lower_bound - - -
- - - - -
-
-
-
- - class - SemiIntegerVar: - - -
- - -

A class for creating a semi-integer variable

- -

The SemiIntegerVar class is used to create a semi-integer variable. -The lower and upper bounds of the variable can be specified by:

- -
    -
  • an integer value
    • -
  • a float value
    • -
  • a scalar expression that does not contains any decision variable
    • -
  • a Placeholder object whose dimensionality is equal to that of this variable.
    • -
  • a subscripted variable whose dimensionality is equal to that of this variable.
    • -
- -

The index operator ([]) of a semi-integer variable with ndim >= 1 returns a Subscript object.

- -

Attributes

- -
    -
  • name (str): A name of the semi-integer variable.
    • -
  • shape (tuple): A tuple with the size of each dimension of the integer variable. Empty if the variable is not multi-dimensional.
    • -
  • lower_bound: The lower bound of the variable.
    • -
  • upper_bound: The upper bound of the variable.
    • -
  • description (str): A description of the semi-integer variable.
    • -
- -

Args

- -
    -
  • name (str): A name of the semi-integer variable.
    • -
  • shape (list | tuple): A sequence with the size of each dimension of the integer variable. Defaults to an empty tuple (a scalar value). -
      -
    • Each item in shape must be a valid expression evaluating to a non-negative scalar.
      • -
    • -
  • lower_bound: The lower bound of the variable.
    • -
  • upper_bound: The upper bound of the variable.
    • -
  • latex (str, optional): A LaTeX-name of the semi-integer variable to be represented in Jupyter notebook. -
      -
    • It is set to name by default.
      • -
    • -
  • description (str, optional): A description of the semi-integer variable.
    • -
- -

Raises

- -

ModelingError: Raises if a bound is a Placeholder or Subscript object whose ndim is neither 0 -nor the same value as ndim of the semi-integer variable.

- -

Examples

- -

Create a scalar semi-integer variable whose name is "z" and domain is [-1, 1].

- -
-
>>> import jijmodeling as jm
->>> z = jm.SemiIntegerVar("z", lower_bound=-1, upper_bound=1)
-
-
- -

Create a 2-dimensional semi-integer variable...

- -
    -
  • whose name is "x".
    • -
  • whose domain is [0, 2].
    • -
  • where each dimension has length 2 (making this a 2x2 matrix).
    • -
- -
-
>>> import jijmodeling as jm
->>> x = jm.SemiIntegerVar("x", shape=[2, 2], lower_bound=0, upper_bound=2)
-
-
- -

Create a 1-dimensional semi-integer variable with the index of 123.

- -
-
>>> import jijmodeling as jm
->>> x = jm.SemiIntegerVar("x", shape=[124], lower_bound=0, upper_bound=2)
->>> x[123]
-SemiIntegerVar(name='x', shape=[NumberLit(value=124)], lower_bound=NumberLit(value=0), upper_bound=NumberLit(value=2))[NumberLit(value=123)]
-
-
-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- lower_bound - - -
- - - - -
-
-
- shape - - -
- - - - -
-
-
- upper_bound - - -
- - - - -
-
-
- description - - -
- - - - -
-
-
- ndim - - -
- - - - -
-
-
- name - - -
- - - - -
-
-
-
- - class - SemiContinuousVar: - - -
- - -

A class for creating a semi-continuous variable

- -

The SemiContinuousVar class is used to create a semi-continuous variable. -Either the lower bound or the upper bound is set by the following object:

- -
    -
  • an integer value
    • -
  • a float value
    • -
  • a scalar expression that does not contains any decision variable
    • -
  • a Placeholder object whose dimensionality is equal to that of this variable.
    • -
  • a subscripted variable whose dimensionality is equal to that of this variable.
    • -
- -

The index operator ([]) of a semi-continuous variable with ndim >= 1 returns a Subscript object.

- -

Attributes

- -
    -
  • name (str): A name of the semi-continuous variable.
    • -
  • shape (tuple): A tuple with the size of each dimension of the semi-continuous variable. Empty if the variable is not multi-dimensional.
    • -
  • lower_bound: The lower bound of the variable.
    • -
  • upper_bound: The upper bound of the variable.
    • -
  • description (str): A description of the semi-continuous variable.
    • -
- -

Args

- -
    -
  • name (str): A name of the semi-continuous variable.
    • -
  • shape (list | tuple): A sequence with the size of each dimension of the binary variable. Defaults to an empty tuple (a scalar value). -
      -
    • Each item in shape must be a valid expression evaluating to a non-negative scalar.
      • -
    • -
  • lower_bound: The lower bound of the variable.
    • -
  • upper_bound: The upper bound of the variable.
    • -
  • latex (str, optional): A LaTeX-name of the semi-continuous variable to be represented in Jupyter notebook. -
      -
    • It is set to name by default.
      • -
    • -
  • description (str, optional): A description of the semi-continuous variable.
    • -
- -

Raises

- -

ModelingError: Raises if a bound is a Placeholder or Subscript object whose ndim -is neither 0 nor the same value as ndim of the semi-continuous variable.

- -

Examples

- -

Create a scalar semi-continuous variable whose name is "z" and domain is [-1, 1].

- -
-
>>> import jijmodeling as jm
->>> z = jm.SemiContinuousVar("z", lower_bound=-1, upper_bound=1)
-
-
- -

Create a 2-dimensional semi-continuous variable...

- -
    -
  • whose name is "x".
    • -
  • whose domain is [0, 2].
    • -
  • where each dimension has length 2 (making this a 2x2 matrix).
    • -
- -
-
>>> import jijmodeling as jm
->>> x = jm.SemiContinuousVar("x", shape=[2, 2], lower_bound=0, upper_bound=2)
-
-
- -

Create a 1-dimensional semi-continuous variable with the index of 123.

- -
-
>>> import jijmodeling as jm
->>> x = jm.SemiContinuousVar("x", shape=[124], lower_bound=0, upper_bound=2)
->>> x[123]
-SemiContinuousVar(name='x', shape=[NumberLit(value=124)], lower_bound=NumberLit(value=0), upper_bound=NumberLit(value=2))[NumberLit(value=123)]
-
-
-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- lower_bound - - -
- - - - -
-
-
- upper_bound - - -
- - - - -
-
-
- description - - -
- - - - -
-
-
- ndim - - -
- - - - -
-
-
- name - - -
- - - - -
-
-
- shape - - -
- - - - -
-
-
-
- - class - AbsOp: - - -
- - -

A class for representing the absolute value

- -

The AbsOp class is used to represent the absolute value. -The number of dimensions of the operand is zero.

- -

Attributes

- - - -

Note

- -

The AbsOp class does not have a constructor.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- operand - - -
- - - - -
-
-
-
- - class - CeilOp: - - -
- - -

A class for representing the ceil operator

- -

The CeilOp class is used to represent the ceil operator. -The number of dimensions of the operand is zero.

- -

Attributes

- - - -

Note

- -

The CeilOp class does not have a constructor.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- operand - - -
- - - - -
-
-
-
- - class - FloorOp: - - -
- - -

A class for representing the floor operator

- -

The FloorOp class is used to represent the floor operator. -The number of dimensions of the operand is zero.

- -

Attributes

- - - -

Note

- -

The FloorOp class does not have a constructor.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- operand - - -
- - - - -
-
-
-
- - class - Log2Op: - - -
- - -

A class for representing the base 2 logarithm

- -

The Log2Op class is used to represent the base 2 logarithm. -The number of dimensions of the operand is zero.

- -

Attributes

- -

operand: The operand.

- -

Note

- -

The Log2Op class does not have a constructor.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- operand - - -
- - - - -
-
-
-
- - class - Log10Op: - - -
- - -

A class for representing the base 10 logarithm

- -

The Log10Op class is used to represent the base 10 logarithm. -The number of dimensions of the operand is zero.

- -

Attributes

- - - -

Note

- -

The Log10Op class does not have a constructor.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- operand - - -
- - - - -
-
-
-
- - class - LnOp: - - -
- - -

A class for representing the natural logarithm

- -

The LnOp class is used to represent the natural logarithm. -The number of dimensions of the operand is zero.

- -

Attributes

- -

operand: The operand.

- -

Note

- -

The LnOp class does not have a constructor.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- operand - - -
- - - - -
-
-
-
- - class - AddOp: - - -
- - -

A class for representing addition

- -

The AddOp class is used to represent addition (+) of an arbitrary number of operands. -For example a + b + c + d would be one AddOp object. -The number of dimensions of each operand is zero.

- -

Attributes

- -

terms: A sequence of operands to be added.

- -

Note

- -

The AddOp class does not have a constructor. Its intended -instantiation method is by calling the addition operation on other -expressions.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- terms - - -
- - - - -
-
-
-
- - class - MulOp: - - -
- - -

A class for representing multiplication

- -

The MulOp class is used to represent multiplication (*) of an arbitrary number of operands. -For example a * b * c * d would be one AddOp object. -The number of dimensions of each operand is zero.

- -

Attributes

- -

terms: A sequence of operands to be multiplied.

- -

Note

- -

The MulOp class does not have a constructor. Its intended -instantiation method is by calling the multiplication operation on other -expressions.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- terms - - -
- - - - -
-
-
-
- - class - ModOp: - - -
- - -

A class for representing modulo

- -

The ModOp class is used to represent modulo (or remainder) (%). -The number of dimensions of each operand is zero.

- -

Attributes

- -
    -
  • left: The left-hand operand.
    • -
  • right: The right-hand operand.
    • -
- -

Note

- -

The ModOp class does not have a constructor.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- left - - -
- - - - -
-
-
- right - - -
- - - - -
-
-
-
- - class - MinOp: - - -
- - -

A class for representing the minimum value.

- -

The MinOp class is used to represent the minimum value of operands. -The number of dimensions of each operand is zero.

- -

Attributes

- -
    -
  • terms: A sequence of operands.
    • -
- -

Note

- -

The MinOp class does not have a constructor. Its intended -instantiation method is by calling the min function.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- terms - - -
- - - - -
-
-
-
- - class - MaxOp: - - -
- - -

A class for representing the maximum value.

- -

The MaxOp class is used to represent the minimum value of operands. -The number of dimensions of each operand is zero.

- -

Attributes

- -
    -
  • terms: A sequence of operands.
    • -
- -

Note

- -

The MaxOp class does not have a constructor. Its intended -instantiation method is by calling the max function.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- terms - - -
- - - - -
-
-
-
- - class - PowOp: - - -
- - -

A class for representing the power operator

- -

The ModOp class is used to represent the power operator(**). -The number of dimensions of each operand is zero.

- -

Attributes

- -
    -
  • base: The base operand.
    • -
  • exponent: The exponent operand.
    • -
- -

Note

- -

The PowOp class does not have a constructor.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- base - - -
- - - - -
-
-
- exponent - - -
- - - - -
-
-
-
- - class - SumOp: - - -
- - -

A class for representing summation

- -

The SumOp class is used to represent summation. -The number of dimensions of the opreand is zero.

- -

Attributes

- -
    -
  • index: The index of summation.
    • -
  • condition: The condition for the summation index.
    • -
  • operand: The opreand.
    • -
- -

Note

- -

The SumOp class does not have a constructor.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- index - - -
- - - - -
-
-
- operand - - -
- - - - -
-
-
- condition - - -
- - - - -
-
-
-
- - class - ProdOp: - - -
- - -

A class for representing product

- -

The ProdOp class is used to represent product. -The number of dimensions of the opreand is zero.

- -

Attributes

- - - -

Note

- -

The ProdOp class does not have a constructor.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- condition - - -
- - - - -
-
-
- index - - -
- - - - -
-
-
- operand - - -
- - - - -
-
-
-
- - class - AndOp: - - -
- - -

A class for representing logical AND

- -

The AndOp class is used to represent logical AND (&) of an arbitrary number of operands. -For example a & b & c & d would be one AndOp object. -The number of dimensions of each operand is zero.

- -

Attributes

- -
    -
  • terms: A sequence of operands to apply the AND operation.
    • -
- -

Note

- -

The AndOp class does not have a constructor.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- terms - - -
- - - - -
-
-
-
- - class - OrOp: - - -
- - -

A class for representing logical OR

- -

The OrOp class is used to represent logical OR (|) of an arbitrary number of operands. -For example a | b | c | d would be one OrOp object. -The number of dimensions of each operand is zero.

- -

Attributes

- -
    -
  • terms: A sequence of operands to apply the OR operation.
    • -
- -

Note

- -

The OrOp class does not have a constructor.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- terms - - -
- - - - -
-
-
-
- - class - XorOp: - - -
- - -

A class for representing logical XOR

- -

The XorOp class is used to represent logical XOR (^) of an arbitrary number of operands. -For example a ^ b ^ c ^ d would be one XorOp object. -The number of dimensions of each operand is zero.

- -

Attributes

- -
    -
  • terms-: A sequence of operands to apply the XOR operation.
    • -
- -

Note

- -

The XorOp class does not have a constructor.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- terms - - -
- - - - -
-
-
-
- - class - EqualOp: - - -
- - -

A class for representing the equal operator

- -

The EqualOp class is used to represent the equal operator (==). -The number of dimensions of each operand is zero.

- -

Attributes

- -
    -
  • left: The left-hand operand.
    • -
  • right: The right-hand operand.
    • -
- -

Note

- -

The EqualOp class does not have a constructor.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- right - - -
- - - - -
-
-
- left - - -
- - - - -
-
-
-
- - class - NotEqualOp: - - -
- - -

A class for representing the not equal operator

- -

The NotEqualOp class is used to represent the not equal operator (!=). -The number of dimensions of each operand is zero.

- -

Attributes

- -
    -
  • left: The left-hand operand.
    • -
  • right: The right-hand operand.
    • -
- -

Note

- -

The NotEqualOp class does not have a constructor.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- right - - -
- - - - -
-
-
- left - - -
- - - - -
-
-
-
- - class - LessThanOp: - - -
- - -

A class for representing the less than operator

- -

The LessThanOp class is used to represent the less than operator (<). -The number of dimensions of each operand is zero.

- -

Attributes

- -
    -
  • left: The left-hand operand.
    • -
  • right: The right-hand operand.
    • -
- -

Note

- -

The LessThanOp class does not have a constructor.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- left - - -
- - - - -
-
-
- right - - -
- - - - -
-
-
-
- - class - LessThanEqualOp: - - -
- - -

A class for representing the less than equal operator

- -

The LessThanEqualOp class is used to represent the less than equal operator (<=). -The number of dimensions of each operand is zero.

- -

Attributes

- -
    -
  • left: The left-hand operand.
    • -
  • right: The right-hand operand.
    • -
- -

Note

- -

The LessThanEqualOp class does not have a constructor.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- left - - -
- - - - -
-
-
- right - - -
- - - - -
-
-
-
- - class - GreaterThanOp: - - -
- - -

A class for representing the greater than operator

- -

The GreaterThanOp class is used to represent the greater than operator (>). -The number of dimensions of each operand is zero.

- -

Attributes

- -
    -
  • left: The left-hand operand.
    • -
  • right: The right-hand operand.
    • -
- -

Note

- -

The GreaterThanOp class does not have a constructor.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- left - - -
- - - - -
-
-
- right - - -
- - - - -
-
-
-
- - class - GreaterThanEqualOp: - - -
- - -

A class for representing the greater than equal operator

- -

The GreaterThanEqualOp class is used to represent the greater than equal operator (>=). -The number of dimensions of each operand is zero.

- -

Attributes

- -
    -
  • left: The left-hand operand.
    • -
  • right: The right-hand operand.
    • -
- -

Note

- -

The GreaterThanEqualOp class does not have a constructor.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- left - - -
- - - - -
-
-
- right - - -
- - - - -
-
-
-
- - class - Problem: - - -
- - -

A class for creating an optimization problem

- -

The Problem class is used to create an optimization problem.

- -

Attributes

- -
    -
  • name (str): A name of the optimization problem.
    • -
  • sense: Sense of the optimization problem.
    • -
  • objective: The objective function of the optimization problem.
    • -
  • constraints (dict): A dictionary that stores constraints. -
      -
    • A key is the name of a constraint and the value is the constraint object.
      • -
    • -
  • custom_penalty_terms (dict): A dictionary that stores custom penalty terms. -
      -
    • A key is the name of a custom penalty and the value is the custom penalty object.
      • -
    • -
- -

Args

- - -
- - -
-
- - def - used_placeholders(self, /): - - -
- - - - -
-
-
- - def - get_problem_schema(self, /): - - -
- - -

Returns the schema of the problem.

- -

Returns

- -
    -
  • schema: The dictionary containing the schema of the problem.
    • -
-
- - -
-
-
- - def - generate_random_dataset(seed, default, options): - - -
- - -

Generates a dictionary of random InstanceDataValue for a given problem. -To generate ommx.v1.Instance object directly, use InstanceDataValue.generate_random_instance instead.

- -

Args

- -
    -
  • options (optional): a dictionary of range parameters for each separate placeholders. The key must be the name of the placeholder and the value must be range parameter (as described in "Range Parameters and Range Syntax" below).
    • -
  • default (optional): default range parameters for placeholders which is not specified in options.
    • -
  • seed (optional): seed for random number generation.
    • -
- -

Returns

- -

dict: The dictionary from the name of placeholders to the generated InstanceDataValue objects. To be fed to Interpreter.eval_problem.

- -

Range Parameters and Range Syntax

- -

A range parameter is a dictionary consisting of the following fields:

- -
    -
  • size (optional): interval of natural numbers for the size of each array dimension (default: range(1, 6))
    • -
  • value (optional): interval of real numbers for the value of each array element (default: range(-1.0, 1.0) - a uniform distribution on a closed interval $[-1.0, 1.0]$).
    • -
- -

Example range parameter config:

- -
-
{"size": range(2, 10), "value": jm.range.value.closed(100.0, 200.0)}
-
-
- -

Intervals are expressed as a range object. -Currently, the following syntax is supported for range objects:

- -
    -
  1. Direct value of type int or float - it corresponds to a singleton interval $[a, a] = {a}$. In random generation context, this just means a constant fixed value.
    2. -
  2. Use the functions from jijmodeling.range, jijmodeling.range.size, or jijmodeling.range.value modules. -
      -
    • Use functions from jij.modeling.range.size to specify (non-negative) integer intervals, and jij.modeling.range.value for real intervals. jij.modeling.range dynamically determines the type of the range based on the input.
      • -
    • These three modules provides the following combinators (see the module documents for more details.): -
        -
      • closed(a, b): a closed interval $[a, b]$
        • -
      • open(a, b): an open interval $(a, b)$
        • -
      • closed_open(a, b): an upper half-open interval $[a, b)$
        • -
      • open_closed(a, b): a lower half-open interval $(a, b]$
        • -
      • greater_than(a): an open interval $(a, \infty)$
        • -
      • at_least(a): a closed interval $[a, \infty)$
        • -
      • less_than(a): an open interval $(-\infty, a)$
        • -
      • at_most(a): a closed interval $(-\infty, a]$
        • -
      • -
    3. -
  3. Use range builtin function: this is equivalent to jijmodeling.range.value.closed_open(a, b). -
      -
    • Any python range object with step = 1 can be used as a size range; otherwise it results in runtime error.
      • -
    4. -

  4. Use a tuple: raw tuple (a, b) is equivalent to jijmodeling.range.closed_open(a, b) if a and b are either int or float.

    - -
      -

    • You can also use bound object as a tuple component; in such case, both tuple components must be one of the following:

      - -
        -
      1. A string "Unbounded" means $-\infty$ (in the first component) or $\infty$ (the second).
        2. -
      2. A dictionary {"Included": a} means the endpoint is inclusive.
        3. -
      3. A dictionary {"Excluded": a} means the endpoint is exclusive.
        4. -
      • -
    • Examples: -
        -
      • (1.2, 4) is equivalent to closed_open(1.2, 4),
        • -
      • (-1, {"Included": 1}) is equivalent to closed(-1, 1),
        • -
      • (-5, {"Excluded": 4}) is equivalent to closed_open(-5, 4) and built in function range(-5, 4),
        • -
      • ({"Excluded": 1}, {"Excluded": 2.5}) is equivalent to open(1, 2.5),
        • -
      • ({"Included": -1}, "Unbounded") is equivalent to at_least(-1).
        • -
      • (5, "Unbounded") is INVALID; 5 must be bound object.
        • -
      • -
    5. -
  5. The range object: A dictionary of form {"start": lb, "end": ub}, where both lb and ub are the bound object described as above.
    6. -
- -

Examples

- -
-
>>> import jijmodeling as jm
->>> import builtins
->>> N = jm.Placeholder("N", dtype=jm.DataType.INTEGER)
->>> c = jm.Placeholder("c", dtype=jm.DataType.FLOAT, shape=(N,))
->>> x = jm.BinaryVar("x", shape=(N,))
->>> i = jm.Element("i", belong_to=N)
-
->>> problem = jm.Problem("problem")
->>> problem += jm.sum(i, c[i] * x[i])
-
->>> inputs = problem.generate_random_dataset(
-...     options={
-...         'N': {"value": builtins.range(10, 20)},
-...         'c': {"value": jm.range.value.closed(-1.0, 1.0)}
-...          # You can also specify "size" for the range of jagged array dimension size.
-...     },
-...     seed=123 # omittable
-... )
->>> assert set(inputs.keys()) == {"N", "c"}
->>> inputs
-{'N': 11.0, 'c': array([ 0.93914459, -0.06511935, -0.7460324 , -0.32443706,  0.99981451,
-       -0.24407535,  0.31329469,  0.52206453, -0.1291936 ,  0.30443087,
-        0.53125838])}
-
-
-
- - -
-
-
- - def - generate_random_instance(seed, default, options, hints): - - -
- - -

Generates random ommx.v1.Instance for a given problem. -See also InstanceDataValue.generate_random_dataset.

- -

Args

- -
    -
  • options (optional): a dictionary of range parameters for each separate placeholders. The key must be the name of the placeholder and the value must be range parameter (as described in "Range Parameters and Range Syntax" section in ~jijmodeling.Problem.generate_random_dataset()).
    • -
  • default (optional): default range parameters for placeholders which is not specified in options.
    • -
  • seed (optional): seed for random number generation.
    • -
  • hints (optional): the hints to be detected during compilation see Interpreter.eval_problem for more details.
    • -
- -

Returns

- -

instance: The OMMX v1 instance object.

- -

Examples

- -
-
>>> import jijmodeling as jm
->>> import builtins
->>> import ommx.v1
->>> N = jm.Placeholder("N", dtype=jm.DataType.INTEGER)
->>> c = jm.Placeholder("c", dtype=jm.DataType.FLOAT, shape=(N,))
->>> x = jm.BinaryVar("x", shape=(N,))
->>> i = jm.Element("i", belong_to=N)
-
->>> problem = jm.Problem("problem")
->>> problem += jm.sum(i, c[i] * x[i])
-
->>> instance = problem.generate_random_instance(
-...     options={
-...         'N': {"value": builtins.range(10, 20)},
-...         'c': {"value": jm.range.value.closed(-1.0, 1.0)}
-...     },
-...     seed=123
-... )
->>> assert type(instance) is ommx.v1.Instance
-
-
-
- - -
-
-
- name - - -
- - - - -
-
-
- sense - - -
- - - - -
-
-
- constraints - - -
- - - - -
-
-
- custom_penalty_terms - - -
- - - - -
-
-
- objective - - -
- - - - -
-
-
-
- - class - ProblemSense: - - -
- - -

An optimization sense

-
- - -
-
- MINIMIZE = -ProblemSense.MINIMIZE - - -
- - - - -
-
-
- MAXIMIZE = -ProblemSense.MAXIMIZE - - -
- - - - -
-
-
-
- - class - Constraint: - - -
- - -

A class for creating a constraint

- -

The Constraint class is used to create a constraint.

- -

Attributes

- -
    -
  • name (str): A name of the constraint.
    • -
  • sense: equal sign (=) or inequality sign (>= or <=) included in the expression.
    • -
  • expression: The (in)equality equation of the constraint.
    • -
  • forall (list): A list that stores forall indices.
    • -
- -

Args

- -
    -
  • name (str): A name of the constraint.
    • -
  • expression: The (in)equality equation of the constraint.
    • -
  • forall: A list that stores forall indices. Defaults to None.
    • -
- -

Raises

- -

ModelingError: Raises if expression does not contain any decision variable.

- -

Expression

- -

Create an equality constraint that the sum of $N$ binary variables is equal to one.

- -
-
>>> import jijmodeling as jm
->>> N = jm.Placeholder("N")
->>> i = jm.Element("i", belong_to=N)
->>> x = jm.BinaryVar("x", shape=(N,))
->>> repr(jm.Constraint("constraint", jm.sum(i, x[i]) == 1))
-'Constraint(name="constraint", expression=sum(i in [0..N), x[i]) == 1)'
-
-
- -

Create an inequality constraint with forall.

- -
-
>>> import jijmodeling as jm
->>> N = jm.Placeholder("N")
->>> i = jm.Element("i", belong_to=N)
->>> j = jm.Element("j", belong_to=N)
->>> x = jm.BinaryVar("x", shape=(N, N))
->>> repr(jm.Constraint("constraint", jm.sum(i, x[i,j]) == 1, forall=j))
-'Constraint(name="constraint", expression=sum(i in [0..N), x[i, j]) == 1, forall=[j])'
-
-
- -

Create an inequality constraint with conditional forall.

- -
-
>>> import jijmodeling as jm
->>> N = jm.Placeholder("N")
->>> i = jm.Element("i", belong_to=N)
->>> j = jm.Element("j", belong_to=N)
->>> x = jm.BinaryVar("x", shape=(N, N))
->>> repr(jm.Constraint("constraint", x[i,j] <= 2, forall=[i, (j, j != i)]))
-'Constraint(name="constraint", expression=x[i, j] <= 2, forall=[i, (j, j != i)])'
-
-
-
- - -
-
- - def - is_equality(self, /): - - -
- - -

Returns true if the constraint is an equality constraint.

- -

Returns

- -

bool: True if the constraint is an equality constraint. Otherwise, False.

- -

Examples

- -
-
>>> import jijmodeling as jm
->>> N = jm.Placeholder("N")
->>> i = jm.Element("i", belong_to=N)
->>> x = jm.BinaryVar("x", shape=(N,))
->>> constraint = jm.Constraint("constraint", jm.sum(i, x[i]) == 1)
->>> assert constraint.is_equality()
-
-
-
- - -
-
-
- - def - is_inequality(self, /): - - -
- - -

Returns true if the constraint is an inequality constraint.

- -

Returns

- -

bool: True if the constraint is an inequality constraint. Otherwise, False.

- -

Examples

- -
-
>>> import jijmodeling as jm
->>> N = jm.Placeholder("N")
->>> i = jm.Element("i", belong_to=N)
->>> x = jm.BinaryVar("x", shape=(N,))
->>> constraint = jm.Constraint("constraint", jm.sum(i, x[i]) <= 1)
->>> assert constraint.is_inequality()
-
-
-
- - -
-
-
- right - - -
- - - - -
-
-
- expression - - -
- - - - -
-
-
- left - - -
- - - - -
-
-
- forall - - -
- - - - -
-
-
- sense - - -
- - - - -
-
-
- name - - -
- - - - -
-
-
-
- - class - ConstraintSense: - - -
- - -

Equality of a constraint

-
- - -
-
- EQUAL = -ConstraintSense.EQUAL - - -
- - - - -
-
-
- LESS_THAN_EQUAL = -ConstraintSense.LESS_THAN_EQUAL - - -
- - - - -
-
-
- GREATER_THAN_EQUAL = -ConstraintSense.GREATER_THAN_EQUAL - - -
- - - - -
-
-
-
- - class - CustomPenaltyTerm: - - -
- - -

A class for creating a custom penalty term

- -

The CustomPenaltyTerm class is used to create a custom penalty term.

- -

Attributes

- -
    -
  • name (str): A name of the custom penalty term.
    • -
  • expression: The expression of the custom penalty term.
    • -
  • forall (list): A list that stores forall indices.
    • -
- -

Args

- -
    -
  • name (str): A name of the custom penalty term.
    • -
  • expression: The expression of the custom penalty term.
    • -
  • forall: A list that stores forall indices. Defaults to None.
    • -
- -

Raises

- -

ModelingError: Raises if expression does not contain any decision variable.

- -

Expression

- -

Create a custom penalty term.

- -
-
>>> import jijmodeling as jm
->>> N = jm.Placeholder("N")
->>> i = jm.Element("i", belong_to=N)
->>> x = jm.BinaryVar("x", shape=(N,))
->>> repr(jm.CustomPenaltyTerm("custom penalty term", (jm.sum(i, x[i]) - 1)**2))  # doctest: +ELLIPSIS
-'CustomPenaltyTerm(name="custom penalty term", expression=((sum(i in [0..N), x[i]) - 1) ** 2))'
-
-
- -

Create a custom penalty term with forall.

- -
-
>>> import jijmodeling as jm
->>> N = jm.Placeholder("N")
->>> i = jm.Element("i", belong_to=N)
->>> j = jm.Element("j", belong_to=N)
->>> x = jm.BinaryVar("x", shape=(N, N))
->>> repr(jm.CustomPenaltyTerm("custom penalty term", (jm.sum(i, x[i,j]) - 1)**2, forall=j))  # doctest: +ELLIPSIS
-'CustomPenaltyTerm(name="custom penalty term", expression=((sum(i in [0..N), x[i, j]) - 1) ** 2), forall=[j])'
-
-
- -

Create a custom penalty term with conditional forall.

- -
-
>>> import jijmodeling as jm
->>> N = jm.Placeholder("N")
->>> i = jm.Element("i", belong_to=N)
->>> j = jm.Element("j", belong_to=N)
->>> x = jm.BinaryVar("x", shape=(N, N))
->>> repr(jm.CustomPenaltyTerm("custom penalty term", (x[i,j] - 2)**2, forall=[i, (j, j != i)]))  # doctest: +ELLIPSIS
-'CustomPenaltyTerm(name="custom penalty term", expression=((x[i, j] - 2) ** 2), forall=[i, (j, j != i)])'
-
-
-
- - -
-
- name - - -
- - - - -
-
-
- expression - - -
- - - - -
-
-
- forall - - -
- - - - -
-
-
-
- - class - Evaluation: - - -
- - -

A class for evaluation.

- -

The Evaluation class is to represent the result of evaluating a model.

- -

Attributes: - energy (numpy.ndarray): The value of energy of each sample. - objective (numpy.ndarray): The value of an objective function of each sample. - constraint_violations (dict[str, numpy.ndarray]): The constraint violation of each sample. - constraint_forall (dict[str, numpy.ndarray]): The constraint forall of each sample. - constraint_values (numpy.ndarray): The constraint value of each sample. - penalty (dict[str, numpy.ndarray]): The penalty of each sample.

-
- - -
-
- - def - to_dict(self, /): - - -
- - -

Convert into a dict.

- -

Returns: - dict: A dict whose keys are name of the Evaluation's fields.

-
- - -
-
-
- - def - from_dict(dict): - - -
- - -

Create a Evaluation object from the given dict.

- -

If a key is not the name of the Evaluation fields, the value value is ignored.

- -

Args: - dict: A dict whose keys are name of the Evaluation fields.

-
- - -
-
-
- - def - to_json(self, /): - - -
- - -

Serialize the Evaluation object into a JSON string.

- -

Returns: - str: A JSON string.

- -

Note: - A numpy array is converted into a list.

-
- - -
-
-
- - def - from_json(json): - - -
- - -

Create a Evaluation object from the JSON string.

- -

Args: - json (str): A JSON string.

- -

Returns: - Evaluation: An Evaluation object.

-
- - -
-
-
- - def - to_pandas(self, /): - - -
- - -

Convert into a pandas DataFrame.

- -

Returns: - pandas.DataFrame: A pandas DataFrame.

-
- - -
-
-
- energy - - -
- - - - -
-
-
- penalty - - -
- - - - -
-
-
- constraint_violations - - -
- - - - -
-
-
- constraint_forall - - -
- - - - -
-
-
- objective - - -
- - - - -
-
-
- constraint_values - - -
- - - - -
-
-
- constraint_expr_values - - -
- - -

Return the values for the each constraint.

- -

The return value is a list of dictionaries. -Each dictionary has the keys of the constraint names and the dictionary values. -The dictionary values have the keys of the forall indices as tuple and the values for the forall indices.

- -

Returns: - A list of dictionaries.

-
- - -
-
-
-
- - class - Record: - - -
- - -

A class for representing a record.

- -

There are two types of solutions that can be given; dense solutions and sparse solutions. -A dense solution is a dict whose key is a variable name and the value is a list of numpy.ndarray. -A sparse solution is a dict whose key is a variable name and the value is a list of tuples with three elements, -where the first element is a list of indices, the second element is a list of non-zero values, and the third element is a shape of the array. -The length of the list of solutions must be the same as the length of the list of num_occurrences. -Each index of the list of solutions corresponds to the same index of the list of non-zero values.

- -

As an example, consider the following solutions:

- -
-
{
-    "x": [
-        np.array([[0.0, 1.0, 0.0], [2.0, 0.0, 3.0]]),
-        np.array([[1.0, 0.0, 0.0], [2.0, 3.0, 4.0]])
-    ],
-    "y": [
-        np.array([0.0, 0.0, 1.0]),
-        np.array([0.0, 1.0, 0.0])
-    ]
-}
-
-
- -

This is a dense solution. The corresponding sparse solution is as follows:

- -
-
{
-    "x": [
-        (([0, 1, 1], [1, 0, 2]), [1.0, 2.0, 3.0], (2, 3)),
-        (([0, 1, 1, 1], [0, 0, 1, 2]), [1.0, 2.0, 3.0, 4.0], (2, 3))
-    ],
-    "y": [
-        (([2],), [1.0], (3,)),
-        (([1],), [1.0], (3,))
-    ]
-}
-
-
- -

Attributes

- -
    -
  • solution (Union[Dict[str, List[numpy.ndarray]], Dict[str, List[Tuple[List[int], List[float], Tuple[int, ...]]]]]): A solution.
    • -
  • num_occurrences (List[int]): A list of the number of occurrences in which the solution is observed.
    • -
-
- - -
-
- - def - to_dict(self, /): - - -
- - -

Convert into a dict.

- -

Returns

- -

dict: A dict whose keys are "solution" and "num_occurrences".

-
- - -
-
-
- - def - from_dict(dict): - - -
- - -

Create a Record object from the given dict.

- -

If a key is not one of "solution" or "num_occurrences", the value is ignored.

- -

Args

- -
    -
  • dict: A dict of str to a list of dense solutions or sparse solutions.
    • -
- -

Returns

- -

A Record object.

-
- - -
-
-
- - def - to_json(self, /): - - -
- - -

Serialize the Record object into a JSON string.

- -

Returns

- -

str: A JSON string.

- -

Note

- -

A numpy array is serialized into a list.

-
- - -
-
-
- - def - from_json(json): - - -
- - -

Create a Record object from the JSON string.

- -

Args

- -

json (str): A JSON string.

- -

Returns

- -

Record: A Record object.

-
- - -
-
-
- - def - is_dense(self, /): - - -
- - -

Return true if the solution is dense.

- -

Returns

- -

bool: True if the solution is dense.

-
- - -
-
-
- - def - is_sparse(self, /): - - -
- - -

Return true if the solution is sparse.

- -

Returns

- -

bool: True if the solution is sparse.

-
- - -
-
-
- - def - to_dense(self, /): - - -
- - -

Return a Record object whose solution is dense.

- -

If the solution is already dense, the solution is not converted. -Otherwise, the solution is converted into a dense solution.

- -

Returns

- -

Record: A Record object whose solution is dense.

-
- - -
-
-
- - def - to_sparse(self, /): - - -
- - -

Return a Record object whose solution is sparse.

- -

If the solution is already sparse, the solution is not converted. -Otherwise, the solution is converted into a sparse solution.

- -

Returns

- -

Record: A Record object whose solution is sparse.

-
- - -
-
-
- - def - to_pandas(self, /): - - -
- - -

Convert into a pandas DataFrame.

- -

Returns

- -

pandas.DataFrame: A pandas DataFrame.

-
- - -
-
-
- num_occurrences - - -
- - - - -
-
-
- solution - - -
- - - - -
-
-
-
- - class - SolvingTime: - - -
- - -

A class for storing time to solve a problem.

- -

Attributes

- -
    -
  • preprocess (float, optional): Time to preprocess the problem. Defaults to None.
    • -
  • solve (float, optional): Time to solve the problem. Defaults to None.
    • -
  • postprocess (float, optional): Time to postprocess the problem. Defaults to None.
    • -
-
- - -
-
- - def - to_dict(self, /): - - -
- - -

Convert into a dict.

- -

Returns

- -

dict: A dict with keys "preprocess", "solve", and "postprocess".

-
- - -
-
-
- - def - from_dict(dict): - - -
- - -

Create a SolvingTime object from the given dict.

- -

If a key is not one of "preprocess", "solve", or "postprocess", the value is ignored.

- -

Args

- -

dict (dict): A dict.

- -

Returns

- -

A SolvingTime object.

-
- - -
-
-
- - def - to_json(self, /): - - -
- - -

Serialize the SolvingTime object into a JSON string.

- -

Returns

- -

str: A JSON string.

- -

Note

- -

A numpy array is serialized into a list.

-
- - -
-
-
- - def - from_json(json): - - -
- - -

Create a SolvingTime object from the JSON string.

- -

Args

- -

json (str): A JSON string.

- -

Returns

- -

SolvingTime: A SolvingTime object.

-
- - -
-
-
- preprocess - - -
- - - - -
-
-
- solve - - -
- - - - -
-
-
- postprocess - - -
- - - - -
-
-
-
- - class - SystemTime: - - -
- - -

A class for storing time of jijzept running.

- -

Attributes

- - -
- - -
-
- - def - to_dict(self, /): - - -
- - -

Convert into a dict.

- -

Returns

- -

dict: A dict whose keys are name of the SystemTime's fields.

-
- - -
-
-
- - def - from_dict(dict): - - -
- - -

Create a SystemTime object from the given dict.

- -

If a key is not the name of the SystemTime fields, the value value is ignored.

- -

Args

- -

dict (dict): A dict.

- -

Returns

- -

A SystemTime object.

-
- - -
-
-
- - def - to_json(self, /): - - -
- - -

Serialize the SystemTime object into a JSON string.

- -

Returns

- -

str: A JSON string.

- -

Note

- -

A numpy array is serialized into a list.

-
- - -
-
-
- - def - from_json(json): - - -
- - -

Create a SystemTime object from the JSON string.

- -

Args

- -

json (str): A JSON string.

- -

Returns

- -

SystemTime: A SystemTime object.

-
- - -
-
-
- request_queue - - -
- - - - -
-
-
- fetch_problem_and_instance_data - - -
- - - - -
-
-
- deserialize_solution - - -
- - - - -
-
-
- fetch_result - - -
- - - - -
-
-
- post_problem_and_instance_data - - -
- - - - -
-
-
-
- - class - MeasuringTime: - - -
- - -

A class for storing time to be measured.

- -

Attributes

- - -
- - -
-
- - def - to_dict(self, /): - - -
- - -

Convert into a dict.

- -

Returns

- -

dict: A dict whose keys are name of the MeasuringTime's fields.

-
- - -
-
-
- - def - from_dict(dict): - - -
- - -

Create a MeasuringTime object from the given dict.

- -

If a key is not the name of the MeasuringTime fields, the value value is ignored.

- -

Args

- -

dict (dict): A dict.

- -

Returns

- -

A MeasuringTime object.

-
- - -
-
-
- - def - to_json(self, /): - - -
- - -

Serialize the MeasuringTime object into a JSON string.

- -

Returns

- -

str: A JSON string.

- -

Note

- -

A numpy array is serialized into a list.

-
- - -
-
-
- - def - from_json(json): - - -
- - -

Create a MeasuringTime object from the JSON string.

- -

Args

- -

json (str): A JSON string.

- -

Returns

- -

MeasuringTime: A MeasuringTime object.

-
- - -
-
-
- total - - -
- - - - -
-
-
- solve - - -
- - - - -
-
-
- system - - -
- - - - -
-
-
-
- - class - SampleSet: - - -
- - -

A class for storing time of jijzept running.

- -

Attributes

- -
    -
  • post_problem_and_instance_data (float, optional): Time to upload problem and instance_data to blob. Defaults to None.
    • -
  • request_queue (float, optional): Time to send request to queue. Defaults to None.
    • -
  • fetch_problem_and_instance_data (float, optional): Time to fetch problem and instance_data from blob. Defaults to None.
    • -
  • fetch_result (float, optional): Time to fetch result. Defaults to None.
    • -
  • deserialize_solution (float, optional): Time to deserialize json object. Defaults to None.
    • -
-
- - -
-
- - def - to_dict(self, /): - - -
- - -

Convert into a dict.

- -

Returns

- -

dict: A dict whose keys are name of the SampleSet's fields.

-
- - -
-
-
- - def - from_dict(dict): - - -
- - -

Create a SampleSet object from the given dict.

- -

If a key is not the name of the SampleSet fields, the value value is ignored.

- -

Args

- -

dict (dict): A dict.

- -

Returns

- -

A SampleSet object.

-
- - -
-
-
- - def - to_json(self, /): - - -
- - -

Serialize the SampleSet object into a JSON string.

- -

Returns

- -

str: A JSON string.

- -

Note

- -

A numpy array is converted into a list.

-
- - -
-
-
- - def - from_json(json): - - -
- - -

Create a SampleSet object from the JSON string.

- -

Args

- -

json (str): A JSON string.

- -

Returns

- -

SampleSet: A SampleSet object.

-
- - -
-
-
- - def - to_pandas(self, /): - - -
- - -

Convert into a pandas DataFrame.

- -

Returns

- -

pandas.DataFrame: A pandas DataFrame.

-
- - -
-
-
- - def - to_dense(self, /): - - -
- - -

Return a SampleSet whose record is converted into a dense solution format. -If the record is already a dense solution format, return itself.

- -

Returns

- -

SampleSet: A SampleSet object.

-
- - -
-
-
- - def - feasible(self, /, rtol=1e-05, atol=1e-08): - - -
- - -

Return a SampleSet with only feasible solutions. -If there is no feasible solution, the record and evaluation are empty.

- -

Args

- -
    -
  • rtol (float): The relative tolerance parameter. Defaults to 1e-5.
    • -
  • atol (float): The absolute tolerance parameter. Defaults to 1e-8.
    • -
- -

Returns

- -

SampleSet: A SampleSet object with only feasible solutions or empty.

- -

Note

- -

The feasible solutions are determined by the following condition: -$$ |0 - v| \leq \mathrm{atol} + \mathrm{rtol} \cdot |v| $$

-
- - -
-
-
- - def - infeasible(self, /, rtol=1e-05, atol=1e-08): - - -
- - -

Return a SampleSet with only infeasible solutions. -If there is no infeasible solution, the record and evaluation are empty.

- -

Args

- -
    -
  • rtol (float): The relative tolerance parameter. Defaults to 1e-5.
    • -
  • atol (float): The absolute tolerance parameter. Defaults to 1e-8.
    • -
- -

Returns

- -

SampleSet: A SampleSet object with only infeasible solutions or empty.

- -

Note

- -

The feasible solutions are determined by the following condition: -$$ |0 - v| > \mathrm{atol} + \mathrm{rtol} \cdot |v| $$

-
- - -
-
-
- - def - lowest(self, /, rtol=1e-05, atol=1e-08): - - -
- - -

Return a SampleSet with feasible solutions which has the lowest objective. -If there is no feasible solution, the record and evaluation are empty.

- -

Args

- -
    -
  • rtol (float): The relative tolerance parameter. Defaults to 1e-5.
    • -
  • atol (float): The absolute tolerance parameter. Defaults to 1e-8.
    • -
- -

Returns

- -

SampleSet: A SampleSet object with feasible solutions or empty.

- -

Note

- -

The feasible solutions are determined by the following condition: -$$ |0 - v| \leq \mathrm{atol} + \mathrm{rtol} \cdot |v| $$

-
- - -
-
-
- - def - is_dense(self, /): - - -
- - -

Return true if the solution is dense.

- -

Returns

- -

bool: True if the solution is dense.

-
- - -
-
-
- - def - is_sparse(self, /): - - -
- - -

Return true if the solution is sparse.

- -

Returns

- -

bool: True if the solution is sparse.

-
- - -
-
-
- - def - get_backend_calculation_time(self, /): - - -
- - -

Return report of the calculation time of the JijZept backends.

- -

Returns

- -

dict: A dictionary of the calculation time of the JijZept backends.

-
- - -
-
-
- measuring_time - - -
- - - - -
-
-
- metadata - - -
- - - - -
-
-
- record - - -
- - - - -
-
-
- evaluation - - -
- - - - -
-
-
-
- - class - Interpreter: - - -
- - -

Interpreter of the JijModeling AST

- -

This class is responsible for

- -
    -
  • Creating OMMX instance from the AST. -
      -
    • This means this module also has responsible to register decision variable ID for each decision variables in AST.
      • -
    • -
  • Manage instance data to be substituted into the Placeholder.
    • -
- -

Examples

- -
-
>>> import jijmodeling as jm
->>> import numpy as np
-
-Create a new interpreter with scalar instance data
->>> interpreter = jm.Interpreter({"a": 1})
-
-Insert instance data after creating the interpreter
->>> interpreter.insert_instance_data("b", 2) # scalar
-
-Python list and numpy array are supported
->>> interpreter.insert_instance_data("c", [3, 4])
->>> interpreter.insert_instance_data("d", [[1, 2], [3, 4]])
->>> interpreter.insert_instance_data("e", np.array([3, 4]))
-
-JaggedArray, non-uniform multi-dimensional array is also supported
->>> interpreter.insert_instance_data("f", [[[1, 2]], [[3, 4, 5]], [[6]]])
->>> interpreter.insert_instance_data("g", jm.JaggedArray([[[1, 2]], [[3, 4, 5]], [[6]]]))
-
-You can get the instance data by using get_instance_data method
->>> interpreter.get_instance_data("a")
-1.0
-
-Array are normalized to numpy array
->>> interpreter.get_instance_data("c")
-array([3., 4.])
->>> interpreter.get_instance_data("e")
-array([3., 4.])
->>> interpreter.get_instance_data("f")  # doctest: +ELLIPSIS
-<jijmodeling.JaggedArray object at 0x...>
-
-Update instance data and re-evaluate expressions
->>> a = jm.Placeholder("a")
->>> x = jm.BinaryVar("x")
->>> interpreter.eval_expr(a * x)  # Original evaluation with a = 1
-Function(x0)
->>> interpreter.insert_instance_data("a", 3)  # Update value of 'a'
->>> interpreter.eval_expr(a * x)  # Re-evaluate with updated value
-Function(3*x0)
-
-
-
- - -
-
- - def - insert_instance_data(self, /, key, value): - - -
- - - - -
-
-
- - def - get_instance_data(self, /, key): - - -
- - - - -
-
-
- - def - instance_data(self, /): - - -
- - - - -
-
-
- - def - get_decision_variable_by_id(self, /, id): - - -
- - - - -
-
-
- - def - get_decision_variable_by_name(self, /, name, subscript=None): - - -
- - - - -
-
-
- - def - eval_scalar(self, /, expr): - - -
- - - - -
-
-
- - def - eval_expr(self, /, expr): - - -
- - - - -
-
-
- - def - eval_constraints(self, /, constraints): - - -
- - - - -
-
-
- - def - eval_problem(self, /, problem, hints=None): - - -
- - -

Evaluate the problem and returns ommx.v1.Instance.

- -

Args

- -
    -
  • problem: a jijmodeling.Problem object to compile.
    • -
  • hints (optional): a list of constraint hints to be detected during evaluation. When omitted or given None, the default constraint hints are used. Pass empty list to disable hint detection entirely.
    • -
- -

Returns

- -

instance: an OMMX v1 instance object.

-
- - -
-
-
- num_decision_variables - - -
- - - - -
-
-
-
- - class - JaggedArray: - - -
- - -

Jagged array, a multi-dimensional array where each element can be an array of different length.

- -

Examples

- -
-
>>> import jijmodeling as jm
->>> arr = jm.JaggedArray([[[1, 2], [3, 4, 5]], [[6]]])
-
-# Three dimensional
->>> assert arr.dim == 3
-
-# __getitem__ works
->>> assert arr[0, 0, 0] == 1.0
->>> assert arr[0, 0, 1] == 2.0
->>> assert arr[0, 1, 0] == 3.0
->>> assert arr[0, 1, 1] == 4.0
->>> assert arr[0, 1, 2] == 5.0
->>> assert arr[1, 0, 0] == 6.0
-
-# out of range
->>> arr[0, 0, 2]
-Traceback (most recent call last):
- ...
-IndexError: Invalid index
-
-# dimension mismatch
->>> arr[0, 0]
-Traceback (most recent call last):
- ...
-IndexError: Invalid index
-
->>> assert arr.size_at([]) == 2  # [[1, 2], [3, 4, 5]] and [[6]]
->>> assert arr.size_at([0]) == 2  # [1, 2] and [3, 4, 5]
->>> assert arr.size_at([1]) == 1  # [[6]]
->>> assert arr.size_at([0, 0]) == 2  # [1, 2]
->>> assert arr.size_at([0, 1]) == 3  # [3, 4, 5]
->>> assert arr.size_at([1, 0]) == 1  # [6]
-
-
-
- - -
-
- - def - get(self, /, index): - - -
- - - - -
-
-
- - def - size_at(self, /, index): - - -
- - - - -
-
-
- dim - - -
- - - - -
-
-
- - \ No newline at end of file diff --git a/apis/jijmodeling/_jijmodeling.html b/apis/jijmodeling/_jijmodeling.html deleted file mode 100644 index 11ae7d67..00000000 --- a/apis/jijmodeling/_jijmodeling.html +++ /dev/null @@ -1,7869 +0,0 @@ - - - - - - - jijmodeling._jijmodeling API documentation - - - - - - - - - - - - -
-
-

-jijmodeling._jijmodeling

- - - - - -
-
-
- __version__ = -'1.13.2' - - -
- - - - -
-
-
- - class - ModelingError(builtins.Exception): - - -
- - -

Error while creating a model.

-
- - -
-
-
- - class - InterpreterError(builtins.RuntimeError): - - -
- - -

Error while interpreting the model.

-
- - -
-
-
- - class - ProtobufSerializationError(builtins.Exception): - - -
- - -

Failed to encode the object to a buffer.

-
- - -
-
-
- - class - ProtobufDeserializationError(builtins.Exception): - - -
- - -

Failed to decode the buffer to an instance.

-
- - -
-
-
- - def - is_linear(expr): - - -
- - -

Return true if the given expression is linear.

- -

Args

- -

expr: A math expression to be checked.

- -

Returns

- -

bool: True if the given expression is linear.

-
- - -
-
-
- - def - is_quadratic(expr): - - -
- - -

Return true if the given expression is quadratic.

- -

Args

- -

expr: A math expression to be checked.

- -

Returns

- -

bool: True if the given expression is quadratic.

-
- - -
-
-
- - def - is_higher_order(expr): - - -
- - -

Return true if the degree of the given expression is higher than 2.

- -

Args

- -

expr: A math expression to be checked.

- -

Returns

- -

bool: True if the degree of the given expression is higher than 2.

-
- - -
-
-
- - def - is_dynamic_degree(expr): - - -
- - -

Return true if degree of the expression is not determined statically.

- -

Args

- -

expr: A math expression to be checked.

- -

Returns

- -

bool: True if degree of the expression is determined dynamically.

- -

Examples

- -

Check if the degree of the following expression is determined dynamically.

- -
-
>>> a = jm.Placeholder("a", ndim=1)
->>> i = jm.Element("i", belong_to=a)
->>> x = jm.BinaryVar("x", shape=(a.len_at(0),))
->>> # The number of multiplication is not determined until the value of a is given.
->>> expr = jm.prod(i, x[i])
->>> assert jm.is_dynamic_degree(expr)
-
-
-
- - -
-
-
- - def - extract_nodes(obj, class_or_tuple): - - -
- - -

Extract all nodes from the given object.

- -

Args

- -
    -
  • obj: An expression defined by JijModeling's module, or a Problem, Constraint, CustomPenaltyTerm, or a list of forall object.
    • -
  • class_or_tuple: A type or tuple of types of nodes to be extracted.
    • -
- -

Returns

- -

list: A list of nodes whose type is type.

- -

Examples

- -

Extract all placeholders from the given expression.

- -
-
>>> from itertools import zip_longest
->>> import jijmodeling as jm
->>> a = jm.Placeholder("a")
->>> b = jm.Placeholder("b")
->>> expr = jm.Placeholder("a") + jm.Placeholder("b")
->>> for actual, expect in zip_longest(jm.extract_nodes(expr, jm.Placeholder), [a, b]):
->>>     assert jm.is_same(actual, expect)
-
-
- -

Extract all BinaryVar objects and Placeholder objects from the given expression.

- -
-
>>> from itertools import zip_longest
->>> import jijmodeling as jm
->>> a = jm.Placeholder("a")
->>> b = jm.Placeholder("b")
->>> x = jm.BinaryVar("x")
->>> y = jm.ContinuousVar("y", lower_bound=0, upper_bound=1)
->>> expr = a * x + b * y + 123
->>> actual = jm.extract_nodes(expr, (jm.BinaryVar, jm.Placeholder))
->>> expect = [a, x, b, y]
->>> for actual, expect in zip_longest(actual, expect):
->>>     assert jm.is_same(actual, expect)
-
-
-
- - -
-
-
- - def - extract_variables(obj): - - -
- - -

Extract all variables from the given object without duplication.

- -

This function returns a list of variables present within the given object. -The variables are stored in the list according to their visit order in a depth-first traversal of the tree. -Even if the same variable appears more than twice in the expression tree, the variable is only stored in the list upon its first appearance. -Consequently, the list returned by this function contains only the unique variables present in the expression.

- -

Args

- -

obj: An expression defined by JijModeling's module, or a Problem, Constraint, CustomPenaltyTerm, or a list of forall object.

- -

Returns

- -

list: A list of variables, which are Placeholder, Element, ArrayLength, BinaryVar, IntegerVar, ContinuousVar, SemiIntegerVar, or SemiContinuousVar.

- -

Examples

- -

Extract all variables from a problem without duplication.

- -
-
>>> n = jm.Placeholder("n")
->>> i = jm.Element("i", belong_to=n)
->>> x = jm.BinaryVar("x", shape=[10])
->>> y = jm.IntegerVar("y", shape=[10], lower_bound=0, upper_bound=1)
->>> z = jm.ContinuousVar("z", shape=[10], lower_bound=0, upper_bound=1)
->>> problem = jm.Problem("problem")
->>> problem += jm.sum(i, x[i])
->>> problem += jm.Constraint("constraint", y[i] == 1, forall=i)
->>> problem += jm.CustomPenaltyTerm("penalty", z[i], forall=i)
->>> actual = jm.extract_variables(problem)
->>> expect = [i, n, x, y, z]
->>> assert len(actual) == len(expect)
->>> for actual_node, expect_node in zip(actual, expect):
->>>     assert jm.is_same(actual_node, expect_node)
-
-
-
- - -
-
-
- - def - is_same(src, dst): - - -
- - -

Return true if src and dst are the same object defined by Jijmodeling.

- -

Args

- -
    -
  • src: An object defined by Jijmodeling module, or an iterable of Jijmodeling objects.
    • -
  • dst: An object defined by Jijmodeling module, or an iterable of Jijmodeling objects.
    • -
- -

Returns

- -

bool: true if src and dst is the same object. Otherwise false.

- -

Examples

- -

Check if the two placeholders are the same.

- -
-
>>> import jijmodeling as jm
->>> a = jm.Placeholder("name")
->>> b = jm.Placeholder("name")
->>> assert jm.is_same(a, b)
->>> c = jm.Placeholder("name", ndim=2)
->>> assert not jm.is_same(a, c) # the value of `ndim` is different
-
-
- -

Raises

- -

TypeError: Raises if

- -
    -
  • src and dst are of different types which are not iterable
    • -
  • called on a type not defined by Jijmodeling (e.g. str)
    • -
- -

Note

- -

This function does not check the following attributes:

- -
    -
  • description
    • -
  • latex
    • -
- -

For example,

- -
-
>>> import jijmodeling as jm
->>> src = jm.Placeholder("placeholder", latex="src")
->>> dst = jm.Placeholder("placeholder", latex="dst")
->>> assert jm.is_same(src, dst)
-
-
- -

this code works without any exception.

-
- - -
-
-
- - def - abs(operand): - - -
- - -

Create the AbsOp object from the expression.

- -

Args

- -

operand: An operand of the abs operator.

- -

Returns

- -

obj: The AbsOp object whose operand is the input.

- -

Examples

- -

Create the AbsOp object whose operand is a placeholder.

- -
-
>>> import jijmodeling as jm
->>> a = jm.Placeholder("a")
->>> jm.abs(a)
-
-
- -

Raises

- -

ModelingError: Raises if the input contains a decision variable.

-
- - -
-
-
- - def - ceil(operand): - - -
- - -

Create the CeilOp object from the expression.

- -

Args

- -
    -
  • operand: An operand of the ceil operator.
    • -
- -

Returns

- -

obj: The CeilOp object whose operand is the input.

- -

Examples

- -

Create the CeilOp object whose operand is a placeholder.

- -
-
>>> import jijmodeling as jm
->>> a = jm.Placeholder("a")
->>> jm.ceil(a)
-
-
- -

Raises

- -

ModelingError: Raises if the input contains a decision variable.

-
- - -
-
-
- - def - floor(operand): - - -
- - -

Create the FloorOp object from the expression.

- -

Args

- -
    -
  • operand: An operand of the floor operator.
    • -
- -

Returns

- -

obj: The FloorOp object whose operand is the input.

- -

Examples

- -

Create the FloorOp object whose operand is a placeholder.

- -
-
>>> import jijmodeling as jm
->>> a = jm.Placeholder("a")
->>> jm.floor(a)
-
-
- -

Raises

- -

ModelingError: Raises if the input contains a decision variable.

-
- - -
-
-
- - def - log2(operand): - - -
- - -

Create the Log2Op object from the expression.

- -

Args

- -
    -
  • operand: An operand of the log2 operator.
    • -
- -

Returns

- -

obj: The Log2Op object whose operand is the input.

- -

Examples

- -

Create the Log2Op object whose operand is a placeholder.

- -
-
>>> import jijmodeling as jm
->>> a = jm.Placeholder("a")
->>> jm.log2(a)
-
-
- -

Raises

- -

ModelingError: Raises if the input contains a decision variable.

-
- - -
-
-
- - def - log10(operand): - - -
- - -

Create the Log10Op object from the expression.

- -

Args

- -
    -
  • operand: An operand of the log10 operator.
    • -
- -

Returns

- -

obj: The Log10Op object whose operand is the input.

- -

Examples

- -

Create the Log10Op object whose operand is a placeholder.

- -
-
>>> import jijmodeling as jm
->>> a = jm.Placeholder("a")
->>> jm.log10(a)
-
-
- -

Raises

- -

ModelingError: Raises if the input contains a decision variable.

-
- - -
-
-
- - def - ln(operand): - - -
- - -

Create the LnOp object from the expression.

- -

Args

- -

operand: An operand of the natural logarithm.

- -

Returns

- -

obj: The LnOp object whose operand is the input.

- -

Examples

- -

Create the LnOp object whose operand is a placeholder.

- -
-
>>> import jijmodeling as jm
->>> a = jm.Placeholder("a")
->>> jm.ln(a)
-
-
- -

Raises

- -

ModelingError: Raises if the input contains a decision variable.

-
- - -
-
-
- - def - min(*operands): - - -
- - -

Create the MinOp object from the expression.

- -

Args

- -
    -
  • operands: A sequence of operands.
    • -
- -

Returns

- -

obj: The MinOp object whose operands are the inputs.

- -

Examples

- -

Create the MinOp object whose operands are three placeholders.

- -
-
>>> import jijmodeling as jm
->>> a = jm.Placeholder("a")
->>> b = jm.Placeholder("b")
->>> c = jm.Placeholder("c")
->>> jm.min(a, b, c)
-
-
- -

Raises

- -

ModelingError: Raises if the input contains a decision variable.

-
- - -
-
-
- - def - max(*operands): - - -
- - -

Create the MaxOp object from the expression.

- -

Args

- -
    -
  • operands: A sequence of operands.
    • -
- -

Returns

- -

obj: The MaxOp object whose operands are the inputs.

- -

Examples

- -

Create the MaxOp object whose operands are three placeholders.

- -
-
>>> import jijmodeling as jm
->>> a = jm.Placeholder("a")
->>> b = jm.Placeholder("b")
->>> c = jm.Placeholder("c")
->>> jm.max(a, b, c)
-
-
- -

Raises

- -

ModelingError: Raises if the input contains a decision variable.

-
- - -
-
-
- - def - sum(index, operand): - - -
- - -

Create the SumOp object.

- -

Args

- -
    -
  • index: An index of summation.
    • -
  • operand: The operand of summation.
    • -
- -

Returns

- -

obj: The SumOp object.

- -

Examples

- -

Create summation of an indexed placeholder $a_{i}$ from $i=0$ to $N-1$.

- -
-
>>> import jijmodeling as jm
->>> a = jm.Placeholder("a", ndim=1)
->>> N = jm.Placeholder("N")
->>> i = jm.Element("i", belong_to=(0, N))
->>> jm.sum(i, a[i])
-
-
- -

Create summation of an indexed binary variable $x_{e}$ over all elements $e$ in the set $E$.

- -
-
>>> import jijmodeling as jm
->>> x = jm.BinaryVar("x", ndim=1)
->>> E = jm.Placeholder("E", ndim=1)
->>> e = jm.Element("e", belong_to=E)
->>> jm.sum(e, x[e])
-
-
- -

By passing a tuple containing two elements to the index of summation, a conditional index can be constructed. -The left element of the tuple should be an instance of the Element class, while the right element should be the conditional expression for that index.

- -
-
>>> import jijmodeling as jm
->>> a = jm.Placeholder("a", ndim=1)
->>> i = jm.Element("i", belong_to=(0, 10))
->>> jm.sum((i, i != 2), a[i])
-
-
- -

This code creates summation of an indexed placeholder $a_{i}$ from $i=0$ to $9$ subject to the condition $i\neq2$.

-
- - -
-
-
- - def - prod(index, operand): - - -
- - -

Create the ProdOp object.

- -

Args

- -
    -
  • index: An index of product.
    • -
  • operand: The operand of product.
    • -
- -

Returns

- -

obj: The ProdOp object.

- -

Examples

- -

Create product of an indexed placeholder $a_{i}$ from $i=0$ to $N-1$.

- -
-
>>> import jijmodeling as jm
->>> a = jm.Placeholder("a", ndim=1)
->>> N = jm.Placeholder("N")
->>> i = jm.Element("i", belong_to=(0, N))
->>> jm.prod(i, a[i])
-
-
- -

Create product of an indexed binary variable $x_{e}$ over all elements $e$ in the set $E$.

- -
-
>>> import jijmodeling as jm
->>> x = jm.BinaryVar("x", ndim=1)
->>> E = jm.Placeholder("E", ndim=1)
->>> e = jm.Element("e", belong_to=E)
->>> jm.prod(e, x[e])
-
-
- -

By passing a tuple containing two elements to the index of product, a conditional index can be constructed. -The left element of the tuple should be an instance of the Element class, -while the right element should be the conditional expression for that index.

- -
-
>>> import jijmodeling as jm
->>> a = jm.Placeholder("a", ndim=1)
->>> i = jm.Element("i", belong_to=(0, 10))
->>> jm.prod((i, i != 2), a[i])
-
-
- -

This code creates product of an indexed placeholder $a_{i}$ from $i=0$ to $9$ subject to the condition $i\neq2$.

-
- - -
-
-
- - def - to_protobuf(obj): - - -
- - - - -
-
-
- - def - from_protobuf(buf): - - -
- - - - -
-
-
- - def - replace(target, replacer): - - -
- - -

Replace expression nodes with the result of calling the replacer function.

- -

This function can be used either with individual expressions or Problem instances. In -both cases, this function returns a new object without modifying the original.

- -

When used with a single expression, walks down the expression tree, applying the replacer -function to each node on the expression. When something is not replaced (i.e. the replacer -returns the original node), we walk down the expression tree to apply the replacer to child -nodes.

- -

When used with a Problem object, this is equivalent to creating a new Problem defined by -calling this function on each expression within the original, that is, the objective function, -as well as each constraint and each custom penalty term, including "forall" fields.

- -

Args

- -
    -
  • target: An expression or Problem to apply replacement.
    • -
  • replacer: A callable object that takes an expression node and returns a new expression node.
    • -
- -

Examples

- -

Replace all placeholders in an expression with a binary variable.

- -
-
>>> import jijmodeling as jm
->>> ph = jm.Placeholder("placeholder")
->>> var = jm.BinaryVar("x")
->>> expr = var + ph + 1
->>> replaced = jm.replace(ph, lambda x: var if isinstance(x, jm.Placeholder) else x)
->>> assert jm.is_same(replaced, var + var + 1)
-
-
- -

Replace a subscripted variable with a binary variable if it is a placeholder.

- -
-
>>> import jijmodeling as jm
->>> ph = jm.Placeholder("ph", ndim=2)
->>> x = jm.BinaryVar("x", shape=(1000, 1000))
->>> elt = jm.Element("elt", belong_to=ph)
->>> expr = ph[123, 456] + elt[123]
->>> replacer = (
->>>     lambda node: x[node.subscripts]
->>>     if isinstance(node, jm.Subscript) and isinstance(node.variable, jm.Placeholder)
->>>     else node
->>> )
->>> replaced = jm.replace(expr, replacer)
->>> assert jm.is_same(replaced, x[123, 456] + elt[123])
-
-
- -

Replace all instances of a variable in a problem with a different variable

- -
-
>>> import jijmodeling as jm
->>> x = jm.BinaryVar("x")
->>> problem = jm.Problem("p")
->>> problem += x / 2
->>> problem += jm.Constraint("c1", x + 3 <= 10)
->>>
->>> y = jm.IntegerVar("y", lower_bound=0, upper_bound=10)
->>> replacer = lambda node: y if jm.is_same(node, x) else node
->>> new_problem = jm.replace(problem, replacer)
->>> # no x present in new problem:
->>> vars = jm.extract_variables(new_problem)
->>> assert not any(jm.is_same(x, v) for v in vars)
-
-
- -

Raises

- -

TypeError: Raises if target is neither an expression nor an instance of Problem, or if replacer is not callable.

- -

Note

- -

The replacer must satisfy the following conditions:

- -
    -
  • The replacer must be a callable object.
    • -
  • The number of arguments of the replacer must be one.
    • -
  • The replacer must take an expression node as the argument.
    • -
  • The replacer must return an expression node.
    • -
-
- - -
-
-
- - def - concatenate(sample_sets): - - -
- - -

Concatenate some SampleSet objects into a single SampleSet object.

- -

Args

- -

sample_sets (list[SampleSet]): A list of SampleSet objects.

- -

Returns

- -

SampleSet: A SampleSet object which is concatenated from the given SampleSet objects.

- -

Note

- -

This function will be deprecated in v1.1.0.

-
- - -
-
-
- - def - load_mps(path): - - -
- - -

Load a MPS file as a set of Problem and dictionary for instance data

-
- - -
-
-
- - def - load_qplib(path, load_extra_data=False): - - -
- - -

Load a file in QPLIB format to generate a Problem object and a -corresponding instance data dictionary.

- -

Note regarding the final sections of qplib files:

- -
    -
  • starting points of x, y (lagrange multipliers), z (dual variables)
    • -
  • variable names and constraint names
    • -
- -

These are not directly supported by jijmodeling and not linked to the -Problem object, and are thus not part of the instance data. With the -optional argument load_extra_data=True, these final sections of the qplib -file are parsed and returned alongisde the problem.

- -

The extra_data dictionary is provided effectively raw, just as it is in -the file, with entries being dictionaries keyed by variable/constraint -indices. These are provided as-is, and do not match the rest the indices -seen in instance_data.

- -

Args

- -
    -
  • load_extra_data(bool)
    • -
- -

Returns

- -
    -
  • (problem, instance_data): a Problem and a dictionary generated from -the parsed file.
    • -
- -

or

- -
    -
  • (problem, instance_data, extra_data): if load_extra_data; the -additional dictionary holds information on starting points, variable names -and constraint names, as defined in the file.
    • -
-
- - -
-
-
- - class - DataType: - - -
- - - - -
-
- FLOAT = -DataType.FLOAT - - -
- - - - -
-
-
- INTEGER = -DataType.INTEGER - - -
- - - - -
-
-
-
- - class - NumberLit: - - -
- - -

A class for creating a number literal

- -

The NumberLit class is used to create a number literal. -Its instance is automatically generated by the return value of -arithmetic or mathematical functions taking a number literal and -an object defined by jijmodeling as arguments.

- -

Attributes

- - - -

Args

- -
    -
  • value (int | float): A numeric value.
    • -
- -

Examples

- -

Create a number literal with a integer value 123.

- -
-
>>> import jijmodeling as jm
->>> v = jm.NumberLit(123)
->>> assert v.value == 123
->>> assert v.dtype == jm.DataType.INTEGER
-
-
- -

Create a number literal with a float value 1.23.

- -
-
>>> import jijmodeling as jm
->>> v = jm.NumberLit(1.23)
->>> assert v.value == 1.23
->>> assert v.dtype == jm.DataType.FLOAT
-
-
-
- - -
-
- value - - -
- - - - -
-
-
- dtype - - -
- - - - -
-
-
-
- - class - Placeholder: - - -
- - -

A class for creating a placeholder

- -

The Placeholder class is used to create a placeholder. -It is a symbol to be replaced by a numerical value when you solve an optimization problem.

- -

The index operator ([]) of a placeholder with ndim >= 1 returns a Subscript object.

- -

Attributes

- -
    -
  • name (str): A name of the placeholder.
    • -
  • ndim (int): The number of dimensions of the placeholder.
    • -
  • shape (list of Optional[Expression], optional): The (partial) shape of the placeholder if given.
    • -
  • dtype (DataType, optional): The data type (DataType.INT or DataType.FLOAT) of the placeholder.
    • -
  • jagged (boolean, defaut: False): True if the placeholder will be treated as a jagged array in random data generation. Ignored for scalars.
    • -
  • description (str, optional): A description of the placeholder.
    • -
- -

Args

- -
    -
  • name (str): A name of the placeholder.
    • -
  • ndim (Optional[int]): The number of dimensions of the placeholder. Defaults to 0. The ndim must be set to a non-negative value and must concide with the length of shape if both specified. If None is given, you must specify shape explicitly and the length of shape will be used.
    • -
  • shape (list[Optional[Expression]], optional): The (partial) shape of the placeholder. Used for random data generation.
    • -
  • dtype (DataType, optional): The data type (DataType.INT or DataType.FLOAT) of the placeholder. Used for random data generation.
    • -
  • jagged (boolean, defaut: False): True if the placeholder will be treated as a jagged array in random data generation. Ignored for scalars.
    • -
  • latex (str, optional): A LaTeX-name of the placeholder to be represented in Jupyter notebook. -It is set to name by default.
    • -
  • description (str, optional): A description of the placeholder.
    • -
- -

Raises

- -
    -
  • TypeError: Raises if set a float value to ndim.
    • -
  • OverflowError: Raises if set a negative value to ndim.
    • -
- -

Examples

- -

Create a scalar (or ndim is 0) placeholder whose name is "a".

- -
-
>>> import jijmodeling as jm
->>> a = jm.Placeholder("a")
-
-
- -

Create a 2-dimensional placeholder whose name is "m".

- -
-
>>> import jijmodeling as jm
->>> m = jm.Placeholder("m", ndim=2)
-
-
- -

Create a 1-dimensional placeholder with the index of 123.

- -
-
>>> import jijmodeling as jm
->>> a = jm.Placeholder("a", ndim=2)
->>> a[123]
-Placeholder(name='a', ndim=2)[NumberLit(value=123)]
-
-
-
- - -
-
- - def - len_at(self, /, axis, *, latex=None, description=None): - - -
- - - - -
-
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- ndim - - -
- - - - -
-
-
- shape - - -
- - - - -
-
-
- description - - -
- - - - -
-
-
- name - - -
- - - - -
-
-
- dtype - - -
- - - - -
-
-
- jagged - - -
- - - - -
-
-
-
- - class - Range: - - -
- - -

A class representing a half-open interval.

- -

The Range class is used to represent a half-open interval [start, end). -This class does not have a constructor because it should be created by the Element class.

- -

Attributes

- -
    -
  • start: The lower bound of the range (inclusive).
    • -
  • end: The upper bound of the range (exclusive).
    • -
- -

Note

- -

This class does not contain any decision variable.

-
- - -
-
- start - - -
- - - - -
-
-
- end - - -
- - - - -
-
-
-
- - class - Element: - - -
- - -

A class for creating an element

- -

The Element class is used to create an element. -It is used in the following cases:

- -
    -
  • an index of summation $\displaystyle \sum$ (SumOp)
    • -
  • an index of product $\displaystyle \prod$ (ProdOp)
    • -
  • a bound variable of the universal quantifier $\forall$ (Forall)
    • -
- -

Elements specify a set to which they belong. The set can be:

- -
    -
  1. A half-open range, where the lower bound is included and the upper bound is excluded.
    2. -
  2. A Placeholder, Element, or Subscript object with ndim >= 1.
    3. -
- -

Ranges are generally specified with tuples as (start, end). For -convenience, passing a single number or scalar object as the argument is -interpreted as the end of a range starting from zero.

- -

The index operator ([]) of an element with ndim >= 1 returns a Subscript object.

- -

Attributes

- -
    -
  • name (str): A name of the element.
    • -
  • ndim (int): The number of dimensions of the element. The value is one less than the value of belong_to.ndim.
    • -
  • description (str): A description of the element.
    • -
  • belong_to: A set the element belongs to.
    • -
- -

Args

- -
    -
  • name (str): A name of the element.
    • -
  • belong_to: A set the element belongs to.
    • -
  • latex (str, optional): A LaTeX-name of the element to be represented in Jupyter notebook. -
      -
    • It is set to name by default.
      • -
    • -
  • description (str, optional): A description of the element.
    • -
- -

Examples

- -

Note that belong_to is a positional argument, not a keyword -argument, and so does not need to be written out. This is done in some -of these examples for clarity.

- -

Create an element that belongs to a half-open range.

- -
-
>>> import jijmodeling as jm
->>> i = jm.Element("i", belong_to=(0,10))
-
-
- -

If you pass a scalar as the belong_to argument, the set that the element belongs to is a range starting at 0 going up to that value.

- -
-
>>> import jijmodeling as jm
->>> i = jm.Element("i", 10)
->>> assert jm.is_same(i, jm.Element("i", belong_to=(0,10)))
-
-
- -

The applies not just to numbers, but certain scalars, like Placeholder (with ndim == 0).

- -
-
>>> import jijmodeling as jm
->>> n = jm.Placeholder("N")
->>> i = jm.Element("i", n)
->>> assert jm.is_same(i, jm.Element("i", belong_to=(0,n)))
-
-
- -

Create an element that belongs to a 1-dimensional placeholder.

- -
-
>>> import jijmodeling as jm
->>> E = jm.Placeholder("E", ndim=1)
->>> e = jm.Element("e", E)
-
-
- -

Create a 1-dimensional element with the index of 123.

- -
-
>>> import jijmodeling as jm
->>> a = jm.Placeholder("a", ndim=2)
->>> e = jm.Element("e", a)
->>> e[123]
-Element(name='e', belong_to=Placeholder(name='a', ndim=2))[NumberLit(value=123)]
-
-
-
- - -
-
- - def - len_at(self, /, axis, *, latex=None, description=None): - - -
- - - - -
-
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- name - - -
- - - - -
-
-
- ndim - - -
- - - - -
-
-
- belong_to - - -
- - - - -
-
-
- shape - - -
- - - - -
-
-
- description - - -
- - - - -
-
-
-
- - class - Subscript: - - -
- - -

A class for representing a subscripted variable

- -

The Subscript class is used to represent a variable with subscriptions.

- -

Attributes

- -
    -
  • variable: A variable that has subscripts.
    • -
  • subscripts (list): A list of subscripts.
    • -
  • ndim (int): The number of dimensions of the subscripted variable.
    • -
- -

Note

- -

The Subscript class does not have a constructor.

-
- - -
-
- - def - len_at(self, /, axis, *, latex=None, description=None): - - -
- - - - -
-
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- shape - - -
- - - - -
-
-
- ndim - - -
- - - - -
-
-
- subscripts - - -
- - - - -
-
-
- variable - - -
- - - - -
-
-
-
- - class - ArrayLength: - - -
- - -

A class for referring to the length of an array at a given axis.

- -

The ArrayLength class is to refer to the number of elements of an axis in an array.

- -

This class is not intended to be constructed directly. Instead, we -recommend using the len_at method of Placeholder, Element or -Subscript.

- -

Attributes

- -
    -
  • array: A variable with ndim >= 1.
    • -
  • axis: An axis index. A $n$-dimensional variable has $n$ axes. Axis 0 is the array's outermost axis and $n-1$ is the innermost.
    • -
  • description (str, optional): A description of the ArrayLength instance.
    • -
- -

Raises

- -

ModelingError: Raises if axis >= array.ndim.

- -

Examples

- -

Create a length of axis 0 in a 2-dimensional placeholder.

- -
-
>>> import jijmodeling as jm
->>> a = jm.Placeholder("a", ndim=2)
->>> N = a.len_at(0, latex="N")
-
-
-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- axis - - -
- - - - -
-
-
- array - - -
- - - - -
-
-
- description - - -
- - - - -
-
-
-
- - class - DummyIndexedVar: - - -
- - -

A class for representing a subscripted variable with dummy indices

- -

The DummyIndexedVar class is an intermediate representation to support syntactic sugar of sum/prod with slices.

- -

Note

- -

The DummyIndexedVar class does not have a constructor.

-
- - -
-
- - def - sum(self, /): - - -
- - -

Take a sum of the decision variable over the elements for which the slice is given and return a SumOp object.

- -

Returns

- -

SumOp: A SumOp object taken a sum of the decision variable over the elements for which the slice is given.

- -

Note

- -

An automatically created dummy index

- -
    -
  • has a name of the form __dummy_{decision_var.name}_{axis} where axis is the axis of the slice.
    • -
  • belongs to a range whose start defaults to 0 and end defaults to the length of the axis.
    • -
  • has description of the form dummy index at {axis} for {decision_var.name}.
    • -
  • has latex string of the form \\ast_{axis}.
    • -
- -

Examples

- -

Create a SumOp object taken a sum of the 2-dim binary variable over the 0-th elements for which the slice is given.

- -
-
>>> import jijmodeling as jm
->>> n = jm.Placeholder("n")
->>> x = jm.BinaryVar("x", shape=(n, n))
->>> i = jm.Element("__dummy_x_0", belong_to=n)
->>> j = jm.Element("j", belong_to=n)
->>> assert jm.is_same(x[:, j].sum(), jm.sum(i, x[i, j]))
-
-
-
- - -
-
-
- - def - prod(self, /): - - -
- - -

Take a prod of the decision variable over the elements for which the slice is given and return a ProdOp object.

- -

Returns

- -

ProdOp: A ProdOp object taken a prod of the decision variable over the elements for which the slice is given.

- -

Note

- -

An automatically created dummy index

- -
    -
  • has a name of the form __dummy_{decision_var.name}_{axis} where axis is the axis of the slice.
    • -
  • belongs to a range whose start defaults to 0 and end defaults to the length of the axis.
    • -
  • has description of the form dummy index at {axis} for {decision_var.name}.
    • -
  • has latex string of the form \\ast_{axis}.
    • -
- -

Examples

- -

Create a ProdOp object taken a prod of the 2-dim binary variable over the 0-th elements for which the slice is given.

- -
-
>>> import jijmodeling as jm
->>> n = jm.Placeholder("n")
->>> x = jm.BinaryVar("x", shape=(n, n))
->>> i = jm.Element("__dummy_x_0", belong_to=n)
->>> j = jm.Element("j", belong_to=n)
->>> assert jm.is_same(x[:, j].prod(), jm.prod(i, x[i, j]))
-
-
-
- - -
-
-
-
- - class - BinaryVar: - - -
- - -

A class for creating a binary variable

- -

The BinaryVar class is used to create a binary variable.

- -

The index operator ([]) of a binary variable with ndim >= 1 returns a Subscript object.

- -

Attributes

- -
    -
  • name (str): A name of the binary variable.
    • -
  • shape (tuple): A tuple with the size of each dimension of the binary variable. Empty if the variable is not multi-dimensional.
    • -
  • description (str): A description of the binary variable.
    • -
- -

Args

- -
    -
  • name (str): A name of the binary variable.
    • -
  • shape (list | tuple): A sequence with the size of each dimension of the binary variable. Defaults to an empty tuple (a scalar value). -
      -
    • Each item in shape must be a valid expression evaluating to a non-negative scalar.
      • -
    • -
  • latex (str, optional): A LaTeX-name of the binary variable to be represented in Jupyter notebook. -
      -
    • It is set to name by default.
      • -
    • -
  • description (str, optional): A description of the binary variable.
    • -
- -

Examples

- -

Create a scalar binary variable whose name is "z".

- -
-
>>> import jijmodeling as jm
->>> z = jm.BinaryVar("z")
-
-
- -

Create a 2-dimensional binary variable whose name is "x" and has a 2x2 shape.

- -
-
>>> import jijmodeling as jm
->>> x = jm.BinaryVar("x", shape=[2, 2])
-
-
- -

Create a 1-dimensional binary variable with the index of 123.

- -
-
>>> import jijmodeling as jm
->>> x = jm.BinaryVar("x", shape=[124])
->>> x[123]
-BinaryVar(name='x', shape=[NumberLit(value=124)])[NumberLit(value=123)]
-
-
-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- ndim - - -
- - - - -
-
-
- name - - -
- - - - -
-
-
- description - - -
- - - - -
-
-
- shape - - -
- - - - -
-
-
-
- - class - IntegerVar: - - -
- - -

A class for creating an integer variable

- -

The IntegerVar class is used to create an integer variable. -The lower and upper bounds of the variable can be specified by:

- -
    -
  • an integer value
    • -
  • a float value
    • -
  • a scalar expression that does not contains any decision variable
    • -
  • a Placeholder object whose dimensionality is equal to that of this variable.
    • -
  • a subscripted variable whose dimensionality is equal to that of this variable.
    • -
- -

The index operator ([]) of a variable with ndim >= 1 returns a Subscript object.

- -

Attributes

- -
    -
  • name (str): A name of the integer variable.
    • -
  • shape (tuple): A tuple with the size of each dimension of the integer variable. Empty if the variable is not multi-dimensional.
    • -
  • lower_bound: The lower bound of the variable.
    • -
  • upper_bound: The upper bound of the variable.
    • -
  • description (str): A description of the integer variable.
    • -
- -

Args

- -
    -
  • name (`str): A name of the integer variable.
    • -
  • shape (list | tuple): A sequence with the size of each dimension of the integer variable. Defaults to an empty tuple (a scalar value). -
      -
    • Each item in shape must be a valid expression evaluating to a non-negative scalar.
      • -
    • -
  • lower_bound: The lower bound of the variable.
    • -
  • upper_bound: The upper bound of the variable.
    • -
  • latex (str, optional): A LaTeX-name of the integer variable to be represented in Jupyter notebook. -
      -
    • It is set to name by default.
      • -
    • -
  • description (str, optional): A description of the integer variable.
    • -
- -

Raises

- -

ModelingError: Raises if a bound is a Placeholder or Subscript object whose ndim -is neither 0 nor the same value as ndim of the integer variable.

- -

Examples

- -

Create a scalar integer variable whose name is "z" and domain is [-1, 1].

- -
-
>>> import jijmodeling as jm
->>> z = jm.IntegerVar("z", lower_bound=-1, upper_bound=1)
-
-
- -

Create a 2-dimensional integer variable...

- -
    -
  • whose name is "x".
    • -
  • whose domain is [0, 2].
    • -
  • where each dimension has length 2 (making this a 2x2 matrix).
    • -
- -
-
>>> import jijmodeling as jm
->>> x = jm.IntegerVar("x", shape=[2, 2], lower_bound=0, upper_bound=2)
-
-
- -

Create a 1-dimensional integer variable with the index of 123.

- -
-
>>> import jijmodeling as jm
->>> x = jm.IntegerVar("x", shape=[124], lower_bound=0, upper_bound=2)
->>> x[123]
-IntegerVar(name='x', shape=[NumberLit(value=124)], lower_bound=NumberLit(value=0), upper_bound=NumberLit(value=2))[NumberLit(value=123)]
-
-
-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- name - - -
- - - - -
-
-
- ndim - - -
- - - - -
-
-
- upper_bound - - -
- - - - -
-
-
- shape - - -
- - - - -
-
-
- lower_bound - - -
- - - - -
-
-
- description - - -
- - - - -
-
-
-
- - class - ContinuousVar: - - -
- - -

A class for creating a continuous variable

- -

The ContinuousVar class is used to create a continuous variable. -The lower and upper bounds of the variable can be specified by:

- -
    -
  • an integer value
    • -
  • a float value
    • -
  • a scalar expression that does not contains any decision variable
    • -
  • a Placeholder object whose dimensionality is equal to that of this variable.
    • -
  • a subscripted variable whose dimensionality is equal to that of this variable.
    • -
- -

The index operator ([]) of a variable with ndim >= 1 returns a Subscript object.

- -

Attributes

- -
    -
  • name (str): A name of the continuous variable.
    • -
  • shape (tuple): A tuple with the size of each dimension of the integer variable. Empty if the variable is not multi-dimensional.
    • -
  • lower_bound: The lower bound of the variable.
    • -
  • upper_bound: The upper bound of the variable.
    • -
  • description (str): A description of the continuous variable.
    • -
- -

Args

- -
    -
  • name (str): A name of the continuous variable.
    • -
  • shape (list | tuple): A sequence with the size of each dimension of the continuous variable. Defaults to an empty tuple (a scalar value). -
      -
    • Each item in shape must be a valid expression evaluating to a non-negative scalar.
      • -
    • -
  • lower_bound: The lower bound of the variable.
    • -
  • upper_bound: The upper bound of the variable.
    • -
  • latex (str, optional): A LaTeX-name of the continuous variable to be represented in Jupyter notebook. -
      -
    • It is set to name by default.
      • -
    • -
  • description (str, optional): A description of the continuous variable.
    • -
- -

Raises

- -

ModelingError: Raises if a bound is a Placeholder or Subscript object whose ndim is neither 0 nor the same value as ndim of the continuous variable.

- -

Examples

- -

Create a scalar continuous variable whose name is "z" and domain is [-1, 1].

- -
-
>>> import jijmodeling as jm
->>> z = jm.ContinuousVar("z", lower_bound=-1, upper_bound=1)
-
-
- -

Create a 2-dimensional continuous variable...

- -
    -
  • whose name is "x".
    • -
  • whose domain is [0, 2].
    • -
  • where each dimension has length 2 (making this a 2x2 matrix).
    • -
- -
-
>>> import jijmodeling as jm
->>> x = jm.ContinuousVar("x", shape=[2, 2], lower_bound=0, upper_bound=2)
-
-
- -

Create a 1-dimensional continuous variable with the index of 123.

- -
-
>>> import jijmodeling as jm
->>> x = jm.ContinuousVar("x", shape=[124], lower_bound=0, upper_bound=2)
->>> x[123]
-ContinuousVar(name='x', shape=[NumberLit(value=124)], lower_bound=NumberLit(value=0), upper_bound=NumberLit(value=2))[NumberLit(value=123)]
-
-
-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- description - - -
- - - - -
-
-
- ndim - - -
- - - - -
-
-
- upper_bound - - -
- - - - -
-
-
- shape - - -
- - - - -
-
-
- name - - -
- - - - -
-
-
- lower_bound - - -
- - - - -
-
-
-
- - class - SemiIntegerVar: - - -
- - -

A class for creating a semi-integer variable

- -

The SemiIntegerVar class is used to create a semi-integer variable. -The lower and upper bounds of the variable can be specified by:

- -
    -
  • an integer value
    • -
  • a float value
    • -
  • a scalar expression that does not contains any decision variable
    • -
  • a Placeholder object whose dimensionality is equal to that of this variable.
    • -
  • a subscripted variable whose dimensionality is equal to that of this variable.
    • -
- -

The index operator ([]) of a semi-integer variable with ndim >= 1 returns a Subscript object.

- -

Attributes

- -
    -
  • name (str): A name of the semi-integer variable.
    • -
  • shape (tuple): A tuple with the size of each dimension of the integer variable. Empty if the variable is not multi-dimensional.
    • -
  • lower_bound: The lower bound of the variable.
    • -
  • upper_bound: The upper bound of the variable.
    • -
  • description (str): A description of the semi-integer variable.
    • -
- -

Args

- -
    -
  • name (str): A name of the semi-integer variable.
    • -
  • shape (list | tuple): A sequence with the size of each dimension of the integer variable. Defaults to an empty tuple (a scalar value). -
      -
    • Each item in shape must be a valid expression evaluating to a non-negative scalar.
      • -
    • -
  • lower_bound: The lower bound of the variable.
    • -
  • upper_bound: The upper bound of the variable.
    • -
  • latex (str, optional): A LaTeX-name of the semi-integer variable to be represented in Jupyter notebook. -
      -
    • It is set to name by default.
      • -
    • -
  • description (str, optional): A description of the semi-integer variable.
    • -
- -

Raises

- -

ModelingError: Raises if a bound is a Placeholder or Subscript object whose ndim is neither 0 -nor the same value as ndim of the semi-integer variable.

- -

Examples

- -

Create a scalar semi-integer variable whose name is "z" and domain is [-1, 1].

- -
-
>>> import jijmodeling as jm
->>> z = jm.SemiIntegerVar("z", lower_bound=-1, upper_bound=1)
-
-
- -

Create a 2-dimensional semi-integer variable...

- -
    -
  • whose name is "x".
    • -
  • whose domain is [0, 2].
    • -
  • where each dimension has length 2 (making this a 2x2 matrix).
    • -
- -
-
>>> import jijmodeling as jm
->>> x = jm.SemiIntegerVar("x", shape=[2, 2], lower_bound=0, upper_bound=2)
-
-
- -

Create a 1-dimensional semi-integer variable with the index of 123.

- -
-
>>> import jijmodeling as jm
->>> x = jm.SemiIntegerVar("x", shape=[124], lower_bound=0, upper_bound=2)
->>> x[123]
-SemiIntegerVar(name='x', shape=[NumberLit(value=124)], lower_bound=NumberLit(value=0), upper_bound=NumberLit(value=2))[NumberLit(value=123)]
-
-
-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- lower_bound - - -
- - - - -
-
-
- shape - - -
- - - - -
-
-
- upper_bound - - -
- - - - -
-
-
- description - - -
- - - - -
-
-
- ndim - - -
- - - - -
-
-
- name - - -
- - - - -
-
-
-
- - class - SemiContinuousVar: - - -
- - -

A class for creating a semi-continuous variable

- -

The SemiContinuousVar class is used to create a semi-continuous variable. -Either the lower bound or the upper bound is set by the following object:

- -
    -
  • an integer value
    • -
  • a float value
    • -
  • a scalar expression that does not contains any decision variable
    • -
  • a Placeholder object whose dimensionality is equal to that of this variable.
    • -
  • a subscripted variable whose dimensionality is equal to that of this variable.
    • -
- -

The index operator ([]) of a semi-continuous variable with ndim >= 1 returns a Subscript object.

- -

Attributes

- -
    -
  • name (str): A name of the semi-continuous variable.
    • -
  • shape (tuple): A tuple with the size of each dimension of the semi-continuous variable. Empty if the variable is not multi-dimensional.
    • -
  • lower_bound: The lower bound of the variable.
    • -
  • upper_bound: The upper bound of the variable.
    • -
  • description (str): A description of the semi-continuous variable.
    • -
- -

Args

- -
    -
  • name (str): A name of the semi-continuous variable.
    • -
  • shape (list | tuple): A sequence with the size of each dimension of the binary variable. Defaults to an empty tuple (a scalar value). -
      -
    • Each item in shape must be a valid expression evaluating to a non-negative scalar.
      • -
    • -
  • lower_bound: The lower bound of the variable.
    • -
  • upper_bound: The upper bound of the variable.
    • -
  • latex (str, optional): A LaTeX-name of the semi-continuous variable to be represented in Jupyter notebook. -
      -
    • It is set to name by default.
      • -
    • -
  • description (str, optional): A description of the semi-continuous variable.
    • -
- -

Raises

- -

ModelingError: Raises if a bound is a Placeholder or Subscript object whose ndim -is neither 0 nor the same value as ndim of the semi-continuous variable.

- -

Examples

- -

Create a scalar semi-continuous variable whose name is "z" and domain is [-1, 1].

- -
-
>>> import jijmodeling as jm
->>> z = jm.SemiContinuousVar("z", lower_bound=-1, upper_bound=1)
-
-
- -

Create a 2-dimensional semi-continuous variable...

- -
    -
  • whose name is "x".
    • -
  • whose domain is [0, 2].
    • -
  • where each dimension has length 2 (making this a 2x2 matrix).
    • -
- -
-
>>> import jijmodeling as jm
->>> x = jm.SemiContinuousVar("x", shape=[2, 2], lower_bound=0, upper_bound=2)
-
-
- -

Create a 1-dimensional semi-continuous variable with the index of 123.

- -
-
>>> import jijmodeling as jm
->>> x = jm.SemiContinuousVar("x", shape=[124], lower_bound=0, upper_bound=2)
->>> x[123]
-SemiContinuousVar(name='x', shape=[NumberLit(value=124)], lower_bound=NumberLit(value=0), upper_bound=NumberLit(value=2))[NumberLit(value=123)]
-
-
-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- lower_bound - - -
- - - - -
-
-
- upper_bound - - -
- - - - -
-
-
- description - - -
- - - - -
-
-
- ndim - - -
- - - - -
-
-
- name - - -
- - - - -
-
-
- shape - - -
- - - - -
-
-
-
- - class - AbsOp: - - -
- - -

A class for representing the absolute value

- -

The AbsOp class is used to represent the absolute value. -The number of dimensions of the operand is zero.

- -

Attributes

- - - -

Note

- -

The AbsOp class does not have a constructor.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- operand - - -
- - - - -
-
-
-
- - class - CeilOp: - - -
- - -

A class for representing the ceil operator

- -

The CeilOp class is used to represent the ceil operator. -The number of dimensions of the operand is zero.

- -

Attributes

- - - -

Note

- -

The CeilOp class does not have a constructor.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- operand - - -
- - - - -
-
-
-
- - class - FloorOp: - - -
- - -

A class for representing the floor operator

- -

The FloorOp class is used to represent the floor operator. -The number of dimensions of the operand is zero.

- -

Attributes

- - - -

Note

- -

The FloorOp class does not have a constructor.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- operand - - -
- - - - -
-
-
-
- - class - Log2Op: - - -
- - -

A class for representing the base 2 logarithm

- -

The Log2Op class is used to represent the base 2 logarithm. -The number of dimensions of the operand is zero.

- -

Attributes

- -

operand: The operand.

- -

Note

- -

The Log2Op class does not have a constructor.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- operand - - -
- - - - -
-
-
-
- - class - Log10Op: - - -
- - -

A class for representing the base 10 logarithm

- -

The Log10Op class is used to represent the base 10 logarithm. -The number of dimensions of the operand is zero.

- -

Attributes

- - - -

Note

- -

The Log10Op class does not have a constructor.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- operand - - -
- - - - -
-
-
-
- - class - LnOp: - - -
- - -

A class for representing the natural logarithm

- -

The LnOp class is used to represent the natural logarithm. -The number of dimensions of the operand is zero.

- -

Attributes

- -

operand: The operand.

- -

Note

- -

The LnOp class does not have a constructor.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- operand - - -
- - - - -
-
-
-
- - class - AddOp: - - -
- - -

A class for representing addition

- -

The AddOp class is used to represent addition (+) of an arbitrary number of operands. -For example a + b + c + d would be one AddOp object. -The number of dimensions of each operand is zero.

- -

Attributes

- -

terms: A sequence of operands to be added.

- -

Note

- -

The AddOp class does not have a constructor. Its intended -instantiation method is by calling the addition operation on other -expressions.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- terms - - -
- - - - -
-
-
-
- - class - MulOp: - - -
- - -

A class for representing multiplication

- -

The MulOp class is used to represent multiplication (*) of an arbitrary number of operands. -For example a * b * c * d would be one AddOp object. -The number of dimensions of each operand is zero.

- -

Attributes

- -

terms: A sequence of operands to be multiplied.

- -

Note

- -

The MulOp class does not have a constructor. Its intended -instantiation method is by calling the multiplication operation on other -expressions.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- terms - - -
- - - - -
-
-
-
- - class - ModOp: - - -
- - -

A class for representing modulo

- -

The ModOp class is used to represent modulo (or remainder) (%). -The number of dimensions of each operand is zero.

- -

Attributes

- -
    -
  • left: The left-hand operand.
    • -
  • right: The right-hand operand.
    • -
- -

Note

- -

The ModOp class does not have a constructor.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- left - - -
- - - - -
-
-
- right - - -
- - - - -
-
-
-
- - class - MinOp: - - -
- - -

A class for representing the minimum value.

- -

The MinOp class is used to represent the minimum value of operands. -The number of dimensions of each operand is zero.

- -

Attributes

- -
    -
  • terms: A sequence of operands.
    • -
- -

Note

- -

The MinOp class does not have a constructor. Its intended -instantiation method is by calling the min function.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- terms - - -
- - - - -
-
-
-
- - class - MaxOp: - - -
- - -

A class for representing the maximum value.

- -

The MaxOp class is used to represent the minimum value of operands. -The number of dimensions of each operand is zero.

- -

Attributes

- -
    -
  • terms: A sequence of operands.
    • -
- -

Note

- -

The MaxOp class does not have a constructor. Its intended -instantiation method is by calling the max function.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- terms - - -
- - - - -
-
-
-
- - class - PowOp: - - -
- - -

A class for representing the power operator

- -

The ModOp class is used to represent the power operator(**). -The number of dimensions of each operand is zero.

- -

Attributes

- -
    -
  • base: The base operand.
    • -
  • exponent: The exponent operand.
    • -
- -

Note

- -

The PowOp class does not have a constructor.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- base - - -
- - - - -
-
-
- exponent - - -
- - - - -
-
-
-
- - class - SumOp: - - -
- - -

A class for representing summation

- -

The SumOp class is used to represent summation. -The number of dimensions of the opreand is zero.

- -

Attributes

- -
    -
  • index: The index of summation.
    • -
  • condition: The condition for the summation index.
    • -
  • operand: The opreand.
    • -
- -

Note

- -

The SumOp class does not have a constructor.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- index - - -
- - - - -
-
-
- operand - - -
- - - - -
-
-
- condition - - -
- - - - -
-
-
-
- - class - ProdOp: - - -
- - -

A class for representing product

- -

The ProdOp class is used to represent product. -The number of dimensions of the opreand is zero.

- -

Attributes

- - - -

Note

- -

The ProdOp class does not have a constructor.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- condition - - -
- - - - -
-
-
- index - - -
- - - - -
-
-
- operand - - -
- - - - -
-
-
-
- - class - AndOp: - - -
- - -

A class for representing logical AND

- -

The AndOp class is used to represent logical AND (&) of an arbitrary number of operands. -For example a & b & c & d would be one AndOp object. -The number of dimensions of each operand is zero.

- -

Attributes

- -
    -
  • terms: A sequence of operands to apply the AND operation.
    • -
- -

Note

- -

The AndOp class does not have a constructor.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- terms - - -
- - - - -
-
-
-
- - class - OrOp: - - -
- - -

A class for representing logical OR

- -

The OrOp class is used to represent logical OR (|) of an arbitrary number of operands. -For example a | b | c | d would be one OrOp object. -The number of dimensions of each operand is zero.

- -

Attributes

- -
    -
  • terms: A sequence of operands to apply the OR operation.
    • -
- -

Note

- -

The OrOp class does not have a constructor.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- terms - - -
- - - - -
-
-
-
- - class - XorOp: - - -
- - -

A class for representing logical XOR

- -

The XorOp class is used to represent logical XOR (^) of an arbitrary number of operands. -For example a ^ b ^ c ^ d would be one XorOp object. -The number of dimensions of each operand is zero.

- -

Attributes

- -
    -
  • terms-: A sequence of operands to apply the XOR operation.
    • -
- -

Note

- -

The XorOp class does not have a constructor.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- terms - - -
- - - - -
-
-
-
- - class - EqualOp: - - -
- - -

A class for representing the equal operator

- -

The EqualOp class is used to represent the equal operator (==). -The number of dimensions of each operand is zero.

- -

Attributes

- -
    -
  • left: The left-hand operand.
    • -
  • right: The right-hand operand.
    • -
- -

Note

- -

The EqualOp class does not have a constructor.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- right - - -
- - - - -
-
-
- left - - -
- - - - -
-
-
-
- - class - NotEqualOp: - - -
- - -

A class for representing the not equal operator

- -

The NotEqualOp class is used to represent the not equal operator (!=). -The number of dimensions of each operand is zero.

- -

Attributes

- -
    -
  • left: The left-hand operand.
    • -
  • right: The right-hand operand.
    • -
- -

Note

- -

The NotEqualOp class does not have a constructor.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- right - - -
- - - - -
-
-
- left - - -
- - - - -
-
-
-
- - class - LessThanOp: - - -
- - -

A class for representing the less than operator

- -

The LessThanOp class is used to represent the less than operator (<). -The number of dimensions of each operand is zero.

- -

Attributes

- -
    -
  • left: The left-hand operand.
    • -
  • right: The right-hand operand.
    • -
- -

Note

- -

The LessThanOp class does not have a constructor.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- left - - -
- - - - -
-
-
- right - - -
- - - - -
-
-
-
- - class - LessThanEqualOp: - - -
- - -

A class for representing the less than equal operator

- -

The LessThanEqualOp class is used to represent the less than equal operator (<=). -The number of dimensions of each operand is zero.

- -

Attributes

- -
    -
  • left: The left-hand operand.
    • -
  • right: The right-hand operand.
    • -
- -

Note

- -

The LessThanEqualOp class does not have a constructor.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- left - - -
- - - - -
-
-
- right - - -
- - - - -
-
-
-
- - class - GreaterThanOp: - - -
- - -

A class for representing the greater than operator

- -

The GreaterThanOp class is used to represent the greater than operator (>). -The number of dimensions of each operand is zero.

- -

Attributes

- -
    -
  • left: The left-hand operand.
    • -
  • right: The right-hand operand.
    • -
- -

Note

- -

The GreaterThanOp class does not have a constructor.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- left - - -
- - - - -
-
-
- right - - -
- - - - -
-
-
-
- - class - GreaterThanEqualOp: - - -
- - -

A class for representing the greater than equal operator

- -

The GreaterThanEqualOp class is used to represent the greater than equal operator (>=). -The number of dimensions of each operand is zero.

- -

Attributes

- -
    -
  • left: The left-hand operand.
    • -
  • right: The right-hand operand.
    • -
- -

Note

- -

The GreaterThanEqualOp class does not have a constructor.

-
- - -
-
- - def - set_latex(self, /, latex=None): - - -
- - -

Set the LaTeX representation of the object. -If the LaTeX representation is not set, the default representation is set.

- -

Args

- -

latex (str, optional): LaTeX representation of the object. Defaults to None.

-
- - -
-
-
- left - - -
- - - - -
-
-
- right - - -
- - - - -
-
-
-
- - class - Problem: - - -
- - -

A class for creating an optimization problem

- -

The Problem class is used to create an optimization problem.

- -

Attributes

- -
    -
  • name (str): A name of the optimization problem.
    • -
  • sense: Sense of the optimization problem.
    • -
  • objective: The objective function of the optimization problem.
    • -
  • constraints (dict): A dictionary that stores constraints. -
      -
    • A key is the name of a constraint and the value is the constraint object.
      • -
    • -
  • custom_penalty_terms (dict): A dictionary that stores custom penalty terms. -
      -
    • A key is the name of a custom penalty and the value is the custom penalty object.
      • -
    • -
- -

Args

- - -
- - -
-
- - def - used_placeholders(self, /): - - -
- - - - -
-
-
- - def - get_problem_schema(self, /): - - -
- - -

Returns the schema of the problem.

- -

Returns

- -
    -
  • schema: The dictionary containing the schema of the problem.
    • -
-
- - -
-
-
- - def - generate_random_dataset(seed, default, options): - - -
- - -

Generates a dictionary of random InstanceDataValue for a given problem. -To generate ommx.v1.Instance object directly, use InstanceDataValue.generate_random_instance instead.

- -

Args

- -
    -
  • options (optional): a dictionary of range parameters for each separate placeholders. The key must be the name of the placeholder and the value must be range parameter (as described in "Range Parameters and Range Syntax" below).
    • -
  • default (optional): default range parameters for placeholders which is not specified in options.
    • -
  • seed (optional): seed for random number generation.
    • -
- -

Returns

- -

dict: The dictionary from the name of placeholders to the generated InstanceDataValue objects. To be fed to Interpreter.eval_problem.

- -

Range Parameters and Range Syntax

- -

A range parameter is a dictionary consisting of the following fields:

- -
    -
  • size (optional): interval of natural numbers for the size of each array dimension (default: range(1, 6))
    • -
  • value (optional): interval of real numbers for the value of each array element (default: range(-1.0, 1.0) - a uniform distribution on a closed interval $[-1.0, 1.0]$).
    • -
- -

Example range parameter config:

- -
-
{"size": range(2, 10), "value": jm.range.value.closed(100.0, 200.0)}
-
-
- -

Intervals are expressed as a range object. -Currently, the following syntax is supported for range objects:

- -
    -
  1. Direct value of type int or float - it corresponds to a singleton interval $[a, a] = {a}$. In random generation context, this just means a constant fixed value.
    2. -
  2. Use the functions from jijmodeling.range, jijmodeling.range.size, or jijmodeling.range.value modules. -
      -
    • Use functions from jij.modeling.range.size to specify (non-negative) integer intervals, and jij.modeling.range.value for real intervals. jij.modeling.range dynamically determines the type of the range based on the input.
      • -
    • These three modules provides the following combinators (see the module documents for more details.): -
        -
      • closed(a, b): a closed interval $[a, b]$
        • -
      • open(a, b): an open interval $(a, b)$
        • -
      • closed_open(a, b): an upper half-open interval $[a, b)$
        • -
      • open_closed(a, b): a lower half-open interval $(a, b]$
        • -
      • greater_than(a): an open interval $(a, \infty)$
        • -
      • at_least(a): a closed interval $[a, \infty)$
        • -
      • less_than(a): an open interval $(-\infty, a)$
        • -
      • at_most(a): a closed interval $(-\infty, a]$
        • -
      • -
    3. -
  3. Use range builtin function: this is equivalent to jijmodeling.range.value.closed_open(a, b). -
      -
    • Any python range object with step = 1 can be used as a size range; otherwise it results in runtime error.
      • -
    4. -

  4. Use a tuple: raw tuple (a, b) is equivalent to jijmodeling.range.closed_open(a, b) if a and b are either int or float.

    - -
      -

    • You can also use bound object as a tuple component; in such case, both tuple components must be one of the following:

      - -
        -
      1. A string "Unbounded" means $-\infty$ (in the first component) or $\infty$ (the second).
        2. -
      2. A dictionary {"Included": a} means the endpoint is inclusive.
        3. -
      3. A dictionary {"Excluded": a} means the endpoint is exclusive.
        4. -
      • -
    • Examples: -
        -
      • (1.2, 4) is equivalent to closed_open(1.2, 4),
        • -
      • (-1, {"Included": 1}) is equivalent to closed(-1, 1),
        • -
      • (-5, {"Excluded": 4}) is equivalent to closed_open(-5, 4) and built in function range(-5, 4),
        • -
      • ({"Excluded": 1}, {"Excluded": 2.5}) is equivalent to open(1, 2.5),
        • -
      • ({"Included": -1}, "Unbounded") is equivalent to at_least(-1).
        • -
      • (5, "Unbounded") is INVALID; 5 must be bound object.
        • -
      • -
    5. -
  5. The range object: A dictionary of form {"start": lb, "end": ub}, where both lb and ub are the bound object described as above.
    6. -
- -

Examples

- -
-
>>> import jijmodeling as jm
->>> import builtins
->>> N = jm.Placeholder("N", dtype=jm.DataType.INTEGER)
->>> c = jm.Placeholder("c", dtype=jm.DataType.FLOAT, shape=(N,))
->>> x = jm.BinaryVar("x", shape=(N,))
->>> i = jm.Element("i", belong_to=N)
-
->>> problem = jm.Problem("problem")
->>> problem += jm.sum(i, c[i] * x[i])
-
->>> inputs = problem.generate_random_dataset(
-...     options={
-...         'N': {"value": builtins.range(10, 20)},
-...         'c': {"value": jm.range.value.closed(-1.0, 1.0)}
-...          # You can also specify "size" for the range of jagged array dimension size.
-...     },
-...     seed=123 # omittable
-... )
->>> assert set(inputs.keys()) == {"N", "c"}
->>> inputs
-{'N': 11.0, 'c': array([ 0.93914459, -0.06511935, -0.7460324 , -0.32443706,  0.99981451,
-       -0.24407535,  0.31329469,  0.52206453, -0.1291936 ,  0.30443087,
-        0.53125838])}
-
-
-
- - -
-
-
- - def - generate_random_instance(seed, default, options, hints): - - -
- - -

Generates random ommx.v1.Instance for a given problem. -See also InstanceDataValue.generate_random_dataset.

- -

Args

- -
    -
  • options (optional): a dictionary of range parameters for each separate placeholders. The key must be the name of the placeholder and the value must be range parameter (as described in "Range Parameters and Range Syntax" section in ~jijmodeling.Problem.generate_random_dataset()).
    • -
  • default (optional): default range parameters for placeholders which is not specified in options.
    • -
  • seed (optional): seed for random number generation.
    • -
  • hints (optional): the hints to be detected during compilation see Interpreter.eval_problem for more details.
    • -
- -

Returns

- -

instance: The OMMX v1 instance object.

- -

Examples

- -
-
>>> import jijmodeling as jm
->>> import builtins
->>> import ommx.v1
->>> N = jm.Placeholder("N", dtype=jm.DataType.INTEGER)
->>> c = jm.Placeholder("c", dtype=jm.DataType.FLOAT, shape=(N,))
->>> x = jm.BinaryVar("x", shape=(N,))
->>> i = jm.Element("i", belong_to=N)
-
->>> problem = jm.Problem("problem")
->>> problem += jm.sum(i, c[i] * x[i])
-
->>> instance = problem.generate_random_instance(
-...     options={
-...         'N': {"value": builtins.range(10, 20)},
-...         'c': {"value": jm.range.value.closed(-1.0, 1.0)}
-...     },
-...     seed=123
-... )
->>> assert type(instance) is ommx.v1.Instance
-
-
-
- - -
-
-
- name - - -
- - - - -
-
-
- sense - - -
- - - - -
-
-
- constraints - - -
- - - - -
-
-
- custom_penalty_terms - - -
- - - - -
-
-
- objective - - -
- - - - -
-
-
-
- - class - ProblemSense: - - -
- - -

An optimization sense

-
- - -
-
- MINIMIZE = -ProblemSense.MINIMIZE - - -
- - - - -
-
-
- MAXIMIZE = -ProblemSense.MAXIMIZE - - -
- - - - -
-
-
-
- - class - Constraint: - - -
- - -

A class for creating a constraint

- -

The Constraint class is used to create a constraint.

- -

Attributes

- -
    -
  • name (str): A name of the constraint.
    • -
  • sense: equal sign (=) or inequality sign (>= or <=) included in the expression.
    • -
  • expression: The (in)equality equation of the constraint.
    • -
  • forall (list): A list that stores forall indices.
    • -
- -

Args

- -
    -
  • name (str): A name of the constraint.
    • -
  • expression: The (in)equality equation of the constraint.
    • -
  • forall: A list that stores forall indices. Defaults to None.
    • -
- -

Raises

- -

ModelingError: Raises if expression does not contain any decision variable.

- -

Expression

- -

Create an equality constraint that the sum of $N$ binary variables is equal to one.

- -
-
>>> import jijmodeling as jm
->>> N = jm.Placeholder("N")
->>> i = jm.Element("i", belong_to=N)
->>> x = jm.BinaryVar("x", shape=(N,))
->>> repr(jm.Constraint("constraint", jm.sum(i, x[i]) == 1))
-'Constraint(name="constraint", expression=sum(i in [0..N), x[i]) == 1)'
-
-
- -

Create an inequality constraint with forall.

- -
-
>>> import jijmodeling as jm
->>> N = jm.Placeholder("N")
->>> i = jm.Element("i", belong_to=N)
->>> j = jm.Element("j", belong_to=N)
->>> x = jm.BinaryVar("x", shape=(N, N))
->>> repr(jm.Constraint("constraint", jm.sum(i, x[i,j]) == 1, forall=j))
-'Constraint(name="constraint", expression=sum(i in [0..N), x[i, j]) == 1, forall=[j])'
-
-
- -

Create an inequality constraint with conditional forall.

- -
-
>>> import jijmodeling as jm
->>> N = jm.Placeholder("N")
->>> i = jm.Element("i", belong_to=N)
->>> j = jm.Element("j", belong_to=N)
->>> x = jm.BinaryVar("x", shape=(N, N))
->>> repr(jm.Constraint("constraint", x[i,j] <= 2, forall=[i, (j, j != i)]))
-'Constraint(name="constraint", expression=x[i, j] <= 2, forall=[i, (j, j != i)])'
-
-
-
- - -
-
- - def - is_equality(self, /): - - -
- - -

Returns true if the constraint is an equality constraint.

- -

Returns

- -

bool: True if the constraint is an equality constraint. Otherwise, False.

- -

Examples

- -
-
>>> import jijmodeling as jm
->>> N = jm.Placeholder("N")
->>> i = jm.Element("i", belong_to=N)
->>> x = jm.BinaryVar("x", shape=(N,))
->>> constraint = jm.Constraint("constraint", jm.sum(i, x[i]) == 1)
->>> assert constraint.is_equality()
-
-
-
- - -
-
-
- - def - is_inequality(self, /): - - -
- - -

Returns true if the constraint is an inequality constraint.

- -

Returns

- -

bool: True if the constraint is an inequality constraint. Otherwise, False.

- -

Examples

- -
-
>>> import jijmodeling as jm
->>> N = jm.Placeholder("N")
->>> i = jm.Element("i", belong_to=N)
->>> x = jm.BinaryVar("x", shape=(N,))
->>> constraint = jm.Constraint("constraint", jm.sum(i, x[i]) <= 1)
->>> assert constraint.is_inequality()
-
-
-
- - -
-
-
- right - - -
- - - - -
-
-
- expression - - -
- - - - -
-
-
- left - - -
- - - - -
-
-
- forall - - -
- - - - -
-
-
- sense - - -
- - - - -
-
-
- name - - -
- - - - -
-
-
-
- - class - ConstraintSense: - - -
- - -

Equality of a constraint

-
- - -
-
- EQUAL = -ConstraintSense.EQUAL - - -
- - - - -
-
-
- LESS_THAN_EQUAL = -ConstraintSense.LESS_THAN_EQUAL - - -
- - - - -
-
-
- GREATER_THAN_EQUAL = -ConstraintSense.GREATER_THAN_EQUAL - - -
- - - - -
-
-
-
- - class - CustomPenaltyTerm: - - -
- - -

A class for creating a custom penalty term

- -

The CustomPenaltyTerm class is used to create a custom penalty term.

- -

Attributes

- -
    -
  • name (str): A name of the custom penalty term.
    • -
  • expression: The expression of the custom penalty term.
    • -
  • forall (list): A list that stores forall indices.
    • -
- -

Args

- -
    -
  • name (str): A name of the custom penalty term.
    • -
  • expression: The expression of the custom penalty term.
    • -
  • forall: A list that stores forall indices. Defaults to None.
    • -
- -

Raises

- -

ModelingError: Raises if expression does not contain any decision variable.

- -

Expression

- -

Create a custom penalty term.

- -
-
>>> import jijmodeling as jm
->>> N = jm.Placeholder("N")
->>> i = jm.Element("i", belong_to=N)
->>> x = jm.BinaryVar("x", shape=(N,))
->>> repr(jm.CustomPenaltyTerm("custom penalty term", (jm.sum(i, x[i]) - 1)**2))  # doctest: +ELLIPSIS
-'CustomPenaltyTerm(name="custom penalty term", expression=((sum(i in [0..N), x[i]) - 1) ** 2))'
-
-
- -

Create a custom penalty term with forall.

- -
-
>>> import jijmodeling as jm
->>> N = jm.Placeholder("N")
->>> i = jm.Element("i", belong_to=N)
->>> j = jm.Element("j", belong_to=N)
->>> x = jm.BinaryVar("x", shape=(N, N))
->>> repr(jm.CustomPenaltyTerm("custom penalty term", (jm.sum(i, x[i,j]) - 1)**2, forall=j))  # doctest: +ELLIPSIS
-'CustomPenaltyTerm(name="custom penalty term", expression=((sum(i in [0..N), x[i, j]) - 1) ** 2), forall=[j])'
-
-
- -

Create a custom penalty term with conditional forall.

- -
-
>>> import jijmodeling as jm
->>> N = jm.Placeholder("N")
->>> i = jm.Element("i", belong_to=N)
->>> j = jm.Element("j", belong_to=N)
->>> x = jm.BinaryVar("x", shape=(N, N))
->>> repr(jm.CustomPenaltyTerm("custom penalty term", (x[i,j] - 2)**2, forall=[i, (j, j != i)]))  # doctest: +ELLIPSIS
-'CustomPenaltyTerm(name="custom penalty term", expression=((x[i, j] - 2) ** 2), forall=[i, (j, j != i)])'
-
-
-
- - -
-
- name - - -
- - - - -
-
-
- expression - - -
- - - - -
-
-
- forall - - -
- - - - -
-
-
-
- - class - Evaluation: - - -
- - -

A class for evaluation.

- -

The Evaluation class is to represent the result of evaluating a model.

- -

Attributes: - energy (numpy.ndarray): The value of energy of each sample. - objective (numpy.ndarray): The value of an objective function of each sample. - constraint_violations (dict[str, numpy.ndarray]): The constraint violation of each sample. - constraint_forall (dict[str, numpy.ndarray]): The constraint forall of each sample. - constraint_values (numpy.ndarray): The constraint value of each sample. - penalty (dict[str, numpy.ndarray]): The penalty of each sample.

-
- - -
-
- - def - to_dict(self, /): - - -
- - -

Convert into a dict.

- -

Returns: - dict: A dict whose keys are name of the Evaluation's fields.

-
- - -
-
-
- - def - from_dict(dict): - - -
- - -

Create a Evaluation object from the given dict.

- -

If a key is not the name of the Evaluation fields, the value value is ignored.

- -

Args: - dict: A dict whose keys are name of the Evaluation fields.

-
- - -
-
-
- - def - to_json(self, /): - - -
- - -

Serialize the Evaluation object into a JSON string.

- -

Returns: - str: A JSON string.

- -

Note: - A numpy array is converted into a list.

-
- - -
-
-
- - def - from_json(json): - - -
- - -

Create a Evaluation object from the JSON string.

- -

Args: - json (str): A JSON string.

- -

Returns: - Evaluation: An Evaluation object.

-
- - -
-
-
- - def - to_pandas(self, /): - - -
- - -

Convert into a pandas DataFrame.

- -

Returns: - pandas.DataFrame: A pandas DataFrame.

-
- - -
-
-
- energy - - -
- - - - -
-
-
- penalty - - -
- - - - -
-
-
- constraint_violations - - -
- - - - -
-
-
- constraint_forall - - -
- - - - -
-
-
- objective - - -
- - - - -
-
-
- constraint_values - - -
- - - - -
-
-
- constraint_expr_values - - -
- - -

Return the values for the each constraint.

- -

The return value is a list of dictionaries. -Each dictionary has the keys of the constraint names and the dictionary values. -The dictionary values have the keys of the forall indices as tuple and the values for the forall indices.

- -

Returns: - A list of dictionaries.

-
- - -
-
-
-
- - class - Record: - - -
- - -

A class for representing a record.

- -

There are two types of solutions that can be given; dense solutions and sparse solutions. -A dense solution is a dict whose key is a variable name and the value is a list of numpy.ndarray. -A sparse solution is a dict whose key is a variable name and the value is a list of tuples with three elements, -where the first element is a list of indices, the second element is a list of non-zero values, and the third element is a shape of the array. -The length of the list of solutions must be the same as the length of the list of num_occurrences. -Each index of the list of solutions corresponds to the same index of the list of non-zero values.

- -

As an example, consider the following solutions:

- -
-
{
-    "x": [
-        np.array([[0.0, 1.0, 0.0], [2.0, 0.0, 3.0]]),
-        np.array([[1.0, 0.0, 0.0], [2.0, 3.0, 4.0]])
-    ],
-    "y": [
-        np.array([0.0, 0.0, 1.0]),
-        np.array([0.0, 1.0, 0.0])
-    ]
-}
-
-
- -

This is a dense solution. The corresponding sparse solution is as follows:

- -
-
{
-    "x": [
-        (([0, 1, 1], [1, 0, 2]), [1.0, 2.0, 3.0], (2, 3)),
-        (([0, 1, 1, 1], [0, 0, 1, 2]), [1.0, 2.0, 3.0, 4.0], (2, 3))
-    ],
-    "y": [
-        (([2],), [1.0], (3,)),
-        (([1],), [1.0], (3,))
-    ]
-}
-
-
- -

Attributes

- -
    -
  • solution (Union[Dict[str, List[numpy.ndarray]], Dict[str, List[Tuple[List[int], List[float], Tuple[int, ...]]]]]): A solution.
    • -
  • num_occurrences (List[int]): A list of the number of occurrences in which the solution is observed.
    • -
-
- - -
-
- - def - to_dict(self, /): - - -
- - -

Convert into a dict.

- -

Returns

- -

dict: A dict whose keys are "solution" and "num_occurrences".

-
- - -
-
-
- - def - from_dict(dict): - - -
- - -

Create a Record object from the given dict.

- -

If a key is not one of "solution" or "num_occurrences", the value is ignored.

- -

Args

- -
    -
  • dict: A dict of str to a list of dense solutions or sparse solutions.
    • -
- -

Returns

- -

A Record object.

-
- - -
-
-
- - def - to_json(self, /): - - -
- - -

Serialize the Record object into a JSON string.

- -

Returns

- -

str: A JSON string.

- -

Note

- -

A numpy array is serialized into a list.

-
- - -
-
-
- - def - from_json(json): - - -
- - -

Create a Record object from the JSON string.

- -

Args

- -

json (str): A JSON string.

- -

Returns

- -

Record: A Record object.

-
- - -
-
-
- - def - is_dense(self, /): - - -
- - -

Return true if the solution is dense.

- -

Returns

- -

bool: True if the solution is dense.

-
- - -
-
-
- - def - is_sparse(self, /): - - -
- - -

Return true if the solution is sparse.

- -

Returns

- -

bool: True if the solution is sparse.

-
- - -
-
-
- - def - to_dense(self, /): - - -
- - -

Return a Record object whose solution is dense.

- -

If the solution is already dense, the solution is not converted. -Otherwise, the solution is converted into a dense solution.

- -

Returns

- -

Record: A Record object whose solution is dense.

-
- - -
-
-
- - def - to_sparse(self, /): - - -
- - -

Return a Record object whose solution is sparse.

- -

If the solution is already sparse, the solution is not converted. -Otherwise, the solution is converted into a sparse solution.

- -

Returns

- -

Record: A Record object whose solution is sparse.

-
- - -
-
-
- - def - to_pandas(self, /): - - -
- - -

Convert into a pandas DataFrame.

- -

Returns

- -

pandas.DataFrame: A pandas DataFrame.

-
- - -
-
-
- num_occurrences - - -
- - - - -
-
-
- solution - - -
- - - - -
-
-
-
- - class - SolvingTime: - - -
- - -

A class for storing time to solve a problem.

- -

Attributes

- -
    -
  • preprocess (float, optional): Time to preprocess the problem. Defaults to None.
    • -
  • solve (float, optional): Time to solve the problem. Defaults to None.
    • -
  • postprocess (float, optional): Time to postprocess the problem. Defaults to None.
    • -
-
- - -
-
- - def - to_dict(self, /): - - -
- - -

Convert into a dict.

- -

Returns

- -

dict: A dict with keys "preprocess", "solve", and "postprocess".

-
- - -
-
-
- - def - from_dict(dict): - - -
- - -

Create a SolvingTime object from the given dict.

- -

If a key is not one of "preprocess", "solve", or "postprocess", the value is ignored.

- -

Args

- -

dict (dict): A dict.

- -

Returns

- -

A SolvingTime object.

-
- - -
-
-
- - def - to_json(self, /): - - -
- - -

Serialize the SolvingTime object into a JSON string.

- -

Returns

- -

str: A JSON string.

- -

Note

- -

A numpy array is serialized into a list.

-
- - -
-
-
- - def - from_json(json): - - -
- - -

Create a SolvingTime object from the JSON string.

- -

Args

- -

json (str): A JSON string.

- -

Returns

- -

SolvingTime: A SolvingTime object.

-
- - -
-
-
- preprocess - - -
- - - - -
-
-
- solve - - -
- - - - -
-
-
- postprocess - - -
- - - - -
-
-
-
- - class - SystemTime: - - -
- - -

A class for storing time of jijzept running.

- -

Attributes

- - -
- - -
-
- - def - to_dict(self, /): - - -
- - -

Convert into a dict.

- -

Returns

- -

dict: A dict whose keys are name of the SystemTime's fields.

-
- - -
-
-
- - def - from_dict(dict): - - -
- - -

Create a SystemTime object from the given dict.

- -

If a key is not the name of the SystemTime fields, the value value is ignored.

- -

Args

- -

dict (dict): A dict.

- -

Returns

- -

A SystemTime object.

-
- - -
-
-
- - def - to_json(self, /): - - -
- - -

Serialize the SystemTime object into a JSON string.

- -

Returns

- -

str: A JSON string.

- -

Note

- -

A numpy array is serialized into a list.

-
- - -
-
-
- - def - from_json(json): - - -
- - -

Create a SystemTime object from the JSON string.

- -

Args

- -

json (str): A JSON string.

- -

Returns

- -

SystemTime: A SystemTime object.

-
- - -
-
-
- request_queue - - -
- - - - -
-
-
- fetch_problem_and_instance_data - - -
- - - - -
-
-
- deserialize_solution - - -
- - - - -
-
-
- fetch_result - - -
- - - - -
-
-
- post_problem_and_instance_data - - -
- - - - -
-
-
-
- - class - MeasuringTime: - - -
- - -

A class for storing time to be measured.

- -

Attributes

- - -
- - -
-
- - def - to_dict(self, /): - - -
- - -

Convert into a dict.

- -

Returns

- -

dict: A dict whose keys are name of the MeasuringTime's fields.

-
- - -
-
-
- - def - from_dict(dict): - - -
- - -

Create a MeasuringTime object from the given dict.

- -

If a key is not the name of the MeasuringTime fields, the value value is ignored.

- -

Args

- -

dict (dict): A dict.

- -

Returns

- -

A MeasuringTime object.

-
- - -
-
-
- - def - to_json(self, /): - - -
- - -

Serialize the MeasuringTime object into a JSON string.

- -

Returns

- -

str: A JSON string.

- -

Note

- -

A numpy array is serialized into a list.

-
- - -
-
-
- - def - from_json(json): - - -
- - -

Create a MeasuringTime object from the JSON string.

- -

Args

- -

json (str): A JSON string.

- -

Returns

- -

MeasuringTime: A MeasuringTime object.

-
- - -
-
-
- total - - -
- - - - -
-
-
- solve - - -
- - - - -
-
-
- system - - -
- - - - -
-
-
-
- - class - SampleSet: - - -
- - -

A class for storing time of jijzept running.

- -

Attributes

- -
    -
  • post_problem_and_instance_data (float, optional): Time to upload problem and instance_data to blob. Defaults to None.
    • -
  • request_queue (float, optional): Time to send request to queue. Defaults to None.
    • -
  • fetch_problem_and_instance_data (float, optional): Time to fetch problem and instance_data from blob. Defaults to None.
    • -
  • fetch_result (float, optional): Time to fetch result. Defaults to None.
    • -
  • deserialize_solution (float, optional): Time to deserialize json object. Defaults to None.
    • -
-
- - -
-
- - def - to_dict(self, /): - - -
- - -

Convert into a dict.

- -

Returns

- -

dict: A dict whose keys are name of the SampleSet's fields.

-
- - -
-
-
- - def - from_dict(dict): - - -
- - -

Create a SampleSet object from the given dict.

- -

If a key is not the name of the SampleSet fields, the value value is ignored.

- -

Args

- -

dict (dict): A dict.

- -

Returns

- -

A SampleSet object.

-
- - -
-
-
- - def - to_json(self, /): - - -
- - -

Serialize the SampleSet object into a JSON string.

- -

Returns

- -

str: A JSON string.

- -

Note

- -

A numpy array is converted into a list.

-
- - -
-
-
- - def - from_json(json): - - -
- - -

Create a SampleSet object from the JSON string.

- -

Args

- -

json (str): A JSON string.

- -

Returns

- -

SampleSet: A SampleSet object.

-
- - -
-
-
- - def - to_pandas(self, /): - - -
- - -

Convert into a pandas DataFrame.

- -

Returns

- -

pandas.DataFrame: A pandas DataFrame.

-
- - -
-
-
- - def - to_dense(self, /): - - -
- - -

Return a SampleSet whose record is converted into a dense solution format. -If the record is already a dense solution format, return itself.

- -

Returns

- -

SampleSet: A SampleSet object.

-
- - -
-
-
- - def - feasible(self, /, rtol=1e-05, atol=1e-08): - - -
- - -

Return a SampleSet with only feasible solutions. -If there is no feasible solution, the record and evaluation are empty.

- -

Args

- -
    -
  • rtol (float): The relative tolerance parameter. Defaults to 1e-5.
    • -
  • atol (float): The absolute tolerance parameter. Defaults to 1e-8.
    • -
- -

Returns

- -

SampleSet: A SampleSet object with only feasible solutions or empty.

- -

Note

- -

The feasible solutions are determined by the following condition: -$$ |0 - v| \leq \mathrm{atol} + \mathrm{rtol} \cdot |v| $$

-
- - -
-
-
- - def - infeasible(self, /, rtol=1e-05, atol=1e-08): - - -
- - -

Return a SampleSet with only infeasible solutions. -If there is no infeasible solution, the record and evaluation are empty.

- -

Args

- -
    -
  • rtol (float): The relative tolerance parameter. Defaults to 1e-5.
    • -
  • atol (float): The absolute tolerance parameter. Defaults to 1e-8.
    • -
- -

Returns

- -

SampleSet: A SampleSet object with only infeasible solutions or empty.

- -

Note

- -

The feasible solutions are determined by the following condition: -$$ |0 - v| > \mathrm{atol} + \mathrm{rtol} \cdot |v| $$

-
- - -
-
-
- - def - lowest(self, /, rtol=1e-05, atol=1e-08): - - -
- - -

Return a SampleSet with feasible solutions which has the lowest objective. -If there is no feasible solution, the record and evaluation are empty.

- -

Args

- -
    -
  • rtol (float): The relative tolerance parameter. Defaults to 1e-5.
    • -
  • atol (float): The absolute tolerance parameter. Defaults to 1e-8.
    • -
- -

Returns

- -

SampleSet: A SampleSet object with feasible solutions or empty.

- -

Note

- -

The feasible solutions are determined by the following condition: -$$ |0 - v| \leq \mathrm{atol} + \mathrm{rtol} \cdot |v| $$

-
- - -
-
-
- - def - is_dense(self, /): - - -
- - -

Return true if the solution is dense.

- -

Returns

- -

bool: True if the solution is dense.

-
- - -
-
-
- - def - is_sparse(self, /): - - -
- - -

Return true if the solution is sparse.

- -

Returns

- -

bool: True if the solution is sparse.

-
- - -
-
-
- - def - get_backend_calculation_time(self, /): - - -
- - -

Return report of the calculation time of the JijZept backends.

- -

Returns

- -

dict: A dictionary of the calculation time of the JijZept backends.

-
- - -
-
-
- measuring_time - - -
- - - - -
-
-
- metadata - - -
- - - - -
-
-
- record - - -
- - - - -
-
-
- evaluation - - -
- - - - -
-
-
-
- - class - Interpreter: - - -
- - -

Interpreter of the JijModeling AST

- -

This class is responsible for

- -
    -
  • Creating OMMX instance from the AST. -
      -
    • This means this module also has responsible to register decision variable ID for each decision variables in AST.
      • -
    • -
  • Manage instance data to be substituted into the Placeholder.
    • -
- -

Examples

- -
-
>>> import jijmodeling as jm
->>> import numpy as np
-
-Create a new interpreter with scalar instance data
->>> interpreter = jm.Interpreter({"a": 1})
-
-Insert instance data after creating the interpreter
->>> interpreter.insert_instance_data("b", 2) # scalar
-
-Python list and numpy array are supported
->>> interpreter.insert_instance_data("c", [3, 4])
->>> interpreter.insert_instance_data("d", [[1, 2], [3, 4]])
->>> interpreter.insert_instance_data("e", np.array([3, 4]))
-
-JaggedArray, non-uniform multi-dimensional array is also supported
->>> interpreter.insert_instance_data("f", [[[1, 2]], [[3, 4, 5]], [[6]]])
->>> interpreter.insert_instance_data("g", jm.JaggedArray([[[1, 2]], [[3, 4, 5]], [[6]]]))
-
-You can get the instance data by using get_instance_data method
->>> interpreter.get_instance_data("a")
-1.0
-
-Array are normalized to numpy array
->>> interpreter.get_instance_data("c")
-array([3., 4.])
->>> interpreter.get_instance_data("e")
-array([3., 4.])
->>> interpreter.get_instance_data("f")  # doctest: +ELLIPSIS
-<jijmodeling.JaggedArray object at 0x...>
-
-Update instance data and re-evaluate expressions
->>> a = jm.Placeholder("a")
->>> x = jm.BinaryVar("x")
->>> interpreter.eval_expr(a * x)  # Original evaluation with a = 1
-Function(x0)
->>> interpreter.insert_instance_data("a", 3)  # Update value of 'a'
->>> interpreter.eval_expr(a * x)  # Re-evaluate with updated value
-Function(3*x0)
-
-
-
- - -
-
- - def - insert_instance_data(self, /, key, value): - - -
- - - - -
-
-
- - def - get_instance_data(self, /, key): - - -
- - - - -
-
-
- - def - instance_data(self, /): - - -
- - - - -
-
-
- - def - get_decision_variable_by_id(self, /, id): - - -
- - - - -
-
-
- - def - get_decision_variable_by_name(self, /, name, subscript=None): - - -
- - - - -
-
-
- - def - eval_scalar(self, /, expr): - - -
- - - - -
-
-
- - def - eval_expr(self, /, expr): - - -
- - - - -
-
-
- - def - eval_constraints(self, /, constraints): - - -
- - - - -
-
-
- - def - eval_problem(self, /, problem, hints=None): - - -
- - -

Evaluate the problem and returns ommx.v1.Instance.

- -

Args

- -
    -
  • problem: a jijmodeling.Problem object to compile.
    • -
  • hints (optional): a list of constraint hints to be detected during evaluation. When omitted or given None, the default constraint hints are used. Pass empty list to disable hint detection entirely.
    • -
- -

Returns

- -

instance: an OMMX v1 instance object.

-
- - -
-
-
- num_decision_variables - - -
- - - - -
-
-
-
- - class - JaggedArray: - - -
- - -

Jagged array, a multi-dimensional array where each element can be an array of different length.

- -

Examples

- -
-
>>> import jijmodeling as jm
->>> arr = jm.JaggedArray([[[1, 2], [3, 4, 5]], [[6]]])
-
-# Three dimensional
->>> assert arr.dim == 3
-
-# __getitem__ works
->>> assert arr[0, 0, 0] == 1.0
->>> assert arr[0, 0, 1] == 2.0
->>> assert arr[0, 1, 0] == 3.0
->>> assert arr[0, 1, 1] == 4.0
->>> assert arr[0, 1, 2] == 5.0
->>> assert arr[1, 0, 0] == 6.0
-
-# out of range
->>> arr[0, 0, 2]
-Traceback (most recent call last):
- ...
-IndexError: Invalid index
-
-# dimension mismatch
->>> arr[0, 0]
-Traceback (most recent call last):
- ...
-IndexError: Invalid index
-
->>> assert arr.size_at([]) == 2  # [[1, 2], [3, 4, 5]] and [[6]]
->>> assert arr.size_at([0]) == 2  # [1, 2] and [3, 4, 5]
->>> assert arr.size_at([1]) == 1  # [[6]]
->>> assert arr.size_at([0, 0]) == 2  # [1, 2]
->>> assert arr.size_at([0, 1]) == 3  # [3, 4, 5]
->>> assert arr.size_at([1, 0]) == 1  # [6]
-
-
-
- - -
-
- - def - get(self, /, index): - - -
- - - - -
-
-
- - def - size_at(self, /, index): - - -
- - - - -
-
-
- dim - - -
- - - - -
-
-
- - \ No newline at end of file diff --git a/apis/jijmodeling/dataset.html b/apis/jijmodeling/dataset.html deleted file mode 100644 index 438e2ee9..00000000 --- a/apis/jijmodeling/dataset.html +++ /dev/null @@ -1,520 +0,0 @@ - - - - - - - jijmodeling.dataset API documentation - - - - - - - - - - - - -
-
-

-jijmodeling.dataset

- - - - - - - 
1from ._jijmodeling import dataset as _dataset  # type: ignore
-2import sys
-3
-4for component in _dataset.__all__:
-5    setattr(sys.modules[__name__], component, getattr(_dataset, component))
-
- - -
-
-
- - class - Miplib: - - -
- - -

Automatically load problems from the MIPLIB dataset.

- -

Warning: the whole dataset is downloaded and cached in your filesystem when -first used. It is quite large (~300MB download, ~600MB extracted), so the -time it takes to download will likely be significant. We recommend first -trying to use this class in a REPL to assure the dataset has been cached.

- -

The dataset is stored at {data_dir}/jijmodeling/miplib, where -{data_dir} depends on your operating system:

- - - - - - - - - - - - - - - - - - - - - - - - - -
Platform{data_dir} locationExample/System Default
Linux$XDG_DATA_HOME or $HOME/.local/share|/home/alice/.local/share
macOS$HOME/Library/Application Support/Users/Alice/Library/Application Support
Windows`%AppData%C:\Users\Alice\AppData\Roaming
- -

Examples

- -
-
>>> import jijmodeling.dataset
-
-Initialize the dataset. If not cached in your filesystem, this will take several minutes.
->>> miplib = jijmodeling.dataset.Miplib()
-
-Show names of each available instance.
->>> miplib.available_names  # doctest: +ELLIPSIS
-[...]
-
-Get basic statistics of a specific instance.
->>> miplib.instance_statistics["unitcal_7"]
-{'variable': 25755, 'constraint': 48939, 'binary': 2856, 'integer': 0, 'continuous': 22899, 'non_zero': 127595}
-
-Load "revised-submissions/miplib2010_pg/instances/pg.mps.gz" in benchmark.zip
->>> problem, instance_data = miplib.load("pg")
-
-
-
- - -
-
- - def - load(self, /, name): - - -
- - -

Load a problem from the MIPLIB dataset.

-
- - -
-
-
- available_names - - -
- - -

List of available problem names.

-
- - -
-
-
- instance_statistics - - -
- - -

Dict of name to problem size, i.e. number of binary/integer/real variable and number of equality/inequality constraints

-
- - -
-
-
-
- - class - Qplib: - - -
- - -

Automatically load problems from the MIPLIB dataset.

- -

Warning: the whole dataset is downloaded and cached in your filesystem when -first used. It is quite large (~700MB download, ~1.5GB extracted), so the -time it takes to download will likely be significant. We recommend first -trying to use this class in a REPL to assure the dataset has been cached.

- -

The dataset is stored at {data_dir}/jijmodeling/qplib, where -{data_dir} depends on your operating system:

- - - - - - - - - - - - - - - - - - - - - - - - - -
Platform{data_dir} locationExample/System Default
Linux$XDG_DATA_HOME or $HOME/.local/share|/home/alice/.local/share
macOS$HOME/Library/Application Support/Users/Alice/Library/Application Support
Windows`%AppData%C:\Users\Alice\AppData\Roaming
- -

Examples

- -
-
>>> import jijmodeling.dataset
-
-Initialize the dataset. If not cached in your filesystem, this will take several minutes.
->>> qplib = jijmodeling.dataset.Qplib()
-
-Show the names of each available instance.
->>> qplib.available_names  # doctest: +ELLIPSIS
-[...]
-
-Get basic statistics of a specific instance.
->>> qplib.instance_statistics["QPLIB_2205"]
-{'variables': 2884, 'constraints': 2874, 'binary': 958, 'integer': 0, 'continuous': 1926, 'non_zero': 13013}
-
-Load a problem instance from QPLIB.
->>> problem, instance_data = qplib.load("QPLIB_1976")
-
-
-
- - -
-
- - def - load(self, /, name): - - -
- - -

Load a problem from the QPLIB dataset.

-
- - -
-
-
- instance_statistics - - -
- - -

Dict of name to problem size, i.e. number of binary/integer/real variable and number of equality/inequality constraints

-
- - -
-
-
- available_names - - -
- - -

List of available problem names.

-
- - -
-
-
- - \ No newline at end of file diff --git a/apis/jijmodeling/experimental.html b/apis/jijmodeling/experimental.html deleted file mode 100644 index 54f34a7b..00000000 --- a/apis/jijmodeling/experimental.html +++ /dev/null @@ -1,1325 +0,0 @@ - - - - - - - jijmodeling.experimental API documentation - - - - - - - - - - - - -
-
-

-jijmodeling.experimental

- - - - - - - 
 1# `jijmodeling.jijmodeling` is a module corresponding to the shared library created by PyO3,
- 2# and `jijmodeling.jijmodeling.experimental` is its submodule defined dynamically
- 3# while the initialization of the shared library.
- 4#
- 5# This file defines a new sub-module `jijmodeling.experimental`,
- 6# and exposes all the components in `jijmodeling.jijmodeling.experimental`
- 7#
- 8
- 9from ._jijmodeling import experimental as _experimental  # type: ignore
-10import sys
-11
-12for component in _experimental.__all__:
-13    setattr(sys.modules[__name__], component, getattr(_experimental, component))
-
- - -
-
-
- - class - Violation: - - -
- - - - -
-
- name - - -
- - - - -
-
-
- expr_values - - -
- - - - -
-
-
- total_violation - - -
- - - - -
-
-
-
- - class - EvaluationResult: - - -
- - - - -
-
- penalties - - -
- - - - -
-
-
- objective - - -
- - - - -
-
-
- constraints - - -
- - - - -
-
-
-
- - class - VarType: - - -
- - - - -
-
- CONTINUOUS = -VarType.CONTINUOUS - - -
- - - - -
-
-
- INTEGER = -VarType.INTEGER - - -
- - - - -
-
-
- BINARY = -VarType.BINARY - - -
- - - - -
-
-
-
- - class - SparseVarValues: - - -
- - - - -
-
- - def - from_array(name, array, var_type=Ellipsis): - - -
- - - - -
-
-
- - def - to_dense(self, /): - - -
- - - - -
-
-
- name - - -
- - - - -
-
-
- values - - -
- - - - -
-
-
- var_type - - -
- - - - -
-
-
- shape - - -
- - - - -
-
-
-
- - class - SolvingTime: - - -
- - - - -
-
- - def - total(self, /): - - -
- - - - -
-
-
- transpiling_time - - -
- - - - -
-
-
- solving_time - - -
- - - - -
-
-
- compiling_time - - -
- - - - -
-
-
- preprocess_time - - -
- - - - -
-
-
- decoding_time - - -
- - - - -
-
-
- postprocess_time - - -
- - - - -
-
-
-
- - class - SystemTime: - - -
- - - - -
-
- request_queuing_time - - -
- - - - -
-
-
- posting_time - - -
- - - - -
-
-
- fetching_problem_time - - -
- - - - -
-
-
- fetching_result_time - - -
- - - - -
-
-
- deserialize_time - - -
- - - - -
-
-
-
- - class - MeasuringTime: - - -
- - - - -
-
- - def - total(self, /): - - -
- - -

Returns the total time measurred, across both SolvingTime and SystemTime.

-
- - -
-
-
- - def - view_solving_time(self, /): - - -
- - -

Returns a readonly view of the internal SolvingTime field.

-
- - -
-
-
- - def - view_system_time(self, /): - - -
- - -

Returns a readonly view of the internal SystemTime field.

-
- - -
-
-
-
- - class - Sample: - - -
- - -

A Sample representing an individual solution found by running the mathematical optimization model.

- -

Variables in var_values are stored in instances of SparseVarValues. This uses a dictionary -style, retaining only non-zero elements. For example, if the values for a two-dimensional -decision variable are x = [[0, 1, 2], [1, 0, 0]], they will be stored as -{(0,1): 1, (0,2): 2, (1,0): 1}. To retrieve this, use sample.var_values["x"].values. -If you want a dense array of decision variables, you can use the to_dense() method.

- -

run_id is a unique identifier of the run in which this sample was found. -Note that this is not the same as a unique identifier of the Sample.

-
- - -
-
- - def - from_dense_arrays(dict, num_occurrences=1, var_types=None, run_id=None, meta_info=None): - - -
- - - - -
-
-
- - def - is_feasible(self, /, epsilon=1e-08): - - -
- - - - -
-
-
- - def - to_dense(self, /): - - -
- - - - -
-
-
- - def - from_dict(dict): - - -
- - -

Converts a python dictionary into a SampleSet.

- -

This is intended to be used primarily with dictionaries generated by -the to_dict() method. As such sparse value maps must be represented as -association lists.

-
- - -
-
-
- - def - to_dict(self, /): - - -
- - -

Converts this SampleSet into a regular python dictionary.

- -

Note that this dictionary has a slightly different structure to better support JSON -serialization of the output dictionary: sparse values are stored differently. Any mapping -with tuples as keys is transformed into an association list of key-value pairs, -that is, [(k1, v1), (k2, v2), ...].

-
- - -
-
-
- eval - - -
- - - - -
-
-
- var_values - - -
- - - - -
-
-
- run_info - - -
- - - - -
-
-
- run_id - - -
- - - - -
-
-
- num_occurrences - - -
- - - - -
-
-
-
- - class - SampleSet: - - -
- - - - -
-
- - def - from_array(samples): - - -
- - -

Builds a SampleSet from a list of dictionaries, where each entry is interpreted as a sample.

- -

Args

- -
    -
  • samples(list[dict[str, list | numpy.ndarray]])
    • -
-
- - -
-
-
- - def - concat(family): - - -
- - -

Creates a single SampleSet by concatenating a list of multiple SampleSets.

- -

Args

- -
    -
  • family (list[SampleSet])
    • -
-
- - -
-
-
- - def - from_dict(dict): - - -
- - -

Converts a python dictionary into a SampleSet.

- -

This is intended to be used primarily with dictionaries generated by -the to_dict() method. As such sparse value maps must be represented as -association lists.

-
- - -
-
-
- - def - to_dict(self, /): - - -
- - -

Converts this SampleSet into a regular python dictionary.

- -

Note that this dictionary has a slightly different structure to better support JSON -serialization of the output dictionary: sparse values are stored differently. Any mapping -with tuples as keys is transformed into an association list of key-value pairs, -that is, [(k1, v1), (k2, v2), ...].

-
- - -
-
-
- - def - feasibles(self, /, epsilon=1e-08): - - -
- - -

Returns a SampleSet containing only the feasible samples.

- -

Args

- -
    -
  • epsilon (float, optional): Tolerance threshold for constraint violations. Defaults to $1e-8$.
    • -
- -

Returns

- -
    -
  • SampleSet: a feasible subset of the current set.
    • -
-
- - -
-
-
- - def - separate(self, /): - - -
- - -

Splits this SampleSet based on the run_id of the samples.

- -

In other words, for each distinct run_id among the Samples contained in this instance, a -new SampleSet is created to store all Samples with that ID.

- -

Returns

- -
    -
  • sets (dict[str, SampleSet]): The separated SampleSets. Keys are the run IDs.
    • -
-
- - -
-
-
- - def - lowest(self, /, epsilon=1e-08): - - -
- - -

Returns a list of the feasible samples which have the lowest objective value. -If there are no feasible solutions, this returns an empty list.

- -

Args

- -
    -
  • epsilon (float, optional): Tolerance threshold. Objective values within this tolerance are included, even if not exactly the minimum value. Defaults to $1e-8$.
    • -
- -

Returns

- -

lowest_samples: A list of Sample objects with the lowest ojective value in this SampleSet.

-
- - -
-
-
- set_info - - -
- - - - -
-
-
- run_info - - -
- - - - -
-
-
- data - - -
- - - - -
-
-
- measuring_time - - -
- - - - -
-
-
- set_id - - -
- - - - -
-
-
- run_times - - -
- - - - -
-
-
- - \ No newline at end of file diff --git a/apis/jijmodeling/range.html b/apis/jijmodeling/range.html deleted file mode 100644 index 1799cb40..00000000 --- a/apis/jijmodeling/range.html +++ /dev/null @@ -1,278 +0,0 @@ - - - - - - - jijmodeling.range API documentation - - - - - - - - - - - - -
-
-

-jijmodeling.range

- - - - - - - 
 1# `jijmodeling.jijmodeling` is a module corresponding to the shared library
- 2# created by PyO3,
- 3# and `jijmodeling.jijmodeling.range` is its submodule defined dynamically
- 4# while the initialization of the shared library.
- 5#
- 6# This file defines a new sub-module `jijmodeling.range`,
- 7# and exposes all the components in `jijmodeling.jijmodeling.range`
- 8#
- 9
-10from .._jijmodeling import range as _range  # type: ignore
-11import sys
-12
-13for component in _range.__all__:
-14    setattr(sys.modules[__name__], component, getattr(_range, component))
-
- - -
-
- - \ No newline at end of file diff --git a/apis/jijmodeling/range/size.html b/apis/jijmodeling/range/size.html deleted file mode 100644 index 45a0810e..00000000 --- a/apis/jijmodeling/range/size.html +++ /dev/null @@ -1,273 +0,0 @@ - - - - - - - jijmodeling.range.size API documentation - - - - - - - - - - - - -
-
-

-jijmodeling.range.size

- - - - - - - 
 1# `jijmodeling.jijmodeling` is a module corresponding to the shared library
- 2# created by PyO3,
- 3# and `jijmodeling.jijmodeling.range` is its submodule defined dynamically
- 4# while the initialization of the shared library.
- 5#
- 6# This file defines a new sub-module `jijmodeling.range`,
- 7# and exposes all the components in `jijmodeling.jijmodeling.range`
- 8#
- 9
-10from . import size as _size  # type: ignore
-11import sys
-12
-13for component in _size.__all__:  # type: ignore
-14    setattr(sys.modules[__name__], component, getattr(_size, component))
-
- - -
-
- - \ No newline at end of file diff --git a/apis/jijmodeling/range/value.html b/apis/jijmodeling/range/value.html deleted file mode 100644 index 435c7d7f..00000000 --- a/apis/jijmodeling/range/value.html +++ /dev/null @@ -1,273 +0,0 @@ - - - - - - - jijmodeling.range.value API documentation - - - - - - - - - - - - -
-
-

-jijmodeling.range.value

- - - - - - - 
 1# `jijmodeling.jijmodeling` is a module corresponding to the shared library
- 2# created by PyO3,
- 3# and `jijmodeling.jijmodeling.range` is its submodule defined dynamically
- 4# while the initialization of the shared library.
- 5#
- 6# This file defines a new sub-module `jijmodeling.range`,
- 7# and exposes all the components in `jijmodeling.jijmodeling.range`
- 8#
- 9
-10from . import value as _value  # type: ignore
-11import sys
-12
-13for component in _value.__all__:  # type: ignore
-14    setattr(sys.modules[__name__], component, getattr(_value, component))
-
- - -
-
- - \ No newline at end of file diff --git a/apis/search.js b/apis/search.js deleted file mode 100644 index be9864b9..00000000 --- a/apis/search.js +++ /dev/null @@ -1,46 +0,0 @@ -window.pdocSearch = (function(){ -/** elasticlunr - http://weixsong.github.io * Copyright (C) 2017 Oliver Nightingale * Copyright (C) 2017 Wei Song * MIT Licensed */!function(){function e(e){if(null===e||"object"!=typeof e)return e;var t=e.constructor();for(var n in e)e.hasOwnProperty(n)&&(t[n]=e[n]);return t}var t=function(e){var n=new t.Index;return n.pipeline.add(t.trimmer,t.stopWordFilter,t.stemmer),e&&e.call(n,n),n};t.version="0.9.5",lunr=t,t.utils={},t.utils.warn=function(e){return function(t){e.console&&console.warn&&console.warn(t)}}(this),t.utils.toString=function(e){return void 0===e||null===e?"":e.toString()},t.EventEmitter=function(){this.events={}},t.EventEmitter.prototype.addListener=function(){var e=Array.prototype.slice.call(arguments),t=e.pop(),n=e;if("function"!=typeof t)throw new TypeError("last argument must be a function");n.forEach(function(e){this.hasHandler(e)||(this.events[e]=[]),this.events[e].push(t)},this)},t.EventEmitter.prototype.removeListener=function(e,t){if(this.hasHandler(e)){var n=this.events[e].indexOf(t);-1!==n&&(this.events[e].splice(n,1),0==this.events[e].length&&delete this.events[e])}},t.EventEmitter.prototype.emit=function(e){if(this.hasHandler(e)){var t=Array.prototype.slice.call(arguments,1);this.events[e].forEach(function(e){e.apply(void 0,t)},this)}},t.EventEmitter.prototype.hasHandler=function(e){return e in this.events},t.tokenizer=function(e){if(!arguments.length||null===e||void 0===e)return[];if(Array.isArray(e)){var n=e.filter(function(e){return null===e||void 0===e?!1:!0});n=n.map(function(e){return t.utils.toString(e).toLowerCase()});var i=[];return n.forEach(function(e){var n=e.split(t.tokenizer.seperator);i=i.concat(n)},this),i}return e.toString().trim().toLowerCase().split(t.tokenizer.seperator)},t.tokenizer.defaultSeperator=/[\s\-]+/,t.tokenizer.seperator=t.tokenizer.defaultSeperator,t.tokenizer.setSeperator=function(e){null!==e&&void 0!==e&&"object"==typeof e&&(t.tokenizer.seperator=e)},t.tokenizer.resetSeperator=function(){t.tokenizer.seperator=t.tokenizer.defaultSeperator},t.tokenizer.getSeperator=function(){return t.tokenizer.seperator},t.Pipeline=function(){this._queue=[]},t.Pipeline.registeredFunctions={},t.Pipeline.registerFunction=function(e,n){n in t.Pipeline.registeredFunctions&&t.utils.warn("Overwriting existing registered function: "+n),e.label=n,t.Pipeline.registeredFunctions[n]=e},t.Pipeline.getRegisteredFunction=function(e){return e in t.Pipeline.registeredFunctions!=!0?null:t.Pipeline.registeredFunctions[e]},t.Pipeline.warnIfFunctionNotRegistered=function(e){var n=e.label&&e.label in this.registeredFunctions;n||t.utils.warn("Function is not registered with pipeline. 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configuration"),this.buildDefaultConfig(n)}},t.Configuration.prototype.buildDefaultConfig=function(e){this.reset(),e.forEach(function(e){this.config[e]={boost:1,bool:"OR",expand:!1}},this)},t.Configuration.prototype.buildUserConfig=function(e,n){var i="OR",o=!1;if(this.reset(),"bool"in e&&(i=e.bool||i),"expand"in e&&(o=e.expand||o),"fields"in e)for(var r in e.fields)if(n.indexOf(r)>-1){var s=e.fields[r],u=o;void 0!=s.expand&&(u=s.expand),this.config[r]={boost:s.boost||0===s.boost?s.boost:1,bool:s.bool||i,expand:u}}else t.utils.warn("field name in user configuration not found in index instance fields");else this.addAllFields2UserConfig(i,o,n)},t.Configuration.prototype.addAllFields2UserConfig=function(e,t,n){n.forEach(function(n){this.config[n]={boost:1,bool:e,expand:t}},this)},t.Configuration.prototype.get=function(){return 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e.elements=this.toArray(),e.length=e.elements.length,e},lunr.SortedSet.prototype.union=function(e){var t,n,i;this.length>=e.length?(t=this,n=e):(t=e,n=this),i=t.clone();for(var o=0,r=n.toArray();o

\n"}, "jijmodeling.ModelingError": {"fullname": "jijmodeling.ModelingError", "modulename": "jijmodeling", "qualname": "ModelingError", "kind": "class", "doc": "

Error while creating a model.

\n", "bases": "builtins.Exception"}, "jijmodeling.InterpreterError": {"fullname": "jijmodeling.InterpreterError", "modulename": "jijmodeling", "qualname": "InterpreterError", "kind": "class", "doc": "

Error while interpreting the model.

\n", "bases": "builtins.RuntimeError"}, "jijmodeling.ProtobufSerializationError": {"fullname": "jijmodeling.ProtobufSerializationError", "modulename": "jijmodeling", "qualname": "ProtobufSerializationError", "kind": "class", "doc": "

Failed to encode the object to a buffer.

\n", "bases": "builtins.Exception"}, "jijmodeling.ProtobufDeserializationError": {"fullname": "jijmodeling.ProtobufDeserializationError", "modulename": "jijmodeling", "qualname": "ProtobufDeserializationError", "kind": "class", "doc": "

Failed to decode the buffer to an instance.

\n", "bases": "builtins.Exception"}, "jijmodeling.DataType": {"fullname": "jijmodeling.DataType", "modulename": "jijmodeling", "qualname": "DataType", "kind": "class", "doc": "

\n"}, "jijmodeling.DataType.FLOAT": {"fullname": "jijmodeling.DataType.FLOAT", "modulename": "jijmodeling", "qualname": "DataType.FLOAT", "kind": "variable", "doc": "

\n", "default_value": "DataType.FLOAT"}, "jijmodeling.DataType.INTEGER": {"fullname": "jijmodeling.DataType.INTEGER", "modulename": "jijmodeling", "qualname": "DataType.INTEGER", "kind": "variable", "doc": "

\n", "default_value": "DataType.INTEGER"}, "jijmodeling.NumberLit": {"fullname": "jijmodeling.NumberLit", "modulename": "jijmodeling", "qualname": "NumberLit", "kind": "class", "doc": "

A class for creating a number literal

\n\n

The NumberLit class is used to create a number literal.\nIts instance is automatically generated by the return value of\narithmetic or mathematical functions taking a number literal and\nan object defined by jijmodeling as arguments.

\n\n

Attributes

\n\n
    \n
  • value (int | float): A numeric value.
    • \n
  • dtype (DataType): A type of the value.\n
      \n
    • dtype is DataType.INTEGER if the type of the value is integer else dtype is DataType.FLOAT.
      • \n
    • \n
\n\n

Args

\n\n
    \n
  • value (int | float): A numeric value.
    • \n
\n\n

Examples

\n\n

Create a number literal with a integer value 123.

\n\n
\n
>>> import jijmodeling as jm\n>>> v = jm.NumberLit(123)\n>>> assert v.value == 123\n>>> assert v.dtype == jm.DataType.INTEGER\n
\n
\n\n

Create a number literal with a float value 1.23.

\n\n
\n
>>> import jijmodeling as jm\n>>> v = jm.NumberLit(1.23)\n>>> assert v.value == 1.23\n>>> assert v.dtype == jm.DataType.FLOAT\n
\n
\n"}, "jijmodeling.NumberLit.value": {"fullname": "jijmodeling.NumberLit.value", "modulename": "jijmodeling", "qualname": "NumberLit.value", "kind": "variable", "doc": "

\n"}, "jijmodeling.NumberLit.dtype": {"fullname": "jijmodeling.NumberLit.dtype", "modulename": "jijmodeling", "qualname": "NumberLit.dtype", "kind": "variable", "doc": "

\n"}, "jijmodeling.Placeholder": {"fullname": "jijmodeling.Placeholder", "modulename": "jijmodeling", "qualname": "Placeholder", "kind": "class", "doc": "

A class for creating a placeholder

\n\n

The Placeholder class is used to create a placeholder.\nIt is a symbol to be replaced by a numerical value when you solve an optimization problem.

\n\n

The index operator ([]) of a placeholder with ndim >= 1 returns a Subscript object.

\n\n

Attributes

\n\n
    \n
  • name (str): A name of the placeholder.
    • \n
  • ndim (int): The number of dimensions of the placeholder.
    • \n
  • shape (list of Optional[Expression], optional): The (partial) shape of the placeholder if given.
    • \n
  • dtype (DataType, optional): The data type (DataType.INT or DataType.FLOAT) of the placeholder.
    • \n
  • jagged (boolean, defaut: False): True if the placeholder will be treated as a jagged array in random data generation. Ignored for scalars.
    • \n
  • description (str, optional): A description of the placeholder.
    • \n
\n\n

Args

\n\n
    \n
  • name (str): A name of the placeholder.
    • \n
  • ndim (Optional[int]): The number of dimensions of the placeholder. Defaults to 0. The ndim must be set to a non-negative value and must concide with the length of shape if both specified. If None is given, you must specify shape explicitly and the length of shape will be used.
    • \n
  • shape (list[Optional[Expression]], optional): The (partial) shape of the placeholder. Used for random data generation.
    • \n
  • dtype (DataType, optional): The data type (DataType.INT or DataType.FLOAT) of the placeholder. Used for random data generation.
    • \n
  • jagged (boolean, defaut: False): True if the placeholder will be treated as a jagged array in random data generation. Ignored for scalars.
    • \n
  • latex (str, optional): A LaTeX-name of the placeholder to be represented in Jupyter notebook.\nIt is set to name by default.
    • \n
  • description (str, optional): A description of the placeholder.
    • \n
\n\n

Raises

\n\n
    \n
  • TypeError: Raises if set a float value to ndim.
    • \n
  • OverflowError: Raises if set a negative value to ndim.
    • \n
\n\n

Examples

\n\n

Create a scalar (or ndim is 0) placeholder whose name is \"a\".

\n\n
\n
>>> import jijmodeling as jm\n>>> a = jm.Placeholder("a")\n
\n
\n\n

Create a 2-dimensional placeholder whose name is \"m\".

\n\n
\n
>>> import jijmodeling as jm\n>>> m = jm.Placeholder("m", ndim=2)\n
\n
\n\n

Create a 1-dimensional placeholder with the index of 123.

\n\n
\n
>>> import jijmodeling as jm\n>>> a = jm.Placeholder("a", ndim=2)\n>>> a[123]\nPlaceholder(name='a', ndim=2)[NumberLit(value=123)]\n
\n
\n"}, "jijmodeling.Placeholder.len_at": {"fullname": "jijmodeling.Placeholder.len_at", "modulename": "jijmodeling", "qualname": "Placeholder.len_at", "kind": "function", "doc": "

\n", "signature": "(self, /, axis, *, latex=None, description=None):", "funcdef": "def"}, "jijmodeling.Placeholder.set_latex": {"fullname": "jijmodeling.Placeholder.set_latex", "modulename": "jijmodeling", "qualname": "Placeholder.set_latex", "kind": "function", "doc": "

Set the LaTeX representation of the object.\nIf the LaTeX representation is not set, the default representation is set.

\n\n

Args

\n\n

latex (str, optional): LaTeX representation of the object. Defaults to None.

\n", "signature": "(self, /, latex=None):", "funcdef": "def"}, "jijmodeling.Placeholder.ndim": {"fullname": "jijmodeling.Placeholder.ndim", "modulename": "jijmodeling", "qualname": "Placeholder.ndim", "kind": "variable", "doc": "

\n"}, "jijmodeling.Placeholder.shape": {"fullname": "jijmodeling.Placeholder.shape", "modulename": "jijmodeling", "qualname": "Placeholder.shape", "kind": "variable", "doc": "

\n"}, "jijmodeling.Placeholder.description": {"fullname": "jijmodeling.Placeholder.description", "modulename": "jijmodeling", "qualname": "Placeholder.description", "kind": "variable", "doc": "

\n"}, "jijmodeling.Placeholder.name": {"fullname": "jijmodeling.Placeholder.name", "modulename": "jijmodeling", "qualname": "Placeholder.name", "kind": "variable", "doc": "

\n"}, "jijmodeling.Placeholder.dtype": {"fullname": "jijmodeling.Placeholder.dtype", "modulename": "jijmodeling", "qualname": "Placeholder.dtype", "kind": "variable", "doc": "

\n"}, "jijmodeling.Placeholder.jagged": {"fullname": "jijmodeling.Placeholder.jagged", "modulename": "jijmodeling", "qualname": "Placeholder.jagged", "kind": "variable", "doc": "

\n"}, "jijmodeling.Range": {"fullname": "jijmodeling.Range", "modulename": "jijmodeling", "qualname": "Range", "kind": "class", "doc": "

A class representing a half-open interval.

\n\n

The Range class is used to represent a half-open interval [start, end).\nThis class does not have a constructor because it should be created by the Element class.

\n\n

Attributes

\n\n
    \n
  • start: The lower bound of the range (inclusive).
    • \n
  • end: The upper bound of the range (exclusive).
    • \n
\n\n

Note

\n\n

This class does not contain any decision variable.

\n"}, "jijmodeling.Range.start": {"fullname": "jijmodeling.Range.start", "modulename": "jijmodeling", "qualname": "Range.start", "kind": "variable", "doc": "

\n"}, "jijmodeling.Range.end": {"fullname": "jijmodeling.Range.end", "modulename": "jijmodeling", "qualname": "Range.end", "kind": "variable", "doc": "

\n"}, "jijmodeling.Element": {"fullname": "jijmodeling.Element", "modulename": "jijmodeling", "qualname": "Element", "kind": "class", "doc": "

A class for creating an element

\n\n

The Element class is used to create an element.\nIt is used in the following cases:

\n\n
    \n
  • an index of summation $\\displaystyle \\sum$ (SumOp)
    • \n
  • an index of product $\\displaystyle \\prod$ (ProdOp)
    • \n
  • a bound variable of the universal quantifier $\\forall$ (Forall)
    • \n
\n\n

Elements specify a set to which they belong. The set can be:

\n\n
    \n
  1. A half-open range, where the lower bound is included and the upper bound is excluded.
    2. \n
  2. A Placeholder, Element, or Subscript object with ndim >= 1.
    3. \n
\n\n

Ranges are generally specified with tuples as (start, end). For\nconvenience, passing a single number or scalar object as the argument is\ninterpreted as the end of a range starting from zero.

\n\n

The index operator ([]) of an element with ndim >= 1 returns a Subscript object.

\n\n

Attributes

\n\n
    \n
  • name (str): A name of the element.
    • \n
  • ndim (int): The number of dimensions of the element. The value is one less than the value of belong_to.ndim.
    • \n
  • description (str): A description of the element.
    • \n
  • belong_to: A set the element belongs to.
    • \n
\n\n

Args

\n\n
    \n
  • name (str): A name of the element.
    • \n
  • belong_to: A set the element belongs to.
    • \n
  • latex (str, optional): A LaTeX-name of the element to be represented in Jupyter notebook.\n
      \n
    • It is set to name by default.
      • \n
    • \n
  • description (str, optional): A description of the element.
    • \n
\n\n

Examples

\n\n

Note that belong_to is a positional argument, not a keyword\nargument, and so does not need to be written out. This is done in some\nof these examples for clarity.

\n\n

Create an element that belongs to a half-open range.

\n\n
\n
>>> import jijmodeling as jm\n>>> i = jm.Element("i", belong_to=(0,10))\n
\n
\n\n

If you pass a scalar as the belong_to argument, the set that the element belongs to is a range starting at 0 going up to that value.

\n\n
\n
>>> import jijmodeling as jm\n>>> i = jm.Element("i", 10)\n>>> assert jm.is_same(i, jm.Element("i", belong_to=(0,10)))\n
\n
\n\n

The applies not just to numbers, but certain scalars, like Placeholder (with ndim == 0).

\n\n
\n
>>> import jijmodeling as jm\n>>> n = jm.Placeholder("N")\n>>> i = jm.Element("i", n)\n>>> assert jm.is_same(i, jm.Element("i", belong_to=(0,n)))\n
\n
\n\n

Create an element that belongs to a 1-dimensional placeholder.

\n\n
\n
>>> import jijmodeling as jm\n>>> E = jm.Placeholder("E", ndim=1)\n>>> e = jm.Element("e", E)\n
\n
\n\n

Create a 1-dimensional element with the index of 123.

\n\n
\n
>>> import jijmodeling as jm\n>>> a = jm.Placeholder("a", ndim=2)\n>>> e = jm.Element("e", a)\n>>> e[123]\nElement(name='e', belong_to=Placeholder(name='a', ndim=2))[NumberLit(value=123)]\n
\n
\n"}, "jijmodeling.Element.len_at": {"fullname": "jijmodeling.Element.len_at", "modulename": "jijmodeling", "qualname": "Element.len_at", "kind": "function", "doc": "

\n", "signature": "(self, /, axis, *, latex=None, description=None):", "funcdef": "def"}, "jijmodeling.Element.set_latex": {"fullname": "jijmodeling.Element.set_latex", "modulename": "jijmodeling", "qualname": "Element.set_latex", "kind": "function", "doc": "

Set the LaTeX representation of the object.\nIf the LaTeX representation is not set, the default representation is set.

\n\n

Args

\n\n

latex (str, optional): LaTeX representation of the object. Defaults to None.

\n", "signature": "(self, /, latex=None):", "funcdef": "def"}, "jijmodeling.Element.name": {"fullname": "jijmodeling.Element.name", "modulename": "jijmodeling", "qualname": "Element.name", "kind": "variable", "doc": "

\n"}, "jijmodeling.Element.ndim": {"fullname": "jijmodeling.Element.ndim", "modulename": "jijmodeling", "qualname": "Element.ndim", "kind": "variable", "doc": "

\n"}, "jijmodeling.Element.belong_to": {"fullname": "jijmodeling.Element.belong_to", "modulename": "jijmodeling", "qualname": "Element.belong_to", "kind": "variable", "doc": "

\n"}, "jijmodeling.Element.shape": {"fullname": "jijmodeling.Element.shape", "modulename": "jijmodeling", "qualname": "Element.shape", "kind": "variable", "doc": "

\n"}, "jijmodeling.Element.description": {"fullname": "jijmodeling.Element.description", "modulename": "jijmodeling", "qualname": "Element.description", "kind": "variable", "doc": "

\n"}, "jijmodeling.Subscript": {"fullname": "jijmodeling.Subscript", "modulename": "jijmodeling", "qualname": "Subscript", "kind": "class", "doc": "

A class for representing a subscripted variable

\n\n

The Subscript class is used to represent a variable with subscriptions.

\n\n

Attributes

\n\n
    \n
  • variable: A variable that has subscripts.
    • \n
  • subscripts (list): A list of subscripts.
    • \n
  • ndim (int): The number of dimensions of the subscripted variable.
    • \n
\n\n

Note

\n\n

The Subscript class does not have a constructor.

\n"}, "jijmodeling.Subscript.len_at": {"fullname": "jijmodeling.Subscript.len_at", "modulename": "jijmodeling", "qualname": "Subscript.len_at", "kind": "function", "doc": "

\n", "signature": "(self, /, axis, *, latex=None, description=None):", "funcdef": "def"}, "jijmodeling.Subscript.set_latex": {"fullname": "jijmodeling.Subscript.set_latex", "modulename": "jijmodeling", "qualname": "Subscript.set_latex", "kind": "function", "doc": "

Set the LaTeX representation of the object.\nIf the LaTeX representation is not set, the default representation is set.

\n\n

Args

\n\n

latex (str, optional): LaTeX representation of the object. Defaults to None.

\n", "signature": "(self, /, latex=None):", "funcdef": "def"}, "jijmodeling.Subscript.shape": {"fullname": "jijmodeling.Subscript.shape", "modulename": "jijmodeling", "qualname": "Subscript.shape", "kind": "variable", "doc": "

\n"}, "jijmodeling.Subscript.ndim": {"fullname": "jijmodeling.Subscript.ndim", "modulename": "jijmodeling", "qualname": "Subscript.ndim", "kind": "variable", "doc": "

\n"}, "jijmodeling.Subscript.subscripts": {"fullname": "jijmodeling.Subscript.subscripts", "modulename": "jijmodeling", "qualname": "Subscript.subscripts", "kind": "variable", "doc": "

\n"}, "jijmodeling.Subscript.variable": {"fullname": "jijmodeling.Subscript.variable", "modulename": "jijmodeling", "qualname": "Subscript.variable", "kind": "variable", "doc": "

\n"}, "jijmodeling.ArrayLength": {"fullname": "jijmodeling.ArrayLength", "modulename": "jijmodeling", "qualname": "ArrayLength", "kind": "class", "doc": "

A class for referring to the length of an array at a given axis.

\n\n

The ArrayLength class is to refer to the number of elements of an axis in an array.

\n\n

This class is not intended to be constructed directly. Instead, we\nrecommend using the len_at method of Placeholder, Element or\nSubscript.

\n\n

Attributes

\n\n
    \n
  • array: A variable with ndim >= 1.
    • \n
  • axis: An axis index. A $n$-dimensional variable has $n$ axes. Axis 0 is the array's outermost axis and $n-1$ is the innermost.
    • \n
  • description (str, optional): A description of the ArrayLength instance.
    • \n
\n\n

Raises

\n\n

ModelingError: Raises if axis >= array.ndim.

\n\n

Examples

\n\n

Create a length of axis 0 in a 2-dimensional placeholder.

\n\n
\n
>>> import jijmodeling as jm\n>>> a = jm.Placeholder("a", ndim=2)\n>>> N = a.len_at(0, latex="N")\n
\n
\n"}, "jijmodeling.ArrayLength.set_latex": {"fullname": "jijmodeling.ArrayLength.set_latex", "modulename": "jijmodeling", "qualname": "ArrayLength.set_latex", "kind": "function", "doc": "

Set the LaTeX representation of the object.\nIf the LaTeX representation is not set, the default representation is set.

\n\n

Args

\n\n

latex (str, optional): LaTeX representation of the object. Defaults to None.

\n", "signature": "(self, /, latex=None):", "funcdef": "def"}, "jijmodeling.ArrayLength.axis": {"fullname": "jijmodeling.ArrayLength.axis", "modulename": "jijmodeling", "qualname": "ArrayLength.axis", "kind": "variable", "doc": "

\n"}, "jijmodeling.ArrayLength.array": {"fullname": "jijmodeling.ArrayLength.array", "modulename": "jijmodeling", "qualname": "ArrayLength.array", "kind": "variable", "doc": "

\n"}, "jijmodeling.ArrayLength.description": {"fullname": "jijmodeling.ArrayLength.description", "modulename": "jijmodeling", "qualname": "ArrayLength.description", "kind": "variable", "doc": "

\n"}, "jijmodeling.DummyIndexedVar": {"fullname": "jijmodeling.DummyIndexedVar", "modulename": "jijmodeling", "qualname": "DummyIndexedVar", "kind": "class", "doc": "

A class for representing a subscripted variable with dummy indices

\n\n

The DummyIndexedVar class is an intermediate representation to support syntactic sugar of sum/prod with slices.

\n\n

Note

\n\n

The DummyIndexedVar class does not have a constructor.

\n"}, "jijmodeling.DummyIndexedVar.sum": {"fullname": "jijmodeling.DummyIndexedVar.sum", "modulename": "jijmodeling", "qualname": "DummyIndexedVar.sum", "kind": "function", "doc": "

Take a sum of the decision variable over the elements for which the slice is given and return a SumOp object.

\n\n

Returns

\n\n

SumOp: A SumOp object taken a sum of the decision variable over the elements for which the slice is given.

\n\n

Note

\n\n

An automatically created dummy index

\n\n
    \n
  • has a name of the form __dummy_{decision_var.name}_{axis} where axis is the axis of the slice.
    • \n
  • belongs to a range whose start defaults to 0 and end defaults to the length of the axis.
    • \n
  • has description of the form dummy index at {axis} for {decision_var.name}.
    • \n
  • has latex string of the form \\\\ast_{axis}.
    • \n
\n\n

Examples

\n\n

Create a SumOp object taken a sum of the 2-dim binary variable over the 0-th elements for which the slice is given.

\n\n
\n
>>> import jijmodeling as jm\n>>> n = jm.Placeholder("n")\n>>> x = jm.BinaryVar("x", shape=(n, n))\n>>> i = jm.Element("__dummy_x_0", belong_to=n)\n>>> j = jm.Element("j", belong_to=n)\n>>> assert jm.is_same(x[:, j].sum(), jm.sum(i, x[i, j]))\n
\n
\n", "signature": "(self, /):", "funcdef": "def"}, "jijmodeling.DummyIndexedVar.prod": {"fullname": "jijmodeling.DummyIndexedVar.prod", "modulename": "jijmodeling", "qualname": "DummyIndexedVar.prod", "kind": "function", "doc": "

Take a prod of the decision variable over the elements for which the slice is given and return a ProdOp object.

\n\n

Returns

\n\n

ProdOp: A ProdOp object taken a prod of the decision variable over the elements for which the slice is given.

\n\n

Note

\n\n

An automatically created dummy index

\n\n
    \n
  • has a name of the form __dummy_{decision_var.name}_{axis} where axis is the axis of the slice.
    • \n
  • belongs to a range whose start defaults to 0 and end defaults to the length of the axis.
    • \n
  • has description of the form dummy index at {axis} for {decision_var.name}.
    • \n
  • has latex string of the form \\\\ast_{axis}.
    • \n
\n\n

Examples

\n\n

Create a ProdOp object taken a prod of the 2-dim binary variable over the 0-th elements for which the slice is given.

\n\n
\n
>>> import jijmodeling as jm\n>>> n = jm.Placeholder("n")\n>>> x = jm.BinaryVar("x", shape=(n, n))\n>>> i = jm.Element("__dummy_x_0", belong_to=n)\n>>> j = jm.Element("j", belong_to=n)\n>>> assert jm.is_same(x[:, j].prod(), jm.prod(i, x[i, j]))\n
\n
\n", "signature": "(self, /):", "funcdef": "def"}, "jijmodeling.BinaryVar": {"fullname": "jijmodeling.BinaryVar", "modulename": "jijmodeling", "qualname": "BinaryVar", "kind": "class", "doc": "

A class for creating a binary variable

\n\n

The BinaryVar class is used to create a binary variable.

\n\n

The index operator ([]) of a binary variable with ndim >= 1 returns a Subscript object.

\n\n

Attributes

\n\n
    \n
  • name (str): A name of the binary variable.
    • \n
  • shape (tuple): A tuple with the size of each dimension of the binary variable. Empty if the variable is not multi-dimensional.
    • \n
  • description (str): A description of the binary variable.
    • \n
\n\n

Args

\n\n
    \n
  • name (str): A name of the binary variable.
    • \n
  • shape (list | tuple): A sequence with the size of each dimension of the binary variable. Defaults to an empty tuple (a scalar value).\n
      \n
    • Each item in shape must be a valid expression evaluating to a non-negative scalar.
      • \n
    • \n
  • latex (str, optional): A LaTeX-name of the binary variable to be represented in Jupyter notebook.\n
      \n
    • It is set to name by default.
      • \n
    • \n
  • description (str, optional): A description of the binary variable.
    • \n
\n\n

Examples

\n\n

Create a scalar binary variable whose name is \"z\".

\n\n
\n
>>> import jijmodeling as jm\n>>> z = jm.BinaryVar("z")\n
\n
\n\n

Create a 2-dimensional binary variable whose name is \"x\" and has a 2x2 shape.

\n\n
\n
>>> import jijmodeling as jm\n>>> x = jm.BinaryVar("x", shape=[2, 2])\n
\n
\n\n

Create a 1-dimensional binary variable with the index of 123.

\n\n
\n
>>> import jijmodeling as jm\n>>> x = jm.BinaryVar("x", shape=[124])\n>>> x[123]\nBinaryVar(name='x', shape=[NumberLit(value=124)])[NumberLit(value=123)]\n
\n
\n"}, "jijmodeling.BinaryVar.set_latex": {"fullname": "jijmodeling.BinaryVar.set_latex", "modulename": "jijmodeling", "qualname": "BinaryVar.set_latex", "kind": "function", "doc": "

Set the LaTeX representation of the object.\nIf the LaTeX representation is not set, the default representation is set.

\n\n

Args

\n\n

latex (str, optional): LaTeX representation of the object. Defaults to None.

\n", "signature": "(self, /, latex=None):", "funcdef": "def"}, "jijmodeling.BinaryVar.ndim": {"fullname": "jijmodeling.BinaryVar.ndim", "modulename": "jijmodeling", "qualname": "BinaryVar.ndim", "kind": "variable", "doc": "

\n"}, "jijmodeling.BinaryVar.name": {"fullname": "jijmodeling.BinaryVar.name", "modulename": "jijmodeling", "qualname": "BinaryVar.name", "kind": "variable", "doc": "

\n"}, "jijmodeling.BinaryVar.description": {"fullname": "jijmodeling.BinaryVar.description", "modulename": "jijmodeling", "qualname": "BinaryVar.description", "kind": "variable", "doc": "

\n"}, "jijmodeling.BinaryVar.shape": {"fullname": "jijmodeling.BinaryVar.shape", "modulename": "jijmodeling", "qualname": "BinaryVar.shape", "kind": "variable", "doc": "

\n"}, "jijmodeling.IntegerVar": {"fullname": "jijmodeling.IntegerVar", "modulename": "jijmodeling", "qualname": "IntegerVar", "kind": "class", "doc": "

A class for creating an integer variable

\n\n

The IntegerVar class is used to create an integer variable.\nThe lower and upper bounds of the variable can be specified by:

\n\n
    \n
  • an integer value
    • \n
  • a float value
    • \n
  • a scalar expression that does not contains any decision variable
    • \n
  • a Placeholder object whose dimensionality is equal to that of this variable.
    • \n
  • a subscripted variable whose dimensionality is equal to that of this variable.
    • \n
\n\n

The index operator ([]) of a variable with ndim >= 1 returns a Subscript object.

\n\n

Attributes

\n\n
    \n
  • name (str): A name of the integer variable.
    • \n
  • shape (tuple): A tuple with the size of each dimension of the integer variable. Empty if the variable is not multi-dimensional.
    • \n
  • lower_bound: The lower bound of the variable.
    • \n
  • upper_bound: The upper bound of the variable.
    • \n
  • description (str): A description of the integer variable.
    • \n
\n\n

Args

\n\n
    \n
  • name (`str): A name of the integer variable.
    • \n
  • shape (list | tuple): A sequence with the size of each dimension of the integer variable. Defaults to an empty tuple (a scalar value).\n
      \n
    • Each item in shape must be a valid expression evaluating to a non-negative scalar.
      • \n
    • \n
  • lower_bound: The lower bound of the variable.
    • \n
  • upper_bound: The upper bound of the variable.
    • \n
  • latex (str, optional): A LaTeX-name of the integer variable to be represented in Jupyter notebook.\n
      \n
    • It is set to name by default.
      • \n
    • \n
  • description (str, optional): A description of the integer variable.
    • \n
\n\n

Raises

\n\n

ModelingError: Raises if a bound is a Placeholder or Subscript object whose ndim\nis neither 0 nor the same value as ndim of the integer variable.

\n\n

Examples

\n\n

Create a scalar integer variable whose name is \"z\" and domain is [-1, 1].

\n\n
\n
>>> import jijmodeling as jm\n>>> z = jm.IntegerVar("z", lower_bound=-1, upper_bound=1)\n
\n
\n\n

Create a 2-dimensional integer variable...

\n\n
    \n
  • whose name is \"x\".
    • \n
  • whose domain is [0, 2].
    • \n
  • where each dimension has length 2 (making this a 2x2 matrix).
    • \n
\n\n
\n
>>> import jijmodeling as jm\n>>> x = jm.IntegerVar("x", shape=[2, 2], lower_bound=0, upper_bound=2)\n
\n
\n\n

Create a 1-dimensional integer variable with the index of 123.

\n\n
\n
>>> import jijmodeling as jm\n>>> x = jm.IntegerVar("x", shape=[124], lower_bound=0, upper_bound=2)\n>>> x[123]\nIntegerVar(name='x', shape=[NumberLit(value=124)], lower_bound=NumberLit(value=0), upper_bound=NumberLit(value=2))[NumberLit(value=123)]\n
\n
\n"}, "jijmodeling.IntegerVar.set_latex": {"fullname": "jijmodeling.IntegerVar.set_latex", "modulename": "jijmodeling", "qualname": "IntegerVar.set_latex", "kind": "function", "doc": "

Set the LaTeX representation of the object.\nIf the LaTeX representation is not set, the default representation is set.

\n\n

Args

\n\n

latex (str, optional): LaTeX representation of the object. Defaults to None.

\n", "signature": "(self, /, latex=None):", "funcdef": "def"}, "jijmodeling.IntegerVar.name": {"fullname": "jijmodeling.IntegerVar.name", "modulename": "jijmodeling", "qualname": "IntegerVar.name", "kind": "variable", "doc": "

\n"}, "jijmodeling.IntegerVar.ndim": {"fullname": "jijmodeling.IntegerVar.ndim", "modulename": "jijmodeling", "qualname": "IntegerVar.ndim", "kind": "variable", "doc": "

\n"}, "jijmodeling.IntegerVar.upper_bound": {"fullname": "jijmodeling.IntegerVar.upper_bound", "modulename": "jijmodeling", "qualname": "IntegerVar.upper_bound", "kind": "variable", "doc": "

\n"}, "jijmodeling.IntegerVar.shape": {"fullname": "jijmodeling.IntegerVar.shape", "modulename": "jijmodeling", "qualname": "IntegerVar.shape", "kind": "variable", "doc": "

\n"}, "jijmodeling.IntegerVar.lower_bound": {"fullname": "jijmodeling.IntegerVar.lower_bound", "modulename": "jijmodeling", "qualname": "IntegerVar.lower_bound", "kind": "variable", "doc": "

\n"}, "jijmodeling.IntegerVar.description": {"fullname": "jijmodeling.IntegerVar.description", "modulename": "jijmodeling", "qualname": "IntegerVar.description", "kind": "variable", "doc": "

\n"}, "jijmodeling.ContinuousVar": {"fullname": "jijmodeling.ContinuousVar", "modulename": "jijmodeling", "qualname": "ContinuousVar", "kind": "class", "doc": "

A class for creating a continuous variable

\n\n

The ContinuousVar class is used to create a continuous variable.\nThe lower and upper bounds of the variable can be specified by:

\n\n
    \n
  • an integer value
    • \n
  • a float value
    • \n
  • a scalar expression that does not contains any decision variable
    • \n
  • a Placeholder object whose dimensionality is equal to that of this variable.
    • \n
  • a subscripted variable whose dimensionality is equal to that of this variable.
    • \n
\n\n

The index operator ([]) of a variable with ndim >= 1 returns a Subscript object.

\n\n

Attributes

\n\n
    \n
  • name (str): A name of the continuous variable.
    • \n
  • shape (tuple): A tuple with the size of each dimension of the integer variable. Empty if the variable is not multi-dimensional.
    • \n
  • lower_bound: The lower bound of the variable.
    • \n
  • upper_bound: The upper bound of the variable.
    • \n
  • description (str): A description of the continuous variable.
    • \n
\n\n

Args

\n\n
    \n
  • name (str): A name of the continuous variable.
    • \n
  • shape (list | tuple): A sequence with the size of each dimension of the continuous variable. Defaults to an empty tuple (a scalar value).\n
      \n
    • Each item in shape must be a valid expression evaluating to a non-negative scalar.
      • \n
    • \n
  • lower_bound: The lower bound of the variable.
    • \n
  • upper_bound: The upper bound of the variable.
    • \n
  • latex (str, optional): A LaTeX-name of the continuous variable to be represented in Jupyter notebook.\n
      \n
    • It is set to name by default.
      • \n
    • \n
  • description (str, optional): A description of the continuous variable.
    • \n
\n\n

Raises

\n\n

ModelingError: Raises if a bound is a Placeholder or Subscript object whose ndim is neither 0 nor the same value as ndim of the continuous variable.

\n\n

Examples

\n\n

Create a scalar continuous variable whose name is \"z\" and domain is [-1, 1].

\n\n
\n
>>> import jijmodeling as jm\n>>> z = jm.ContinuousVar("z", lower_bound=-1, upper_bound=1)\n
\n
\n\n

Create a 2-dimensional continuous variable...

\n\n
    \n
  • whose name is \"x\".
    • \n
  • whose domain is [0, 2].
    • \n
  • where each dimension has length 2 (making this a 2x2 matrix).
    • \n
\n\n
\n
>>> import jijmodeling as jm\n>>> x = jm.ContinuousVar("x", shape=[2, 2], lower_bound=0, upper_bound=2)\n
\n
\n\n

Create a 1-dimensional continuous variable with the index of 123.

\n\n
\n
>>> import jijmodeling as jm\n>>> x = jm.ContinuousVar("x", shape=[124], lower_bound=0, upper_bound=2)\n>>> x[123]\nContinuousVar(name='x', shape=[NumberLit(value=124)], lower_bound=NumberLit(value=0), upper_bound=NumberLit(value=2))[NumberLit(value=123)]\n
\n
\n"}, "jijmodeling.ContinuousVar.set_latex": {"fullname": "jijmodeling.ContinuousVar.set_latex", "modulename": "jijmodeling", "qualname": "ContinuousVar.set_latex", "kind": "function", "doc": "

Set the LaTeX representation of the object.\nIf the LaTeX representation is not set, the default representation is set.

\n\n

Args

\n\n

latex (str, optional): LaTeX representation of the object. Defaults to None.

\n", "signature": "(self, /, latex=None):", "funcdef": "def"}, "jijmodeling.ContinuousVar.description": {"fullname": "jijmodeling.ContinuousVar.description", "modulename": "jijmodeling", "qualname": "ContinuousVar.description", "kind": "variable", "doc": "

\n"}, "jijmodeling.ContinuousVar.ndim": {"fullname": "jijmodeling.ContinuousVar.ndim", "modulename": "jijmodeling", "qualname": "ContinuousVar.ndim", "kind": "variable", "doc": "

\n"}, "jijmodeling.ContinuousVar.upper_bound": {"fullname": "jijmodeling.ContinuousVar.upper_bound", "modulename": "jijmodeling", "qualname": "ContinuousVar.upper_bound", "kind": "variable", "doc": "

\n"}, "jijmodeling.ContinuousVar.shape": {"fullname": "jijmodeling.ContinuousVar.shape", "modulename": "jijmodeling", "qualname": "ContinuousVar.shape", "kind": "variable", "doc": "

\n"}, "jijmodeling.ContinuousVar.name": {"fullname": "jijmodeling.ContinuousVar.name", "modulename": "jijmodeling", "qualname": "ContinuousVar.name", "kind": "variable", "doc": "

\n"}, "jijmodeling.ContinuousVar.lower_bound": {"fullname": "jijmodeling.ContinuousVar.lower_bound", "modulename": "jijmodeling", "qualname": "ContinuousVar.lower_bound", "kind": "variable", "doc": "

\n"}, "jijmodeling.SemiIntegerVar": {"fullname": "jijmodeling.SemiIntegerVar", "modulename": "jijmodeling", "qualname": "SemiIntegerVar", "kind": "class", "doc": "

A class for creating a semi-integer variable

\n\n

The SemiIntegerVar class is used to create a semi-integer variable.\nThe lower and upper bounds of the variable can be specified by:

\n\n
    \n
  • an integer value
    • \n
  • a float value
    • \n
  • a scalar expression that does not contains any decision variable
    • \n
  • a Placeholder object whose dimensionality is equal to that of this variable.
    • \n
  • a subscripted variable whose dimensionality is equal to that of this variable.
    • \n
\n\n

The index operator ([]) of a semi-integer variable with ndim >= 1 returns a Subscript object.

\n\n

Attributes

\n\n
    \n
  • name (str): A name of the semi-integer variable.
    • \n
  • shape (tuple): A tuple with the size of each dimension of the integer variable. Empty if the variable is not multi-dimensional.
    • \n
  • lower_bound: The lower bound of the variable.
    • \n
  • upper_bound: The upper bound of the variable.
    • \n
  • description (str): A description of the semi-integer variable.
    • \n
\n\n

Args

\n\n
    \n
  • name (str): A name of the semi-integer variable.
    • \n
  • shape (list | tuple): A sequence with the size of each dimension of the integer variable. Defaults to an empty tuple (a scalar value).\n
      \n
    • Each item in shape must be a valid expression evaluating to a non-negative scalar.
      • \n
    • \n
  • lower_bound: The lower bound of the variable.
    • \n
  • upper_bound: The upper bound of the variable.
    • \n
  • latex (str, optional): A LaTeX-name of the semi-integer variable to be represented in Jupyter notebook.\n
      \n
    • It is set to name by default.
      • \n
    • \n
  • description (str, optional): A description of the semi-integer variable.
    • \n
\n\n

Raises

\n\n

ModelingError: Raises if a bound is a Placeholder or Subscript object whose ndim is neither 0\nnor the same value as ndim of the semi-integer variable.

\n\n

Examples

\n\n

Create a scalar semi-integer variable whose name is \"z\" and domain is [-1, 1].

\n\n
\n
>>> import jijmodeling as jm\n>>> z = jm.SemiIntegerVar("z", lower_bound=-1, upper_bound=1)\n
\n
\n\n

Create a 2-dimensional semi-integer variable...

\n\n
    \n
  • whose name is \"x\".
    • \n
  • whose domain is [0, 2].
    • \n
  • where each dimension has length 2 (making this a 2x2 matrix).
    • \n
\n\n
\n
>>> import jijmodeling as jm\n>>> x = jm.SemiIntegerVar("x", shape=[2, 2], lower_bound=0, upper_bound=2)\n
\n
\n\n

Create a 1-dimensional semi-integer variable with the index of 123.

\n\n
\n
>>> import jijmodeling as jm\n>>> x = jm.SemiIntegerVar("x", shape=[124], lower_bound=0, upper_bound=2)\n>>> x[123]\nSemiIntegerVar(name='x', shape=[NumberLit(value=124)], lower_bound=NumberLit(value=0), upper_bound=NumberLit(value=2))[NumberLit(value=123)]\n
\n
\n"}, "jijmodeling.SemiIntegerVar.set_latex": {"fullname": "jijmodeling.SemiIntegerVar.set_latex", "modulename": "jijmodeling", "qualname": "SemiIntegerVar.set_latex", "kind": "function", "doc": "

Set the LaTeX representation of the object.\nIf the LaTeX representation is not set, the default representation is set.

\n\n

Args

\n\n

latex (str, optional): LaTeX representation of the object. Defaults to None.

\n", "signature": "(self, /, latex=None):", "funcdef": "def"}, "jijmodeling.SemiIntegerVar.lower_bound": {"fullname": "jijmodeling.SemiIntegerVar.lower_bound", "modulename": "jijmodeling", "qualname": "SemiIntegerVar.lower_bound", "kind": "variable", "doc": "

\n"}, "jijmodeling.SemiIntegerVar.shape": {"fullname": "jijmodeling.SemiIntegerVar.shape", "modulename": "jijmodeling", "qualname": "SemiIntegerVar.shape", "kind": "variable", "doc": "

\n"}, "jijmodeling.SemiIntegerVar.upper_bound": {"fullname": "jijmodeling.SemiIntegerVar.upper_bound", "modulename": "jijmodeling", "qualname": "SemiIntegerVar.upper_bound", "kind": "variable", "doc": "

\n"}, "jijmodeling.SemiIntegerVar.description": {"fullname": "jijmodeling.SemiIntegerVar.description", "modulename": "jijmodeling", "qualname": "SemiIntegerVar.description", "kind": "variable", "doc": "

\n"}, "jijmodeling.SemiIntegerVar.ndim": {"fullname": "jijmodeling.SemiIntegerVar.ndim", "modulename": "jijmodeling", "qualname": "SemiIntegerVar.ndim", "kind": "variable", "doc": "

\n"}, "jijmodeling.SemiIntegerVar.name": {"fullname": "jijmodeling.SemiIntegerVar.name", "modulename": "jijmodeling", "qualname": "SemiIntegerVar.name", "kind": "variable", "doc": "

\n"}, "jijmodeling.SemiContinuousVar": {"fullname": "jijmodeling.SemiContinuousVar", "modulename": "jijmodeling", "qualname": "SemiContinuousVar", "kind": "class", "doc": "

A class for creating a semi-continuous variable

\n\n

The SemiContinuousVar class is used to create a semi-continuous variable.\nEither the lower bound or the upper bound is set by the following object:

\n\n
    \n
  • an integer value
    • \n
  • a float value
    • \n
  • a scalar expression that does not contains any decision variable
    • \n
  • a Placeholder object whose dimensionality is equal to that of this variable.
    • \n
  • a subscripted variable whose dimensionality is equal to that of this variable.
    • \n
\n\n

The index operator ([]) of a semi-continuous variable with ndim >= 1 returns a Subscript object.

\n\n

Attributes

\n\n
    \n
  • name (str): A name of the semi-continuous variable.
    • \n
  • shape (tuple): A tuple with the size of each dimension of the semi-continuous variable. Empty if the variable is not multi-dimensional.
    • \n
  • lower_bound: The lower bound of the variable.
    • \n
  • upper_bound: The upper bound of the variable.
    • \n
  • description (str): A description of the semi-continuous variable.
    • \n
\n\n

Args

\n\n
    \n
  • name (str): A name of the semi-continuous variable.
    • \n
  • shape (list | tuple): A sequence with the size of each dimension of the binary variable. Defaults to an empty tuple (a scalar value).\n
      \n
    • Each item in shape must be a valid expression evaluating to a non-negative scalar.
      • \n
    • \n
  • lower_bound: The lower bound of the variable.
    • \n
  • upper_bound: The upper bound of the variable.
    • \n
  • latex (str, optional): A LaTeX-name of the semi-continuous variable to be represented in Jupyter notebook.\n
      \n
    • It is set to name by default.
      • \n
    • \n
  • description (str, optional): A description of the semi-continuous variable.
    • \n
\n\n

Raises

\n\n

ModelingError: Raises if a bound is a Placeholder or Subscript object whose ndim\nis neither 0 nor the same value as ndim of the semi-continuous variable.

\n\n

Examples

\n\n

Create a scalar semi-continuous variable whose name is \"z\" and domain is [-1, 1].

\n\n
\n
>>> import jijmodeling as jm\n>>> z = jm.SemiContinuousVar("z", lower_bound=-1, upper_bound=1)\n
\n
\n\n

Create a 2-dimensional semi-continuous variable...

\n\n
    \n
  • whose name is \"x\".
    • \n
  • whose domain is [0, 2].
    • \n
  • where each dimension has length 2 (making this a 2x2 matrix).
    • \n
\n\n
\n
>>> import jijmodeling as jm\n>>> x = jm.SemiContinuousVar("x", shape=[2, 2], lower_bound=0, upper_bound=2)\n
\n
\n\n

Create a 1-dimensional semi-continuous variable with the index of 123.

\n\n
\n
>>> import jijmodeling as jm\n>>> x = jm.SemiContinuousVar("x", shape=[124], lower_bound=0, upper_bound=2)\n>>> x[123]\nSemiContinuousVar(name='x', shape=[NumberLit(value=124)], lower_bound=NumberLit(value=0), upper_bound=NumberLit(value=2))[NumberLit(value=123)]\n
\n
\n"}, "jijmodeling.SemiContinuousVar.set_latex": {"fullname": "jijmodeling.SemiContinuousVar.set_latex", "modulename": "jijmodeling", "qualname": "SemiContinuousVar.set_latex", "kind": "function", "doc": "

Set the LaTeX representation of the object.\nIf the LaTeX representation is not set, the default representation is set.

\n\n

Args

\n\n

latex (str, optional): LaTeX representation of the object. Defaults to None.

\n", "signature": "(self, /, latex=None):", "funcdef": "def"}, "jijmodeling.SemiContinuousVar.lower_bound": {"fullname": "jijmodeling.SemiContinuousVar.lower_bound", "modulename": "jijmodeling", "qualname": "SemiContinuousVar.lower_bound", "kind": "variable", "doc": "

\n"}, "jijmodeling.SemiContinuousVar.upper_bound": {"fullname": "jijmodeling.SemiContinuousVar.upper_bound", "modulename": "jijmodeling", "qualname": "SemiContinuousVar.upper_bound", "kind": "variable", "doc": "

\n"}, "jijmodeling.SemiContinuousVar.description": {"fullname": "jijmodeling.SemiContinuousVar.description", "modulename": "jijmodeling", "qualname": "SemiContinuousVar.description", "kind": "variable", "doc": "

\n"}, "jijmodeling.SemiContinuousVar.ndim": {"fullname": "jijmodeling.SemiContinuousVar.ndim", "modulename": "jijmodeling", "qualname": "SemiContinuousVar.ndim", "kind": "variable", "doc": "

\n"}, "jijmodeling.SemiContinuousVar.name": {"fullname": "jijmodeling.SemiContinuousVar.name", "modulename": "jijmodeling", "qualname": "SemiContinuousVar.name", "kind": "variable", "doc": "

\n"}, "jijmodeling.SemiContinuousVar.shape": {"fullname": "jijmodeling.SemiContinuousVar.shape", "modulename": "jijmodeling", "qualname": "SemiContinuousVar.shape", "kind": "variable", "doc": "

\n"}, "jijmodeling.AbsOp": {"fullname": "jijmodeling.AbsOp", "modulename": "jijmodeling", "qualname": "AbsOp", "kind": "class", "doc": "

A class for representing the absolute value

\n\n

The AbsOp class is used to represent the absolute value.\nThe number of dimensions of the operand is zero.

\n\n

Attributes

\n\n
    \n
  • operand: The operand.
    • \n
\n\n

Note

\n\n

The AbsOp class does not have a constructor.

\n"}, "jijmodeling.AbsOp.set_latex": {"fullname": "jijmodeling.AbsOp.set_latex", "modulename": "jijmodeling", "qualname": "AbsOp.set_latex", "kind": "function", "doc": "

Set the LaTeX representation of the object.\nIf the LaTeX representation is not set, the default representation is set.

\n\n

Args

\n\n

latex (str, optional): LaTeX representation of the object. Defaults to None.

\n", "signature": "(self, /, latex=None):", "funcdef": "def"}, "jijmodeling.AbsOp.operand": {"fullname": "jijmodeling.AbsOp.operand", "modulename": "jijmodeling", "qualname": "AbsOp.operand", "kind": "variable", "doc": "

\n"}, "jijmodeling.CeilOp": {"fullname": "jijmodeling.CeilOp", "modulename": "jijmodeling", "qualname": "CeilOp", "kind": "class", "doc": "

A class for representing the ceil operator

\n\n

The CeilOp class is used to represent the ceil operator.\nThe number of dimensions of the operand is zero.

\n\n

Attributes

\n\n
    \n
  • operand: The operand.
    • \n
\n\n

Note

\n\n

The CeilOp class does not have a constructor.

\n"}, "jijmodeling.CeilOp.set_latex": {"fullname": "jijmodeling.CeilOp.set_latex", "modulename": "jijmodeling", "qualname": "CeilOp.set_latex", "kind": "function", "doc": "

Set the LaTeX representation of the object.\nIf the LaTeX representation is not set, the default representation is set.

\n\n

Args

\n\n

latex (str, optional): LaTeX representation of the object. Defaults to None.

\n", "signature": "(self, /, latex=None):", "funcdef": "def"}, "jijmodeling.CeilOp.operand": {"fullname": "jijmodeling.CeilOp.operand", "modulename": "jijmodeling", "qualname": "CeilOp.operand", "kind": "variable", "doc": "

\n"}, "jijmodeling.FloorOp": {"fullname": "jijmodeling.FloorOp", "modulename": "jijmodeling", "qualname": "FloorOp", "kind": "class", "doc": "

A class for representing the floor operator

\n\n

The FloorOp class is used to represent the floor operator.\nThe number of dimensions of the operand is zero.

\n\n

Attributes

\n\n
    \n
  • operand: The operand.
    • \n
\n\n

Note

\n\n

The FloorOp class does not have a constructor.

\n"}, "jijmodeling.FloorOp.set_latex": {"fullname": "jijmodeling.FloorOp.set_latex", "modulename": "jijmodeling", "qualname": "FloorOp.set_latex", "kind": "function", "doc": "

Set the LaTeX representation of the object.\nIf the LaTeX representation is not set, the default representation is set.

\n\n

Args

\n\n

latex (str, optional): LaTeX representation of the object. Defaults to None.

\n", "signature": "(self, /, latex=None):", "funcdef": "def"}, "jijmodeling.FloorOp.operand": {"fullname": "jijmodeling.FloorOp.operand", "modulename": "jijmodeling", "qualname": "FloorOp.operand", "kind": "variable", "doc": "

\n"}, "jijmodeling.Log2Op": {"fullname": "jijmodeling.Log2Op", "modulename": "jijmodeling", "qualname": "Log2Op", "kind": "class", "doc": "

A class for representing the base 2 logarithm

\n\n

The Log2Op class is used to represent the base 2 logarithm.\nThe number of dimensions of the operand is zero.

\n\n

Attributes

\n\n

operand: The operand.

\n\n

Note

\n\n

The Log2Op class does not have a constructor.

\n"}, "jijmodeling.Log2Op.set_latex": {"fullname": "jijmodeling.Log2Op.set_latex", "modulename": "jijmodeling", "qualname": "Log2Op.set_latex", "kind": "function", "doc": "

Set the LaTeX representation of the object.\nIf the LaTeX representation is not set, the default representation is set.

\n\n

Args

\n\n

latex (str, optional): LaTeX representation of the object. Defaults to None.

\n", "signature": "(self, /, latex=None):", "funcdef": "def"}, "jijmodeling.Log2Op.operand": {"fullname": "jijmodeling.Log2Op.operand", "modulename": "jijmodeling", "qualname": "Log2Op.operand", "kind": "variable", "doc": "

\n"}, "jijmodeling.Log10Op": {"fullname": "jijmodeling.Log10Op", "modulename": "jijmodeling", "qualname": "Log10Op", "kind": "class", "doc": "

A class for representing the base 10 logarithm

\n\n

The Log10Op class is used to represent the base 10 logarithm.\nThe number of dimensions of the operand is zero.

\n\n

Attributes

\n\n
    \n
  • operand: The operand.
    • \n
\n\n

Note

\n\n

The Log10Op class does not have a constructor.

\n"}, "jijmodeling.Log10Op.set_latex": {"fullname": "jijmodeling.Log10Op.set_latex", "modulename": "jijmodeling", "qualname": "Log10Op.set_latex", "kind": "function", "doc": "

Set the LaTeX representation of the object.\nIf the LaTeX representation is not set, the default representation is set.

\n\n

Args

\n\n

latex (str, optional): LaTeX representation of the object. Defaults to None.

\n", "signature": "(self, /, latex=None):", "funcdef": "def"}, "jijmodeling.Log10Op.operand": {"fullname": "jijmodeling.Log10Op.operand", "modulename": "jijmodeling", "qualname": "Log10Op.operand", "kind": "variable", "doc": "

\n"}, "jijmodeling.LnOp": {"fullname": "jijmodeling.LnOp", "modulename": "jijmodeling", "qualname": "LnOp", "kind": "class", "doc": "

A class for representing the natural logarithm

\n\n

The LnOp class is used to represent the natural logarithm.\nThe number of dimensions of the operand is zero.

\n\n

Attributes

\n\n

operand: The operand.

\n\n

Note

\n\n

The LnOp class does not have a constructor.

\n"}, "jijmodeling.LnOp.set_latex": {"fullname": "jijmodeling.LnOp.set_latex", "modulename": "jijmodeling", "qualname": "LnOp.set_latex", "kind": "function", "doc": "

Set the LaTeX representation of the object.\nIf the LaTeX representation is not set, the default representation is set.

\n\n

Args

\n\n

latex (str, optional): LaTeX representation of the object. Defaults to None.

\n", "signature": "(self, /, latex=None):", "funcdef": "def"}, "jijmodeling.LnOp.operand": {"fullname": "jijmodeling.LnOp.operand", "modulename": "jijmodeling", "qualname": "LnOp.operand", "kind": "variable", "doc": "

\n"}, "jijmodeling.AddOp": {"fullname": "jijmodeling.AddOp", "modulename": "jijmodeling", "qualname": "AddOp", "kind": "class", "doc": "

A class for representing addition

\n\n

The AddOp class is used to represent addition (+) of an arbitrary number of operands.\nFor example a + b + c + d would be one AddOp object.\nThe number of dimensions of each operand is zero.

\n\n

Attributes

\n\n

terms: A sequence of operands to be added.

\n\n

Note

\n\n

The AddOp class does not have a constructor. Its intended\ninstantiation method is by calling the addition operation on other\nexpressions.

\n"}, "jijmodeling.AddOp.set_latex": {"fullname": "jijmodeling.AddOp.set_latex", "modulename": "jijmodeling", "qualname": "AddOp.set_latex", "kind": "function", "doc": "

Set the LaTeX representation of the object.\nIf the LaTeX representation is not set, the default representation is set.

\n\n

Args

\n\n

latex (str, optional): LaTeX representation of the object. Defaults to None.

\n", "signature": "(self, /, latex=None):", "funcdef": "def"}, "jijmodeling.AddOp.terms": {"fullname": "jijmodeling.AddOp.terms", "modulename": "jijmodeling", "qualname": "AddOp.terms", "kind": "variable", "doc": "

\n"}, "jijmodeling.MulOp": {"fullname": "jijmodeling.MulOp", "modulename": "jijmodeling", "qualname": "MulOp", "kind": "class", "doc": "

A class for representing multiplication

\n\n

The MulOp class is used to represent multiplication (*) of an arbitrary number of operands.\nFor example a * b * c * d would be one AddOp object.\nThe number of dimensions of each operand is zero.

\n\n

Attributes

\n\n

terms: A sequence of operands to be multiplied.

\n\n

Note

\n\n

The MulOp class does not have a constructor. Its intended\ninstantiation method is by calling the multiplication operation on other\nexpressions.

\n"}, "jijmodeling.MulOp.set_latex": {"fullname": "jijmodeling.MulOp.set_latex", "modulename": "jijmodeling", "qualname": "MulOp.set_latex", "kind": "function", "doc": "

Set the LaTeX representation of the object.\nIf the LaTeX representation is not set, the default representation is set.

\n\n

Args

\n\n

latex (str, optional): LaTeX representation of the object. Defaults to None.

\n", "signature": "(self, /, latex=None):", "funcdef": "def"}, "jijmodeling.MulOp.terms": {"fullname": "jijmodeling.MulOp.terms", "modulename": "jijmodeling", "qualname": "MulOp.terms", "kind": "variable", "doc": "

\n"}, "jijmodeling.ModOp": {"fullname": "jijmodeling.ModOp", "modulename": "jijmodeling", "qualname": "ModOp", "kind": "class", "doc": "

A class for representing modulo

\n\n

The ModOp class is used to represent modulo (or remainder) (%).\nThe number of dimensions of each operand is zero.

\n\n

Attributes

\n\n
    \n
  • left: The left-hand operand.
    • \n
  • right: The right-hand operand.
    • \n
\n\n

Note

\n\n

The ModOp class does not have a constructor.

\n"}, "jijmodeling.ModOp.set_latex": {"fullname": "jijmodeling.ModOp.set_latex", "modulename": "jijmodeling", "qualname": "ModOp.set_latex", "kind": "function", "doc": "

Set the LaTeX representation of the object.\nIf the LaTeX representation is not set, the default representation is set.

\n\n

Args

\n\n

latex (str, optional): LaTeX representation of the object. Defaults to None.

\n", "signature": "(self, /, latex=None):", "funcdef": "def"}, "jijmodeling.ModOp.left": {"fullname": "jijmodeling.ModOp.left", "modulename": "jijmodeling", "qualname": "ModOp.left", "kind": "variable", "doc": "

\n"}, "jijmodeling.ModOp.right": {"fullname": "jijmodeling.ModOp.right", "modulename": "jijmodeling", "qualname": "ModOp.right", "kind": "variable", "doc": "

\n"}, "jijmodeling.MinOp": {"fullname": "jijmodeling.MinOp", "modulename": "jijmodeling", "qualname": "MinOp", "kind": "class", "doc": "

A class for representing the minimum value.

\n\n

The MinOp class is used to represent the minimum value of operands.\nThe number of dimensions of each operand is zero.

\n\n

Attributes

\n\n
    \n
  • terms: A sequence of operands.
    • \n
\n\n

Note

\n\n

The MinOp class does not have a constructor. Its intended\ninstantiation method is by calling the min function.

\n"}, "jijmodeling.MinOp.set_latex": {"fullname": "jijmodeling.MinOp.set_latex", "modulename": "jijmodeling", "qualname": "MinOp.set_latex", "kind": "function", "doc": "

Set the LaTeX representation of the object.\nIf the LaTeX representation is not set, the default representation is set.

\n\n

Args

\n\n

latex (str, optional): LaTeX representation of the object. Defaults to None.

\n", "signature": "(self, /, latex=None):", "funcdef": "def"}, "jijmodeling.MinOp.terms": {"fullname": "jijmodeling.MinOp.terms", "modulename": "jijmodeling", "qualname": "MinOp.terms", "kind": "variable", "doc": "

\n"}, "jijmodeling.MaxOp": {"fullname": "jijmodeling.MaxOp", "modulename": "jijmodeling", "qualname": "MaxOp", "kind": "class", "doc": "

A class for representing the maximum value.

\n\n

The MaxOp class is used to represent the minimum value of operands.\nThe number of dimensions of each operand is zero.

\n\n

Attributes

\n\n
    \n
  • terms: A sequence of operands.
    • \n
\n\n

Note

\n\n

The MaxOp class does not have a constructor. Its intended\ninstantiation method is by calling the max function.

\n"}, "jijmodeling.MaxOp.set_latex": {"fullname": "jijmodeling.MaxOp.set_latex", "modulename": "jijmodeling", "qualname": "MaxOp.set_latex", "kind": "function", "doc": "

Set the LaTeX representation of the object.\nIf the LaTeX representation is not set, the default representation is set.

\n\n

Args

\n\n

latex (str, optional): LaTeX representation of the object. Defaults to None.

\n", "signature": "(self, /, latex=None):", "funcdef": "def"}, "jijmodeling.MaxOp.terms": {"fullname": "jijmodeling.MaxOp.terms", "modulename": "jijmodeling", "qualname": "MaxOp.terms", "kind": "variable", "doc": "

\n"}, "jijmodeling.PowOp": {"fullname": "jijmodeling.PowOp", "modulename": "jijmodeling", "qualname": "PowOp", "kind": "class", "doc": "

A class for representing the power operator

\n\n

The ModOp class is used to represent the power operator(**).\nThe number of dimensions of each operand is zero.

\n\n

Attributes

\n\n
    \n
  • base: The base operand.
    • \n
  • exponent: The exponent operand.
    • \n
\n\n

Note

\n\n

The PowOp class does not have a constructor.

\n"}, "jijmodeling.PowOp.set_latex": {"fullname": "jijmodeling.PowOp.set_latex", "modulename": "jijmodeling", "qualname": "PowOp.set_latex", "kind": "function", "doc": "

Set the LaTeX representation of the object.\nIf the LaTeX representation is not set, the default representation is set.

\n\n

Args

\n\n

latex (str, optional): LaTeX representation of the object. Defaults to None.

\n", "signature": "(self, /, latex=None):", "funcdef": "def"}, "jijmodeling.PowOp.base": {"fullname": "jijmodeling.PowOp.base", "modulename": "jijmodeling", "qualname": "PowOp.base", "kind": "variable", "doc": "

\n"}, "jijmodeling.PowOp.exponent": {"fullname": "jijmodeling.PowOp.exponent", "modulename": "jijmodeling", "qualname": "PowOp.exponent", "kind": "variable", "doc": "

\n"}, "jijmodeling.SumOp": {"fullname": "jijmodeling.SumOp", "modulename": "jijmodeling", "qualname": "SumOp", "kind": "class", "doc": "

A class for representing summation

\n\n

The SumOp class is used to represent summation.\nThe number of dimensions of the opreand is zero.

\n\n

Attributes

\n\n
    \n
  • index: The index of summation.
    • \n
  • condition: The condition for the summation index.
    • \n
  • operand: The opreand.
    • \n
\n\n

Note

\n\n

The SumOp class does not have a constructor.

\n"}, "jijmodeling.SumOp.set_latex": {"fullname": "jijmodeling.SumOp.set_latex", "modulename": "jijmodeling", "qualname": "SumOp.set_latex", "kind": "function", "doc": "

Set the LaTeX representation of the object.\nIf the LaTeX representation is not set, the default representation is set.

\n\n

Args

\n\n

latex (str, optional): LaTeX representation of the object. Defaults to None.

\n", "signature": "(self, /, latex=None):", "funcdef": "def"}, "jijmodeling.SumOp.index": {"fullname": "jijmodeling.SumOp.index", "modulename": "jijmodeling", "qualname": "SumOp.index", "kind": "variable", "doc": "

\n"}, "jijmodeling.SumOp.operand": {"fullname": "jijmodeling.SumOp.operand", "modulename": "jijmodeling", "qualname": "SumOp.operand", "kind": "variable", "doc": "

\n"}, "jijmodeling.SumOp.condition": {"fullname": "jijmodeling.SumOp.condition", "modulename": "jijmodeling", "qualname": "SumOp.condition", "kind": "variable", "doc": "

\n"}, "jijmodeling.ProdOp": {"fullname": "jijmodeling.ProdOp", "modulename": "jijmodeling", "qualname": "ProdOp", "kind": "class", "doc": "

A class for representing product

\n\n

The ProdOp class is used to represent product.\nThe number of dimensions of the opreand is zero.

\n\n

Attributes

\n\n
    \n
  • index: The index of product.
    • \n
  • condition: The condition for the product index.
    • \n
  • operand: The opreand.
    • \n
\n\n

Note

\n\n

The ProdOp class does not have a constructor.

\n"}, "jijmodeling.ProdOp.set_latex": {"fullname": "jijmodeling.ProdOp.set_latex", "modulename": "jijmodeling", "qualname": "ProdOp.set_latex", "kind": "function", "doc": "

Set the LaTeX representation of the object.\nIf the LaTeX representation is not set, the default representation is set.

\n\n

Args

\n\n

latex (str, optional): LaTeX representation of the object. Defaults to None.

\n", "signature": "(self, /, latex=None):", "funcdef": "def"}, "jijmodeling.ProdOp.condition": {"fullname": "jijmodeling.ProdOp.condition", "modulename": "jijmodeling", "qualname": "ProdOp.condition", "kind": "variable", "doc": "

\n"}, "jijmodeling.ProdOp.index": {"fullname": "jijmodeling.ProdOp.index", "modulename": "jijmodeling", "qualname": "ProdOp.index", "kind": "variable", "doc": "

\n"}, "jijmodeling.ProdOp.operand": {"fullname": "jijmodeling.ProdOp.operand", "modulename": "jijmodeling", "qualname": "ProdOp.operand", "kind": "variable", "doc": "

\n"}, "jijmodeling.AndOp": {"fullname": "jijmodeling.AndOp", "modulename": "jijmodeling", "qualname": "AndOp", "kind": "class", "doc": "

A class for representing logical AND

\n\n

The AndOp class is used to represent logical AND (&) of an arbitrary number of operands.\nFor example a & b & c & d would be one AndOp object.\nThe number of dimensions of each operand is zero.

\n\n

Attributes

\n\n
    \n
  • terms: A sequence of operands to apply the AND operation.
    • \n
\n\n

Note

\n\n

The AndOp class does not have a constructor.

\n"}, "jijmodeling.AndOp.set_latex": {"fullname": "jijmodeling.AndOp.set_latex", "modulename": "jijmodeling", "qualname": "AndOp.set_latex", "kind": "function", "doc": "

Set the LaTeX representation of the object.\nIf the LaTeX representation is not set, the default representation is set.

\n\n

Args

\n\n

latex (str, optional): LaTeX representation of the object. Defaults to None.

\n", "signature": "(self, /, latex=None):", "funcdef": "def"}, "jijmodeling.AndOp.terms": {"fullname": "jijmodeling.AndOp.terms", "modulename": "jijmodeling", "qualname": "AndOp.terms", "kind": "variable", "doc": "

\n"}, "jijmodeling.OrOp": {"fullname": "jijmodeling.OrOp", "modulename": "jijmodeling", "qualname": "OrOp", "kind": "class", "doc": "

A class for representing logical OR

\n\n

The OrOp class is used to represent logical OR (|) of an arbitrary number of operands.\nFor example a | b | c | d would be one OrOp object.\nThe number of dimensions of each operand is zero.

\n\n

Attributes

\n\n
    \n
  • terms: A sequence of operands to apply the OR operation.
    • \n
\n\n

Note

\n\n

The OrOp class does not have a constructor.

\n"}, "jijmodeling.OrOp.set_latex": {"fullname": "jijmodeling.OrOp.set_latex", "modulename": "jijmodeling", "qualname": "OrOp.set_latex", "kind": "function", "doc": "

Set the LaTeX representation of the object.\nIf the LaTeX representation is not set, the default representation is set.

\n\n

Args

\n\n

latex (str, optional): LaTeX representation of the object. Defaults to None.

\n", "signature": "(self, /, latex=None):", "funcdef": "def"}, "jijmodeling.OrOp.terms": {"fullname": "jijmodeling.OrOp.terms", "modulename": "jijmodeling", "qualname": "OrOp.terms", "kind": "variable", "doc": "

\n"}, "jijmodeling.XorOp": {"fullname": "jijmodeling.XorOp", "modulename": "jijmodeling", "qualname": "XorOp", "kind": "class", "doc": "

A class for representing logical XOR

\n\n

The XorOp class is used to represent logical XOR (^) of an arbitrary number of operands.\nFor example a ^ b ^ c ^ d would be one XorOp object.\nThe number of dimensions of each operand is zero.

\n\n

Attributes

\n\n
    \n
  • terms-: A sequence of operands to apply the XOR operation.
    • \n
\n\n

Note

\n\n

The XorOp class does not have a constructor.

\n"}, "jijmodeling.XorOp.set_latex": {"fullname": "jijmodeling.XorOp.set_latex", "modulename": "jijmodeling", "qualname": "XorOp.set_latex", "kind": "function", "doc": "

Set the LaTeX representation of the object.\nIf the LaTeX representation is not set, the default representation is set.

\n\n

Args

\n\n

latex (str, optional): LaTeX representation of the object. Defaults to None.

\n", "signature": "(self, /, latex=None):", "funcdef": "def"}, "jijmodeling.XorOp.terms": {"fullname": "jijmodeling.XorOp.terms", "modulename": "jijmodeling", "qualname": "XorOp.terms", "kind": "variable", "doc": "

\n"}, "jijmodeling.EqualOp": {"fullname": "jijmodeling.EqualOp", "modulename": "jijmodeling", "qualname": "EqualOp", "kind": "class", "doc": "

A class for representing the equal operator

\n\n

The EqualOp class is used to represent the equal operator (==).\nThe number of dimensions of each operand is zero.

\n\n

Attributes

\n\n
    \n
  • left: The left-hand operand.
    • \n
  • right: The right-hand operand.
    • \n
\n\n

Note

\n\n

The EqualOp class does not have a constructor.

\n"}, "jijmodeling.EqualOp.set_latex": {"fullname": "jijmodeling.EqualOp.set_latex", "modulename": "jijmodeling", "qualname": "EqualOp.set_latex", "kind": "function", "doc": "

Set the LaTeX representation of the object.\nIf the LaTeX representation is not set, the default representation is set.

\n\n

Args

\n\n

latex (str, optional): LaTeX representation of the object. Defaults to None.

\n", "signature": "(self, /, latex=None):", "funcdef": "def"}, "jijmodeling.EqualOp.right": {"fullname": "jijmodeling.EqualOp.right", "modulename": "jijmodeling", "qualname": "EqualOp.right", "kind": "variable", "doc": "

\n"}, "jijmodeling.EqualOp.left": {"fullname": "jijmodeling.EqualOp.left", "modulename": "jijmodeling", "qualname": "EqualOp.left", "kind": "variable", "doc": "

\n"}, "jijmodeling.NotEqualOp": {"fullname": "jijmodeling.NotEqualOp", "modulename": "jijmodeling", "qualname": "NotEqualOp", "kind": "class", "doc": "

A class for representing the not equal operator

\n\n

The NotEqualOp class is used to represent the not equal operator (!=).\nThe number of dimensions of each operand is zero.

\n\n

Attributes

\n\n
    \n
  • left: The left-hand operand.
    • \n
  • right: The right-hand operand.
    • \n
\n\n

Note

\n\n

The NotEqualOp class does not have a constructor.

\n"}, "jijmodeling.NotEqualOp.set_latex": {"fullname": "jijmodeling.NotEqualOp.set_latex", "modulename": "jijmodeling", "qualname": "NotEqualOp.set_latex", "kind": "function", "doc": "

Set the LaTeX representation of the object.\nIf the LaTeX representation is not set, the default representation is set.

\n\n

Args

\n\n

latex (str, optional): LaTeX representation of the object. Defaults to None.

\n", "signature": "(self, /, latex=None):", "funcdef": "def"}, "jijmodeling.NotEqualOp.right": {"fullname": "jijmodeling.NotEqualOp.right", "modulename": "jijmodeling", "qualname": "NotEqualOp.right", "kind": "variable", "doc": "

\n"}, "jijmodeling.NotEqualOp.left": {"fullname": "jijmodeling.NotEqualOp.left", "modulename": "jijmodeling", "qualname": "NotEqualOp.left", "kind": "variable", "doc": "

\n"}, "jijmodeling.LessThanOp": {"fullname": "jijmodeling.LessThanOp", "modulename": "jijmodeling", "qualname": "LessThanOp", "kind": "class", "doc": "

A class for representing the less than operator

\n\n

The LessThanOp class is used to represent the less than operator (<).\nThe number of dimensions of each operand is zero.

\n\n

Attributes

\n\n
    \n
  • left: The left-hand operand.
    • \n
  • right: The right-hand operand.
    • \n
\n\n

Note

\n\n

The LessThanOp class does not have a constructor.

\n"}, "jijmodeling.LessThanOp.set_latex": {"fullname": "jijmodeling.LessThanOp.set_latex", "modulename": "jijmodeling", "qualname": "LessThanOp.set_latex", "kind": "function", "doc": "

Set the LaTeX representation of the object.\nIf the LaTeX representation is not set, the default representation is set.

\n\n

Args

\n\n

latex (str, optional): LaTeX representation of the object. Defaults to None.

\n", "signature": "(self, /, latex=None):", "funcdef": "def"}, "jijmodeling.LessThanOp.left": {"fullname": "jijmodeling.LessThanOp.left", "modulename": "jijmodeling", "qualname": "LessThanOp.left", "kind": "variable", "doc": "

\n"}, "jijmodeling.LessThanOp.right": {"fullname": "jijmodeling.LessThanOp.right", "modulename": "jijmodeling", "qualname": "LessThanOp.right", "kind": "variable", "doc": "

\n"}, "jijmodeling.LessThanEqualOp": {"fullname": "jijmodeling.LessThanEqualOp", "modulename": "jijmodeling", "qualname": "LessThanEqualOp", "kind": "class", "doc": "

A class for representing the less than equal operator

\n\n

The LessThanEqualOp class is used to represent the less than equal operator (<=).\nThe number of dimensions of each operand is zero.

\n\n

Attributes

\n\n
    \n
  • left: The left-hand operand.
    • \n
  • right: The right-hand operand.
    • \n
\n\n

Note

\n\n

The LessThanEqualOp class does not have a constructor.

\n"}, "jijmodeling.LessThanEqualOp.set_latex": {"fullname": "jijmodeling.LessThanEqualOp.set_latex", "modulename": "jijmodeling", "qualname": "LessThanEqualOp.set_latex", "kind": "function", "doc": "

Set the LaTeX representation of the object.\nIf the LaTeX representation is not set, the default representation is set.

\n\n

Args

\n\n

latex (str, optional): LaTeX representation of the object. Defaults to None.

\n", "signature": "(self, /, latex=None):", "funcdef": "def"}, "jijmodeling.LessThanEqualOp.left": {"fullname": "jijmodeling.LessThanEqualOp.left", "modulename": "jijmodeling", "qualname": "LessThanEqualOp.left", "kind": "variable", "doc": "

\n"}, "jijmodeling.LessThanEqualOp.right": {"fullname": "jijmodeling.LessThanEqualOp.right", "modulename": "jijmodeling", "qualname": "LessThanEqualOp.right", "kind": "variable", "doc": "

\n"}, "jijmodeling.GreaterThanOp": {"fullname": "jijmodeling.GreaterThanOp", "modulename": "jijmodeling", "qualname": "GreaterThanOp", "kind": "class", "doc": "

A class for representing the greater than operator

\n\n

The GreaterThanOp class is used to represent the greater than operator (>).\nThe number of dimensions of each operand is zero.

\n\n

Attributes

\n\n
    \n
  • left: The left-hand operand.
    • \n
  • right: The right-hand operand.
    • \n
\n\n

Note

\n\n

The GreaterThanOp class does not have a constructor.

\n"}, "jijmodeling.GreaterThanOp.set_latex": {"fullname": "jijmodeling.GreaterThanOp.set_latex", "modulename": "jijmodeling", "qualname": "GreaterThanOp.set_latex", "kind": "function", "doc": "

Set the LaTeX representation of the object.\nIf the LaTeX representation is not set, the default representation is set.

\n\n

Args

\n\n

latex (str, optional): LaTeX representation of the object. Defaults to None.

\n", "signature": "(self, /, latex=None):", "funcdef": "def"}, "jijmodeling.GreaterThanOp.left": {"fullname": "jijmodeling.GreaterThanOp.left", "modulename": "jijmodeling", "qualname": "GreaterThanOp.left", "kind": "variable", "doc": "

\n"}, "jijmodeling.GreaterThanOp.right": {"fullname": "jijmodeling.GreaterThanOp.right", "modulename": "jijmodeling", "qualname": "GreaterThanOp.right", "kind": "variable", "doc": "

\n"}, "jijmodeling.GreaterThanEqualOp": {"fullname": "jijmodeling.GreaterThanEqualOp", "modulename": "jijmodeling", "qualname": "GreaterThanEqualOp", "kind": "class", "doc": "

A class for representing the greater than equal operator

\n\n

The GreaterThanEqualOp class is used to represent the greater than equal operator (>=).\nThe number of dimensions of each operand is zero.

\n\n

Attributes

\n\n
    \n
  • left: The left-hand operand.
    • \n
  • right: The right-hand operand.
    • \n
\n\n

Note

\n\n

The GreaterThanEqualOp class does not have a constructor.

\n"}, "jijmodeling.GreaterThanEqualOp.set_latex": {"fullname": "jijmodeling.GreaterThanEqualOp.set_latex", "modulename": "jijmodeling", "qualname": "GreaterThanEqualOp.set_latex", "kind": "function", "doc": "

Set the LaTeX representation of the object.\nIf the LaTeX representation is not set, the default representation is set.

\n\n

Args

\n\n

latex (str, optional): LaTeX representation of the object. Defaults to None.

\n", "signature": "(self, /, latex=None):", "funcdef": "def"}, "jijmodeling.GreaterThanEqualOp.left": {"fullname": "jijmodeling.GreaterThanEqualOp.left", "modulename": "jijmodeling", "qualname": "GreaterThanEqualOp.left", "kind": "variable", "doc": "

\n"}, "jijmodeling.GreaterThanEqualOp.right": {"fullname": "jijmodeling.GreaterThanEqualOp.right", "modulename": "jijmodeling", "qualname": "GreaterThanEqualOp.right", "kind": "variable", "doc": "

\n"}, "jijmodeling.Problem": {"fullname": "jijmodeling.Problem", "modulename": "jijmodeling", "qualname": "Problem", "kind": "class", "doc": "

A class for creating an optimization problem

\n\n

The Problem class is used to create an optimization problem.

\n\n

Attributes

\n\n
    \n
  • name (str): A name of the optimization problem.
    • \n
  • sense: Sense of the optimization problem.
    • \n
  • objective: The objective function of the optimization problem.
    • \n
  • constraints (dict): A dictionary that stores constraints.\n
      \n
    • A key is the name of a constraint and the value is the constraint object.
      • \n
    • \n
  • custom_penalty_terms (dict): A dictionary that stores custom penalty terms.\n
      \n
    • A key is the name of a custom penalty and the value is the custom penalty object.
      • \n
    • \n
\n\n

Args

\n\n
    \n
  • name (str): A name of the optimization problem.
    • \n
  • sense (optional): Sense of the optimization problem. Defaults to ProblemSense.MINIMIZE.
    • \n
\n"}, "jijmodeling.Problem.used_placeholders": {"fullname": "jijmodeling.Problem.used_placeholders", "modulename": "jijmodeling", "qualname": "Problem.used_placeholders", "kind": "function", "doc": "

\n", "signature": "(self, /):", "funcdef": "def"}, "jijmodeling.Problem.get_problem_schema": {"fullname": "jijmodeling.Problem.get_problem_schema", "modulename": "jijmodeling", "qualname": "Problem.get_problem_schema", "kind": "function", "doc": "

Returns the schema of the problem.

\n\n

Returns

\n\n
    \n
  • schema: The dictionary containing the schema of the problem.
    • \n
\n", "signature": "(self, /):", "funcdef": "def"}, "jijmodeling.Problem.generate_random_dataset": {"fullname": "jijmodeling.Problem.generate_random_dataset", "modulename": "jijmodeling", "qualname": "Problem.generate_random_dataset", "kind": "function", "doc": "

Generates a dictionary of random InstanceDataValue for a given problem.\nTo generate ommx.v1.Instance object directly, use InstanceDataValue.generate_random_instance instead.

\n\n

Args

\n\n
    \n
  • options (optional): a dictionary of range parameters for each separate placeholders. The key must be the name of the placeholder and the value must be range parameter (as described in \"Range Parameters and Range Syntax\" below).
    • \n
  • default (optional): default range parameters for placeholders which is not specified in options.
    • \n
  • seed (optional): seed for random number generation.
    • \n
\n\n

Returns

\n\n

dict: The dictionary from the name of placeholders to the generated InstanceDataValue objects. To be fed to Interpreter.eval_problem.

\n\n

Range Parameters and Range Syntax

\n\n

A range parameter is a dictionary consisting of the following fields:

\n\n
    \n
  • size (optional): interval of natural numbers for the size of each array dimension (default: range(1, 6))
    • \n
  • value (optional): interval of real numbers for the value of each array element (default: range(-1.0, 1.0) - a uniform distribution on a closed interval $[-1.0, 1.0]$).
    • \n
\n\n

Example range parameter config:

\n\n
\n
{"size": range(2, 10), "value": jm.range.value.closed(100.0, 200.0)}\n
\n
\n\n

Intervals are expressed as a range object.\nCurrently, the following syntax is supported for range objects:

\n\n
    \n
  1. Direct value of type int or float - it corresponds to a singleton interval $[a, a] = {a}$. In random generation context, this just means a constant fixed value.
    2. \n
  2. Use the functions from jijmodeling.range, jijmodeling.range.size, or jijmodeling.range.value modules.\n
      \n
    • Use functions from jij.modeling.range.size to specify (non-negative) integer intervals, and jij.modeling.range.value for real intervals. jij.modeling.range dynamically determines the type of the range based on the input.
      • \n
    • These three modules provides the following combinators (see the module documents for more details.):\n
        \n
      • closed(a, b): a closed interval $[a, b]$
        • \n
      • open(a, b): an open interval $(a, b)$
        • \n
      • closed_open(a, b): an upper half-open interval $[a, b)$
        • \n
      • open_closed(a, b): a lower half-open interval $(a, b]$
        • \n
      • greater_than(a): an open interval $(a, \\infty)$
        • \n
      • at_least(a): a closed interval $[a, \\infty)$
        • \n
      • less_than(a): an open interval $(-\\infty, a)$
        • \n
      • at_most(a): a closed interval $(-\\infty, a]$
        • \n
      • \n
    3. \n
  3. Use range builtin function: this is equivalent to jijmodeling.range.value.closed_open(a, b).\n
      \n
    • Any python range object with step = 1 can be used as a size range; otherwise it results in runtime error.
      • \n
    4. \n

  4. Use a tuple: raw tuple (a, b) is equivalent to jijmodeling.range.closed_open(a, b) if a and b are either int or float.

    \n\n
      \n

    • You can also use bound object as a tuple component; in such case, both tuple components must be one of the following:

      \n\n
        \n
      1. A string \"Unbounded\" means $-\\infty$ (in the first component) or $\\infty$ (the second).
        2. \n
      2. A dictionary {\"Included\": a} means the endpoint is inclusive.
        3. \n
      3. A dictionary {\"Excluded\": a} means the endpoint is exclusive.
        4. \n
      • \n
    • Examples:\n
        \n
      • (1.2, 4) is equivalent to closed_open(1.2, 4),
        • \n
      • (-1, {\"Included\": 1}) is equivalent to closed(-1, 1),
        • \n
      • (-5, {\"Excluded\": 4}) is equivalent to closed_open(-5, 4) and built in function range(-5, 4),
        • \n
      • ({\"Excluded\": 1}, {\"Excluded\": 2.5}) is equivalent to open(1, 2.5),
        • \n
      • ({\"Included\": -1}, \"Unbounded\") is equivalent to at_least(-1).
        • \n
      • (5, \"Unbounded\") is INVALID; 5 must be bound object.
        • \n
      • \n
    5. \n
  5. The range object: A dictionary of form {\"start\": lb, \"end\": ub}, where both lb and ub are the bound object described as above.
    6. \n
\n\n

Examples

\n\n
\n
>>> import jijmodeling as jm\n>>> import builtins\n>>> N = jm.Placeholder("N", dtype=jm.DataType.INTEGER)\n>>> c = jm.Placeholder("c", dtype=jm.DataType.FLOAT, shape=(N,))\n>>> x = jm.BinaryVar("x", shape=(N,))\n>>> i = jm.Element("i", belong_to=N)\n\n>>> problem = jm.Problem("problem")\n>>> problem += jm.sum(i, c[i] * x[i])\n\n>>> inputs = problem.generate_random_dataset(\n...     options={\n...         'N': {"value": builtins.range(10, 20)},\n...         'c': {"value": jm.range.value.closed(-1.0, 1.0)}\n...          # You can also specify "size" for the range of jagged array dimension size.\n...     },\n...     seed=123 # omittable\n... )\n>>> assert set(inputs.keys()) == {"N", "c"}\n>>> inputs\n{'N': 11.0, 'c': array([ 0.93914459, -0.06511935, -0.7460324 , -0.32443706,  0.99981451,\n       -0.24407535,  0.31329469,  0.52206453, -0.1291936 ,  0.30443087,\n        0.53125838])}\n
\n
\n", "signature": "(seed, default, options):", "funcdef": "def"}, "jijmodeling.Problem.generate_random_instance": {"fullname": "jijmodeling.Problem.generate_random_instance", "modulename": "jijmodeling", "qualname": "Problem.generate_random_instance", "kind": "function", "doc": "

Generates random ommx.v1.Instance for a given problem.\nSee also InstanceDataValue.generate_random_dataset.

\n\n

Args

\n\n
    \n
  • options (optional): a dictionary of range parameters for each separate placeholders. The key must be the name of the placeholder and the value must be range parameter (as described in \"Range Parameters and Range Syntax\" section in ~jijmodeling.Problem.generate_random_dataset()).
    • \n
  • default (optional): default range parameters for placeholders which is not specified in options.
    • \n
  • seed (optional): seed for random number generation.
    • \n
  • hints (optional): the hints to be detected during compilation see Interpreter.eval_problem for more details.
    • \n
\n\n

Returns

\n\n

instance: The OMMX v1 instance object.

\n\n

Examples

\n\n
\n
>>> import jijmodeling as jm\n>>> import builtins\n>>> import ommx.v1\n>>> N = jm.Placeholder("N", dtype=jm.DataType.INTEGER)\n>>> c = jm.Placeholder("c", dtype=jm.DataType.FLOAT, shape=(N,))\n>>> x = jm.BinaryVar("x", shape=(N,))\n>>> i = jm.Element("i", belong_to=N)\n\n>>> problem = jm.Problem("problem")\n>>> problem += jm.sum(i, c[i] * x[i])\n\n>>> instance = problem.generate_random_instance(\n...     options={\n...         'N': {"value": builtins.range(10, 20)},\n...         'c': {"value": jm.range.value.closed(-1.0, 1.0)}\n...     },\n...     seed=123\n... )\n>>> assert type(instance) is ommx.v1.Instance\n
\n
\n", "signature": "(seed, default, options, hints):", "funcdef": "def"}, "jijmodeling.Problem.name": {"fullname": "jijmodeling.Problem.name", "modulename": "jijmodeling", "qualname": "Problem.name", "kind": "variable", "doc": "

\n"}, "jijmodeling.Problem.sense": {"fullname": "jijmodeling.Problem.sense", "modulename": "jijmodeling", "qualname": "Problem.sense", "kind": "variable", "doc": "

\n"}, "jijmodeling.Problem.constraints": {"fullname": "jijmodeling.Problem.constraints", "modulename": "jijmodeling", "qualname": "Problem.constraints", "kind": "variable", "doc": "

\n"}, "jijmodeling.Problem.custom_penalty_terms": {"fullname": "jijmodeling.Problem.custom_penalty_terms", "modulename": "jijmodeling", "qualname": "Problem.custom_penalty_terms", "kind": "variable", "doc": "

\n"}, "jijmodeling.Problem.objective": {"fullname": "jijmodeling.Problem.objective", "modulename": "jijmodeling", "qualname": "Problem.objective", "kind": "variable", "doc": "

\n"}, "jijmodeling.ProblemSense": {"fullname": "jijmodeling.ProblemSense", "modulename": "jijmodeling", "qualname": "ProblemSense", "kind": "class", "doc": "

An optimization sense

\n"}, "jijmodeling.ProblemSense.MINIMIZE": {"fullname": "jijmodeling.ProblemSense.MINIMIZE", "modulename": "jijmodeling", "qualname": "ProblemSense.MINIMIZE", "kind": "variable", "doc": "

\n", "default_value": "ProblemSense.MINIMIZE"}, "jijmodeling.ProblemSense.MAXIMIZE": {"fullname": "jijmodeling.ProblemSense.MAXIMIZE", "modulename": "jijmodeling", "qualname": "ProblemSense.MAXIMIZE", "kind": "variable", "doc": "

\n", "default_value": "ProblemSense.MAXIMIZE"}, "jijmodeling.Constraint": {"fullname": "jijmodeling.Constraint", "modulename": "jijmodeling", "qualname": "Constraint", "kind": "class", "doc": "

A class for creating a constraint

\n\n

The Constraint class is used to create a constraint.

\n\n

Attributes

\n\n
    \n
  • name (str): A name of the constraint.
    • \n
  • sense: equal sign (=) or inequality sign (>= or <=) included in the expression.
    • \n
  • expression: The (in)equality equation of the constraint.
    • \n
  • forall (list): A list that stores forall indices.
    • \n
\n\n

Args

\n\n
    \n
  • name (str): A name of the constraint.
    • \n
  • expression: The (in)equality equation of the constraint.
    • \n
  • forall: A list that stores forall indices. Defaults to None.
    • \n
\n\n

Raises

\n\n

ModelingError: Raises if expression does not contain any decision variable.

\n\n

Expression

\n\n

Create an equality constraint that the sum of $N$ binary variables is equal to one.

\n\n
\n
>>> import jijmodeling as jm\n>>> N = jm.Placeholder("N")\n>>> i = jm.Element("i", belong_to=N)\n>>> x = jm.BinaryVar("x", shape=(N,))\n>>> repr(jm.Constraint("constraint", jm.sum(i, x[i]) == 1))\n'Constraint(name="constraint", expression=sum(i in [0..N), x[i]) == 1)'\n
\n
\n\n

Create an inequality constraint with forall.

\n\n
\n
>>> import jijmodeling as jm\n>>> N = jm.Placeholder("N")\n>>> i = jm.Element("i", belong_to=N)\n>>> j = jm.Element("j", belong_to=N)\n>>> x = jm.BinaryVar("x", shape=(N, N))\n>>> repr(jm.Constraint("constraint", jm.sum(i, x[i,j]) == 1, forall=j))\n'Constraint(name="constraint", expression=sum(i in [0..N), x[i, j]) == 1, forall=[j])'\n
\n
\n\n

Create an inequality constraint with conditional forall.

\n\n
\n
>>> import jijmodeling as jm\n>>> N = jm.Placeholder("N")\n>>> i = jm.Element("i", belong_to=N)\n>>> j = jm.Element("j", belong_to=N)\n>>> x = jm.BinaryVar("x", shape=(N, N))\n>>> repr(jm.Constraint("constraint", x[i,j] <= 2, forall=[i, (j, j != i)]))\n'Constraint(name="constraint", expression=x[i, j] <= 2, forall=[i, (j, j != i)])'\n
\n
\n"}, "jijmodeling.Constraint.is_equality": {"fullname": "jijmodeling.Constraint.is_equality", "modulename": "jijmodeling", "qualname": "Constraint.is_equality", "kind": "function", "doc": "

Returns true if the constraint is an equality constraint.

\n\n

Returns

\n\n

bool: True if the constraint is an equality constraint. Otherwise, False.

\n\n

Examples

\n\n
\n
>>> import jijmodeling as jm\n>>> N = jm.Placeholder("N")\n>>> i = jm.Element("i", belong_to=N)\n>>> x = jm.BinaryVar("x", shape=(N,))\n>>> constraint = jm.Constraint("constraint", jm.sum(i, x[i]) == 1)\n>>> assert constraint.is_equality()\n
\n
\n", "signature": "(self, /):", "funcdef": "def"}, "jijmodeling.Constraint.is_inequality": {"fullname": "jijmodeling.Constraint.is_inequality", "modulename": "jijmodeling", "qualname": "Constraint.is_inequality", "kind": "function", "doc": "

Returns true if the constraint is an inequality constraint.

\n\n

Returns

\n\n

bool: True if the constraint is an inequality constraint. Otherwise, False.

\n\n

Examples

\n\n
\n
>>> import jijmodeling as jm\n>>> N = jm.Placeholder("N")\n>>> i = jm.Element("i", belong_to=N)\n>>> x = jm.BinaryVar("x", shape=(N,))\n>>> constraint = jm.Constraint("constraint", jm.sum(i, x[i]) <= 1)\n>>> assert constraint.is_inequality()\n
\n
\n", "signature": "(self, /):", "funcdef": "def"}, "jijmodeling.Constraint.right": {"fullname": "jijmodeling.Constraint.right", "modulename": "jijmodeling", "qualname": "Constraint.right", "kind": "variable", "doc": "

\n"}, "jijmodeling.Constraint.expression": {"fullname": "jijmodeling.Constraint.expression", "modulename": "jijmodeling", "qualname": "Constraint.expression", "kind": "variable", "doc": "

\n"}, "jijmodeling.Constraint.left": {"fullname": "jijmodeling.Constraint.left", "modulename": "jijmodeling", "qualname": "Constraint.left", "kind": "variable", "doc": "

\n"}, "jijmodeling.Constraint.forall": {"fullname": "jijmodeling.Constraint.forall", "modulename": "jijmodeling", "qualname": "Constraint.forall", "kind": "variable", "doc": "

\n"}, "jijmodeling.Constraint.sense": {"fullname": "jijmodeling.Constraint.sense", "modulename": "jijmodeling", "qualname": "Constraint.sense", "kind": "variable", "doc": "

\n"}, "jijmodeling.Constraint.name": {"fullname": "jijmodeling.Constraint.name", "modulename": "jijmodeling", "qualname": "Constraint.name", "kind": "variable", "doc": "

\n"}, "jijmodeling.ConstraintSense": {"fullname": "jijmodeling.ConstraintSense", "modulename": "jijmodeling", "qualname": "ConstraintSense", "kind": "class", "doc": "

Equality of a constraint

\n"}, "jijmodeling.ConstraintSense.EQUAL": {"fullname": "jijmodeling.ConstraintSense.EQUAL", "modulename": "jijmodeling", "qualname": "ConstraintSense.EQUAL", "kind": "variable", "doc": "

\n", "default_value": "ConstraintSense.EQUAL"}, "jijmodeling.ConstraintSense.LESS_THAN_EQUAL": {"fullname": "jijmodeling.ConstraintSense.LESS_THAN_EQUAL", "modulename": "jijmodeling", "qualname": "ConstraintSense.LESS_THAN_EQUAL", "kind": "variable", "doc": "

\n", "default_value": "ConstraintSense.LESS_THAN_EQUAL"}, "jijmodeling.ConstraintSense.GREATER_THAN_EQUAL": {"fullname": "jijmodeling.ConstraintSense.GREATER_THAN_EQUAL", "modulename": "jijmodeling", "qualname": "ConstraintSense.GREATER_THAN_EQUAL", "kind": "variable", "doc": "

\n", "default_value": "ConstraintSense.GREATER_THAN_EQUAL"}, "jijmodeling.CustomPenaltyTerm": {"fullname": "jijmodeling.CustomPenaltyTerm", "modulename": "jijmodeling", "qualname": "CustomPenaltyTerm", "kind": "class", "doc": "

A class for creating a custom penalty term

\n\n

The CustomPenaltyTerm class is used to create a custom penalty term.

\n\n

Attributes

\n\n
    \n
  • name (str): A name of the custom penalty term.
    • \n
  • expression: The expression of the custom penalty term.
    • \n
  • forall (list): A list that stores forall indices.
    • \n
\n\n

Args

\n\n
    \n
  • name (str): A name of the custom penalty term.
    • \n
  • expression: The expression of the custom penalty term.
    • \n
  • forall: A list that stores forall indices. Defaults to None.
    • \n
\n\n

Raises

\n\n

ModelingError: Raises if expression does not contain any decision variable.

\n\n

Expression

\n\n

Create a custom penalty term.

\n\n
\n
>>> import jijmodeling as jm\n>>> N = jm.Placeholder("N")\n>>> i = jm.Element("i", belong_to=N)\n>>> x = jm.BinaryVar("x", shape=(N,))\n>>> repr(jm.CustomPenaltyTerm("custom penalty term", (jm.sum(i, x[i]) - 1)**2))  # doctest: +ELLIPSIS\n'CustomPenaltyTerm(name="custom penalty term", expression=((sum(i in [0..N), x[i]) - 1) ** 2))'\n
\n
\n\n

Create a custom penalty term with forall.

\n\n
\n
>>> import jijmodeling as jm\n>>> N = jm.Placeholder("N")\n>>> i = jm.Element("i", belong_to=N)\n>>> j = jm.Element("j", belong_to=N)\n>>> x = jm.BinaryVar("x", shape=(N, N))\n>>> repr(jm.CustomPenaltyTerm("custom penalty term", (jm.sum(i, x[i,j]) - 1)**2, forall=j))  # doctest: +ELLIPSIS\n'CustomPenaltyTerm(name="custom penalty term", expression=((sum(i in [0..N), x[i, j]) - 1) ** 2), forall=[j])'\n
\n
\n\n

Create a custom penalty term with conditional forall.

\n\n
\n
>>> import jijmodeling as jm\n>>> N = jm.Placeholder("N")\n>>> i = jm.Element("i", belong_to=N)\n>>> j = jm.Element("j", belong_to=N)\n>>> x = jm.BinaryVar("x", shape=(N, N))\n>>> repr(jm.CustomPenaltyTerm("custom penalty term", (x[i,j] - 2)**2, forall=[i, (j, j != i)]))  # doctest: +ELLIPSIS\n'CustomPenaltyTerm(name="custom penalty term", expression=((x[i, j] - 2) ** 2), forall=[i, (j, j != i)])'\n
\n
\n"}, "jijmodeling.CustomPenaltyTerm.name": {"fullname": "jijmodeling.CustomPenaltyTerm.name", "modulename": "jijmodeling", "qualname": "CustomPenaltyTerm.name", "kind": "variable", "doc": "

\n"}, "jijmodeling.CustomPenaltyTerm.expression": {"fullname": "jijmodeling.CustomPenaltyTerm.expression", "modulename": "jijmodeling", "qualname": "CustomPenaltyTerm.expression", "kind": "variable", "doc": "

\n"}, "jijmodeling.CustomPenaltyTerm.forall": {"fullname": "jijmodeling.CustomPenaltyTerm.forall", "modulename": "jijmodeling", "qualname": "CustomPenaltyTerm.forall", "kind": "variable", "doc": "

\n"}, "jijmodeling.Evaluation": {"fullname": "jijmodeling.Evaluation", "modulename": "jijmodeling", "qualname": "Evaluation", "kind": "class", "doc": "

A class for evaluation.

\n\n

The Evaluation class is to represent the result of evaluating a model.

\n\n

Attributes:\n energy (numpy.ndarray): The value of energy of each sample.\n objective (numpy.ndarray): The value of an objective function of each sample.\n constraint_violations (dict[str, numpy.ndarray]): The constraint violation of each sample.\n constraint_forall (dict[str, numpy.ndarray]): The constraint forall of each sample.\n constraint_values (numpy.ndarray): The constraint value of each sample.\n penalty (dict[str, numpy.ndarray]): The penalty of each sample.

\n"}, "jijmodeling.Evaluation.to_dict": {"fullname": "jijmodeling.Evaluation.to_dict", "modulename": "jijmodeling", "qualname": "Evaluation.to_dict", "kind": "function", "doc": "

Convert into a dict.

\n\n

Returns:\n dict: A dict whose keys are name of the Evaluation's fields.

\n", "signature": "(self, /):", "funcdef": "def"}, "jijmodeling.Evaluation.from_dict": {"fullname": "jijmodeling.Evaluation.from_dict", "modulename": "jijmodeling", "qualname": "Evaluation.from_dict", "kind": "function", "doc": "

Create a Evaluation object from the given dict.

\n\n

If a key is not the name of the Evaluation fields, the value value is ignored.

\n\n

Args:\n dict: A dict whose keys are name of the Evaluation fields.

\n", "signature": "(dict):", "funcdef": "def"}, "jijmodeling.Evaluation.to_json": {"fullname": "jijmodeling.Evaluation.to_json", "modulename": "jijmodeling", "qualname": "Evaluation.to_json", "kind": "function", "doc": "

Serialize the Evaluation object into a JSON string.

\n\n

Returns:\n str: A JSON string.

\n\n

Note:\n A numpy array is converted into a list.

\n", "signature": "(self, /):", "funcdef": "def"}, "jijmodeling.Evaluation.from_json": {"fullname": "jijmodeling.Evaluation.from_json", "modulename": "jijmodeling", "qualname": "Evaluation.from_json", "kind": "function", "doc": "

Create a Evaluation object from the JSON string.

\n\n

Args:\n json (str): A JSON string.

\n\n

Returns:\n Evaluation: An Evaluation object.

\n", "signature": "(json):", "funcdef": "def"}, "jijmodeling.Evaluation.to_pandas": {"fullname": "jijmodeling.Evaluation.to_pandas", "modulename": "jijmodeling", "qualname": "Evaluation.to_pandas", "kind": "function", "doc": "

Convert into a pandas DataFrame.

\n\n

Returns:\n pandas.DataFrame: A pandas DataFrame.

\n", "signature": "(self, /):", "funcdef": "def"}, "jijmodeling.Evaluation.energy": {"fullname": "jijmodeling.Evaluation.energy", "modulename": "jijmodeling", "qualname": "Evaluation.energy", "kind": "variable", "doc": "

\n"}, "jijmodeling.Evaluation.penalty": {"fullname": "jijmodeling.Evaluation.penalty", "modulename": "jijmodeling", "qualname": "Evaluation.penalty", "kind": "variable", "doc": "

\n"}, "jijmodeling.Evaluation.constraint_violations": {"fullname": "jijmodeling.Evaluation.constraint_violations", "modulename": "jijmodeling", "qualname": "Evaluation.constraint_violations", "kind": "variable", "doc": "

\n"}, "jijmodeling.Evaluation.constraint_forall": {"fullname": "jijmodeling.Evaluation.constraint_forall", "modulename": "jijmodeling", "qualname": "Evaluation.constraint_forall", "kind": "variable", "doc": "

\n"}, "jijmodeling.Evaluation.objective": {"fullname": "jijmodeling.Evaluation.objective", "modulename": "jijmodeling", "qualname": "Evaluation.objective", "kind": "variable", "doc": "

\n"}, "jijmodeling.Evaluation.constraint_values": {"fullname": "jijmodeling.Evaluation.constraint_values", "modulename": "jijmodeling", "qualname": "Evaluation.constraint_values", "kind": "variable", "doc": "

\n"}, "jijmodeling.Evaluation.constraint_expr_values": {"fullname": "jijmodeling.Evaluation.constraint_expr_values", "modulename": "jijmodeling", "qualname": "Evaluation.constraint_expr_values", "kind": "variable", "doc": "

Return the values for the each constraint.

\n\n

The return value is a list of dictionaries.\nEach dictionary has the keys of the constraint names and the dictionary values.\nThe dictionary values have the keys of the forall indices as tuple and the values for the forall indices.

\n\n

Returns:\n A list of dictionaries.

\n"}, "jijmodeling.Record": {"fullname": "jijmodeling.Record", "modulename": "jijmodeling", "qualname": "Record", "kind": "class", "doc": "

A class for representing a record.

\n\n

There are two types of solutions that can be given; dense solutions and sparse solutions.\nA dense solution is a dict whose key is a variable name and the value is a list of numpy.ndarray.\nA sparse solution is a dict whose key is a variable name and the value is a list of tuples with three elements,\nwhere the first element is a list of indices, the second element is a list of non-zero values, and the third element is a shape of the array.\nThe length of the list of solutions must be the same as the length of the list of num_occurrences.\nEach index of the list of solutions corresponds to the same index of the list of non-zero values.

\n\n

As an example, consider the following solutions:

\n\n
\n
{\n    "x": [\n        np.array([[0.0, 1.0, 0.0], [2.0, 0.0, 3.0]]),\n        np.array([[1.0, 0.0, 0.0], [2.0, 3.0, 4.0]])\n    ],\n    "y": [\n        np.array([0.0, 0.0, 1.0]),\n        np.array([0.0, 1.0, 0.0])\n    ]\n}\n
\n
\n\n

This is a dense solution. The corresponding sparse solution is as follows:

\n\n
\n
{\n    "x": [\n        (([0, 1, 1], [1, 0, 2]), [1.0, 2.0, 3.0], (2, 3)),\n        (([0, 1, 1, 1], [0, 0, 1, 2]), [1.0, 2.0, 3.0, 4.0], (2, 3))\n    ],\n    "y": [\n        (([2],), [1.0], (3,)),\n        (([1],), [1.0], (3,))\n    ]\n}\n
\n
\n\n

Attributes

\n\n
    \n
  • solution (Union[Dict[str, List[numpy.ndarray]], Dict[str, List[Tuple[List[int], List[float], Tuple[int, ...]]]]]): A solution.
    • \n
  • num_occurrences (List[int]): A list of the number of occurrences in which the solution is observed.
    • \n
\n"}, "jijmodeling.Record.to_dict": {"fullname": "jijmodeling.Record.to_dict", "modulename": "jijmodeling", "qualname": "Record.to_dict", "kind": "function", "doc": "

Convert into a dict.

\n\n

Returns

\n\n

dict: A dict whose keys are \"solution\" and \"num_occurrences\".

\n", "signature": "(self, /):", "funcdef": "def"}, "jijmodeling.Record.from_dict": {"fullname": "jijmodeling.Record.from_dict", "modulename": "jijmodeling", "qualname": "Record.from_dict", "kind": "function", "doc": "

Create a Record object from the given dict.

\n\n

If a key is not one of \"solution\" or \"num_occurrences\", the value is ignored.

\n\n

Args

\n\n
    \n
  • dict: A dict of str to a list of dense solutions or sparse solutions.
    • \n
\n\n

Returns

\n\n

A Record object.

\n", "signature": "(dict):", "funcdef": "def"}, "jijmodeling.Record.to_json": {"fullname": "jijmodeling.Record.to_json", "modulename": "jijmodeling", "qualname": "Record.to_json", "kind": "function", "doc": "

Serialize the Record object into a JSON string.

\n\n

Returns

\n\n

str: A JSON string.

\n\n

Note

\n\n

A numpy array is serialized into a list.

\n", "signature": "(self, /):", "funcdef": "def"}, "jijmodeling.Record.from_json": {"fullname": "jijmodeling.Record.from_json", "modulename": "jijmodeling", "qualname": "Record.from_json", "kind": "function", "doc": "

Create a Record object from the JSON string.

\n\n

Args

\n\n

json (str): A JSON string.

\n\n

Returns

\n\n

Record: A Record object.

\n", "signature": "(json):", "funcdef": "def"}, "jijmodeling.Record.is_dense": {"fullname": "jijmodeling.Record.is_dense", "modulename": "jijmodeling", "qualname": "Record.is_dense", "kind": "function", "doc": "

Return true if the solution is dense.

\n\n

Returns

\n\n

bool: True if the solution is dense.

\n", "signature": "(self, /):", "funcdef": "def"}, "jijmodeling.Record.is_sparse": {"fullname": "jijmodeling.Record.is_sparse", "modulename": "jijmodeling", "qualname": "Record.is_sparse", "kind": "function", "doc": "

Return true if the solution is sparse.

\n\n

Returns

\n\n

bool: True if the solution is sparse.

\n", "signature": "(self, /):", "funcdef": "def"}, "jijmodeling.Record.to_dense": {"fullname": "jijmodeling.Record.to_dense", "modulename": "jijmodeling", "qualname": "Record.to_dense", "kind": "function", "doc": "

Return a Record object whose solution is dense.

\n\n

If the solution is already dense, the solution is not converted.\nOtherwise, the solution is converted into a dense solution.

\n\n

Returns

\n\n

Record: A Record object whose solution is dense.

\n", "signature": "(self, /):", "funcdef": "def"}, "jijmodeling.Record.to_sparse": {"fullname": "jijmodeling.Record.to_sparse", "modulename": "jijmodeling", "qualname": "Record.to_sparse", "kind": "function", "doc": "

Return a Record object whose solution is sparse.

\n\n

If the solution is already sparse, the solution is not converted.\nOtherwise, the solution is converted into a sparse solution.

\n\n

Returns

\n\n

Record: A Record object whose solution is sparse.

\n", "signature": "(self, /):", "funcdef": "def"}, "jijmodeling.Record.to_pandas": {"fullname": "jijmodeling.Record.to_pandas", "modulename": "jijmodeling", "qualname": "Record.to_pandas", "kind": "function", "doc": "

Convert into a pandas DataFrame.

\n\n

Returns

\n\n

pandas.DataFrame: A pandas DataFrame.

\n", "signature": "(self, /):", "funcdef": "def"}, "jijmodeling.Record.num_occurrences": {"fullname": "jijmodeling.Record.num_occurrences", "modulename": "jijmodeling", "qualname": "Record.num_occurrences", "kind": "variable", "doc": "

\n"}, "jijmodeling.Record.solution": {"fullname": "jijmodeling.Record.solution", "modulename": "jijmodeling", "qualname": "Record.solution", "kind": "variable", "doc": "

\n"}, "jijmodeling.SolvingTime": {"fullname": "jijmodeling.SolvingTime", "modulename": "jijmodeling", "qualname": "SolvingTime", "kind": "class", "doc": "

A class for storing time to solve a problem.

\n\n

Attributes

\n\n
    \n
  • preprocess (float, optional): Time to preprocess the problem. Defaults to None.
    • \n
  • solve (float, optional): Time to solve the problem. Defaults to None.
    • \n
  • postprocess (float, optional): Time to postprocess the problem. Defaults to None.
    • \n
\n"}, "jijmodeling.SolvingTime.to_dict": {"fullname": "jijmodeling.SolvingTime.to_dict", "modulename": "jijmodeling", "qualname": "SolvingTime.to_dict", "kind": "function", "doc": "

Convert into a dict.

\n\n

Returns

\n\n

dict: A dict with keys \"preprocess\", \"solve\", and \"postprocess\".

\n", "signature": "(self, /):", "funcdef": "def"}, "jijmodeling.SolvingTime.from_dict": {"fullname": "jijmodeling.SolvingTime.from_dict", "modulename": "jijmodeling", "qualname": "SolvingTime.from_dict", "kind": "function", "doc": "

Create a SolvingTime object from the given dict.

\n\n

If a key is not one of \"preprocess\", \"solve\", or \"postprocess\", the value is ignored.

\n\n

Args

\n\n

dict (dict): A dict.

\n\n

Returns

\n\n

A SolvingTime object.

\n", "signature": "(dict):", "funcdef": "def"}, "jijmodeling.SolvingTime.to_json": {"fullname": "jijmodeling.SolvingTime.to_json", "modulename": "jijmodeling", "qualname": "SolvingTime.to_json", "kind": "function", "doc": "

Serialize the SolvingTime object into a JSON string.

\n\n

Returns

\n\n

str: A JSON string.

\n\n

Note

\n\n

A numpy array is serialized into a list.

\n", "signature": "(self, /):", "funcdef": "def"}, "jijmodeling.SolvingTime.from_json": {"fullname": "jijmodeling.SolvingTime.from_json", "modulename": "jijmodeling", "qualname": "SolvingTime.from_json", "kind": "function", "doc": "

Create a SolvingTime object from the JSON string.

\n\n

Args

\n\n

json (str): A JSON string.

\n\n

Returns

\n\n

SolvingTime: A SolvingTime object.

\n", "signature": "(json):", "funcdef": "def"}, "jijmodeling.SolvingTime.preprocess": {"fullname": "jijmodeling.SolvingTime.preprocess", "modulename": "jijmodeling", "qualname": "SolvingTime.preprocess", "kind": "variable", "doc": "

\n"}, "jijmodeling.SolvingTime.solve": {"fullname": "jijmodeling.SolvingTime.solve", "modulename": "jijmodeling", "qualname": "SolvingTime.solve", "kind": "variable", "doc": "

\n"}, "jijmodeling.SolvingTime.postprocess": {"fullname": "jijmodeling.SolvingTime.postprocess", "modulename": "jijmodeling", "qualname": "SolvingTime.postprocess", "kind": "variable", "doc": "

\n"}, "jijmodeling.SystemTime": {"fullname": "jijmodeling.SystemTime", "modulename": "jijmodeling", "qualname": "SystemTime", "kind": "class", "doc": "

A class for storing time of jijzept running.

\n\n

Attributes

\n\n
    \n
  • post_problem_and_instance_data (float, optional): Time to upload problem and instance_data to blob. Defaults to None.
    • \n
  • request_queue (float, optional): Time to send request to queue. Defaults to None.
    • \n
  • fetch_problem_and_instance_data (float, optional): Time to fetch problme and instance_data from blob. Defaults to None.
    • \n
  • fetch_result (float, optional): Time to fetch result. Defaults to None.
    • \n
  • deserialize_solution (float, optional): Time to deserialize json object. Defaults to None.
    • \n
\n"}, "jijmodeling.SystemTime.to_dict": {"fullname": "jijmodeling.SystemTime.to_dict", "modulename": "jijmodeling", "qualname": "SystemTime.to_dict", "kind": "function", "doc": "

Convert into a dict.

\n\n

Returns

\n\n

dict: A dict whose keys are name of the SystemTime's fields.

\n", "signature": "(self, /):", "funcdef": "def"}, "jijmodeling.SystemTime.from_dict": {"fullname": "jijmodeling.SystemTime.from_dict", "modulename": "jijmodeling", "qualname": "SystemTime.from_dict", "kind": "function", "doc": "

Create a SystemTime object from the given dict.

\n\n

If a key is not the name of the SystemTime fields, the value value is ignored.

\n\n

Args

\n\n

dict (dict): A dict.

\n\n

Returns

\n\n

A SystemTime object.

\n", "signature": "(dict):", "funcdef": "def"}, "jijmodeling.SystemTime.to_json": {"fullname": "jijmodeling.SystemTime.to_json", "modulename": "jijmodeling", "qualname": "SystemTime.to_json", "kind": "function", "doc": "

Serialize the SystemTime object into a JSON string.

\n\n

Returns

\n\n

str: A JSON string.

\n\n

Note

\n\n

A numpy array is serialized into a list.

\n", "signature": "(self, /):", "funcdef": "def"}, "jijmodeling.SystemTime.from_json": {"fullname": "jijmodeling.SystemTime.from_json", "modulename": "jijmodeling", "qualname": "SystemTime.from_json", "kind": "function", "doc": "

Create a SystemTime object from the JSON string.

\n\n

Args

\n\n

json (str): A JSON string.

\n\n

Returns

\n\n

SystemTime: A SystemTime object.

\n", "signature": "(json):", "funcdef": "def"}, "jijmodeling.SystemTime.request_queue": {"fullname": "jijmodeling.SystemTime.request_queue", "modulename": "jijmodeling", "qualname": "SystemTime.request_queue", "kind": "variable", "doc": "

\n"}, "jijmodeling.SystemTime.fetch_problem_and_instance_data": {"fullname": "jijmodeling.SystemTime.fetch_problem_and_instance_data", "modulename": "jijmodeling", "qualname": "SystemTime.fetch_problem_and_instance_data", "kind": "variable", "doc": "

\n"}, "jijmodeling.SystemTime.deserialize_solution": {"fullname": "jijmodeling.SystemTime.deserialize_solution", "modulename": "jijmodeling", "qualname": "SystemTime.deserialize_solution", "kind": "variable", "doc": "

\n"}, "jijmodeling.SystemTime.fetch_result": {"fullname": "jijmodeling.SystemTime.fetch_result", "modulename": "jijmodeling", "qualname": "SystemTime.fetch_result", "kind": "variable", "doc": "

\n"}, "jijmodeling.SystemTime.post_problem_and_instance_data": {"fullname": "jijmodeling.SystemTime.post_problem_and_instance_data", "modulename": "jijmodeling", "qualname": "SystemTime.post_problem_and_instance_data", "kind": "variable", "doc": "

\n"}, "jijmodeling.MeasuringTime": {"fullname": "jijmodeling.MeasuringTime", "modulename": "jijmodeling", "qualname": "MeasuringTime", "kind": "class", "doc": "

A class for storing time to be measured.

\n\n

Attributes

\n\n
    \n
  • solve (SolvingTime): Time to solve the problem.
    • \n
  • system (SystemTime): Time to measure system time.
    • \n
  • total (float, optional): Total time to solve the problem. Defaults to None.
    • \n
\n"}, "jijmodeling.MeasuringTime.to_dict": {"fullname": "jijmodeling.MeasuringTime.to_dict", "modulename": "jijmodeling", "qualname": "MeasuringTime.to_dict", "kind": "function", "doc": "

Convert into a dict.

\n\n

Returns

\n\n

dict: A dict whose keys are name of the MeasuringTime's fields.

\n", "signature": "(self, /):", "funcdef": "def"}, "jijmodeling.MeasuringTime.from_dict": {"fullname": "jijmodeling.MeasuringTime.from_dict", "modulename": "jijmodeling", "qualname": "MeasuringTime.from_dict", "kind": "function", "doc": "

Create a MeasuringTime object from the given dict.

\n\n

If a key is not the name of the MeasuringTime fields, the value value is ignored.

\n\n

Args

\n\n

dict (dict): A dict.

\n\n

Returns

\n\n

A MeasuringTime object.

\n", "signature": "(dict):", "funcdef": "def"}, "jijmodeling.MeasuringTime.to_json": {"fullname": "jijmodeling.MeasuringTime.to_json", "modulename": "jijmodeling", "qualname": "MeasuringTime.to_json", "kind": "function", "doc": "

Serialize the MeasuringTime object into a JSON string.

\n\n

Returns

\n\n

str: A JSON string.

\n\n

Note

\n\n

A numpy array is serialized into a list.

\n", "signature": "(self, /):", "funcdef": "def"}, "jijmodeling.MeasuringTime.from_json": {"fullname": "jijmodeling.MeasuringTime.from_json", "modulename": "jijmodeling", "qualname": "MeasuringTime.from_json", "kind": "function", "doc": "

Create a MeasuringTime object from the JSON string.

\n\n

Args

\n\n

json (str): A JSON string.

\n\n

Returns

\n\n

MeasuringTime: A MeasuringTime object.

\n", "signature": "(json):", "funcdef": "def"}, "jijmodeling.MeasuringTime.total": {"fullname": "jijmodeling.MeasuringTime.total", "modulename": "jijmodeling", "qualname": "MeasuringTime.total", "kind": "variable", "doc": "

\n"}, "jijmodeling.MeasuringTime.solve": {"fullname": "jijmodeling.MeasuringTime.solve", "modulename": "jijmodeling", "qualname": "MeasuringTime.solve", "kind": "variable", "doc": "

\n"}, "jijmodeling.MeasuringTime.system": {"fullname": "jijmodeling.MeasuringTime.system", "modulename": "jijmodeling", "qualname": "MeasuringTime.system", "kind": "variable", "doc": "

\n"}, "jijmodeling.SampleSet": {"fullname": "jijmodeling.SampleSet", "modulename": "jijmodeling", "qualname": "SampleSet", "kind": "class", "doc": "

A class for storing time of jijzept running.

\n\n

Attributes

\n\n
    \n
  • post_problem_and_instance_data (float, optional): Time to upload problem and instance_data to blob. Defaults to None.
    • \n
  • request_queue (float, optional): Time to send request to queue. Defaults to None.
    • \n
  • fetch_problem_and_instance_data (float, optional): Time to fetch problem and instance_data from blob. Defaults to None.
    • \n
  • fetch_result (float, optional): Time to fetch result. Defaults to None.
    • \n
  • deserialize_solution (float, optional): Time to deserialize json object. Defaults to None.
    • \n
\n"}, "jijmodeling.SampleSet.to_dict": {"fullname": "jijmodeling.SampleSet.to_dict", "modulename": "jijmodeling", "qualname": "SampleSet.to_dict", "kind": "function", "doc": "

Convert into a dict.

\n\n

Returns

\n\n

dict: A dict whose keys are name of the SampleSet's fields.

\n", "signature": "(self, /):", "funcdef": "def"}, "jijmodeling.SampleSet.from_dict": {"fullname": "jijmodeling.SampleSet.from_dict", "modulename": "jijmodeling", "qualname": "SampleSet.from_dict", "kind": "function", "doc": "

Create a SampleSet object from the given dict.

\n\n

If a key is not the name of the SampleSet fields, the value value is ignored.

\n\n

Args

\n\n

dict (dict): A dict.

\n\n

Returns

\n\n

A SampleSet object.

\n", "signature": "(dict):", "funcdef": "def"}, "jijmodeling.SampleSet.to_json": {"fullname": "jijmodeling.SampleSet.to_json", "modulename": "jijmodeling", "qualname": "SampleSet.to_json", "kind": "function", "doc": "

Serialize the SampleSet object into a JSON string.

\n\n

Returns

\n\n

str: A JSON string.

\n\n

Note

\n\n

A numpy array is converted into a list.

\n", "signature": "(self, /):", "funcdef": "def"}, "jijmodeling.SampleSet.from_json": {"fullname": "jijmodeling.SampleSet.from_json", "modulename": "jijmodeling", "qualname": "SampleSet.from_json", "kind": "function", "doc": "

Create a SampleSet object from the JSON string.

\n\n

Args

\n\n

json (str): A JSON string.

\n\n

Returns

\n\n

SampleSet: A SampleSet object.

\n", "signature": "(json):", "funcdef": "def"}, "jijmodeling.SampleSet.to_pandas": {"fullname": "jijmodeling.SampleSet.to_pandas", "modulename": "jijmodeling", "qualname": "SampleSet.to_pandas", "kind": "function", "doc": "

Convert into a pandas DataFrame.

\n\n

Returns

\n\n

pandas.DataFrame: A pandas DataFrame.

\n", "signature": "(self, /):", "funcdef": "def"}, "jijmodeling.SampleSet.to_dense": {"fullname": "jijmodeling.SampleSet.to_dense", "modulename": "jijmodeling", "qualname": "SampleSet.to_dense", "kind": "function", "doc": "

Return a SampleSet whose record is converted into a dense solution format.\nIf the record is already a dense solution format, return itself.

\n\n

Returns

\n\n

SampleSet: A SampleSet object.

\n", "signature": "(self, /):", "funcdef": "def"}, "jijmodeling.SampleSet.feasible": {"fullname": "jijmodeling.SampleSet.feasible", "modulename": "jijmodeling", "qualname": "SampleSet.feasible", "kind": "function", "doc": "

Return a SampleSet with only feasible solutions.\nIf there is no feasible solution, the record and evaluation are empty.

\n\n

Args

\n\n
    \n
  • rtol (float): The relative tolerance parameter. Defaults to 1e-5.
    • \n
  • atol (float): The absolute tolerance parameter. Defaults to 1e-8.
    • \n
\n\n

Returns

\n\n

SampleSet: A SampleSet object with only feasible solutions or empty.

\n\n

Note

\n\n

The feasible solutions are determined by the following condition:\n$$ |0 - v| \\leq \\mathrm{atol} + \\mathrm{rtol} \\cdot |v| $$

\n", "signature": "(self, /, rtol=1e-05, atol=1e-08):", "funcdef": "def"}, "jijmodeling.SampleSet.infeasible": {"fullname": "jijmodeling.SampleSet.infeasible", "modulename": "jijmodeling", "qualname": "SampleSet.infeasible", "kind": "function", "doc": "

Return a SampleSet with only infeasible solutions.\nIf there is no infeasible solution, the record and evaluation are empty.

\n\n

Args

\n\n
    \n
  • rtol (float): The relative tolerance parameter. Defaults to 1e-5.
    • \n
  • atol (float): The absolute tolerance parameter. Defaults to 1e-8.
    • \n
\n\n

Returns

\n\n

SampleSet: A SampleSet object with only infeasible solutions or empty.

\n\n

Note

\n\n

The feasible solutions are determined by the following condition:\n$$ |0 - v| > \\mathrm{atol} + \\mathrm{rtol} \\cdot |v| $$

\n", "signature": "(self, /, rtol=1e-05, atol=1e-08):", "funcdef": "def"}, "jijmodeling.SampleSet.lowest": {"fullname": "jijmodeling.SampleSet.lowest", "modulename": "jijmodeling", "qualname": "SampleSet.lowest", "kind": "function", "doc": "

Return a SampleSet with feasible solutions which has the lowest objective.\nIf there is no feasible solution, the record and evaluation are empty.

\n\n

Args

\n\n
    \n
  • rtol (float): The relative tolerance parameter. Defaults to 1e-5.
    • \n
  • atol (float): The absolute tolerance parameter. Defaults to 1e-8.
    • \n
\n\n

Returns

\n\n

SampleSet: A SampleSet object with feasible solutions or empty.

\n\n

Note

\n\n

The feasible solutions are determined by the following condition:\n$$ |0 - v| \\leq \\mathrm{atol} + \\mathrm{rtol} \\cdot |v| $$

\n", "signature": "(self, /, rtol=1e-05, atol=1e-08):", "funcdef": "def"}, "jijmodeling.SampleSet.is_dense": {"fullname": "jijmodeling.SampleSet.is_dense", "modulename": "jijmodeling", "qualname": "SampleSet.is_dense", "kind": "function", "doc": "

Return true if the solution is dense.

\n\n

Returns

\n\n

bool: True if the solution is dense.

\n", "signature": "(self, /):", "funcdef": "def"}, "jijmodeling.SampleSet.is_sparse": {"fullname": "jijmodeling.SampleSet.is_sparse", "modulename": "jijmodeling", "qualname": "SampleSet.is_sparse", "kind": "function", "doc": "

Return true if the solution is sparse.

\n\n

Returns

\n\n

bool: True if the solution is sparse.

\n", "signature": "(self, /):", "funcdef": "def"}, "jijmodeling.SampleSet.get_backend_calculation_time": {"fullname": "jijmodeling.SampleSet.get_backend_calculation_time", "modulename": "jijmodeling", "qualname": "SampleSet.get_backend_calculation_time", "kind": "function", "doc": "

Return report of the calculation time of the JijZept backends.

\n\n

Returns

\n\n

dict: A dictionary of the calculation time of the JijZept backends.

\n", "signature": "(self, /):", "funcdef": "def"}, "jijmodeling.SampleSet.measuring_time": {"fullname": "jijmodeling.SampleSet.measuring_time", "modulename": "jijmodeling", "qualname": "SampleSet.measuring_time", "kind": "variable", "doc": "

\n"}, "jijmodeling.SampleSet.metadata": {"fullname": "jijmodeling.SampleSet.metadata", "modulename": "jijmodeling", "qualname": "SampleSet.metadata", "kind": "variable", "doc": "

\n"}, "jijmodeling.SampleSet.record": {"fullname": "jijmodeling.SampleSet.record", "modulename": "jijmodeling", "qualname": "SampleSet.record", "kind": "variable", "doc": "

\n"}, "jijmodeling.SampleSet.evaluation": {"fullname": "jijmodeling.SampleSet.evaluation", "modulename": "jijmodeling", "qualname": "SampleSet.evaluation", "kind": "variable", "doc": "

\n"}, "jijmodeling.Interpreter": {"fullname": "jijmodeling.Interpreter", "modulename": "jijmodeling", "qualname": "Interpreter", "kind": "class", "doc": "

Interpreter of the JijModeling AST

\n\n

This class is responsible for

\n\n
    \n
  • Creating OMMX instance from the AST.\n
      \n
    • This means this module also has responsible to register decision variable ID for each decision variables in AST.
      • \n
    • \n
  • Manage instance data to be substituted into the Placeholder.
    • \n
\n\n

Examples

\n\n
\n
>>> import jijmodeling as jm\n>>> import numpy as np\n\nCreate a new interpreter with scalar instance data\n>>> interpreter = jm.Interpreter({"a": 1})\n\nInsert instance data after creating the interpreter\n>>> interpreter.insert_instance_data("b", 2) # scalar\n\nPython list and numpy array are supported\n>>> interpreter.insert_instance_data("c", [3, 4])\n>>> interpreter.insert_instance_data("d", [[1, 2], [3, 4]])\n>>> interpreter.insert_instance_data("e", np.array([3, 4]))\n\nJaggedArray, non-uniform multi-dimensional array is also supported\n>>> interpreter.insert_instance_data("f", [[[1, 2]], [[3, 4, 5]], [[6]]])\n>>> interpreter.insert_instance_data("g", jm.JaggedArray([[[1, 2]], [[3, 4, 5]], [[6]]]))\n\nYou can get the instance data by using get_instance_data method\n>>> interpreter.get_instance_data("a")\n1.0\n\nArray are normalized to numpy array\n>>> interpreter.get_instance_data("c")\narray([3., 4.])\n>>> interpreter.get_instance_data("e")\narray([3., 4.])\n>>> interpreter.get_instance_data("f")  # doctest: +ELLIPSIS\n<jijmodeling.JaggedArray object at 0x...>\n\nUpdate instance data and re-evaluate expressions\n>>> a = jm.Placeholder("a")\n>>> x = jm.BinaryVar("x")\n>>> interpreter.eval_expr(a * x)  # Original evaluation with a = 1\nFunction(x0)\n>>> interpreter.insert_instance_data("a", 3)  # Update value of 'a'\n>>> interpreter.eval_expr(a * x)  # Re-evaluate with updated value\nFunction(3*x0)\n
\n
\n"}, "jijmodeling.Interpreter.insert_instance_data": {"fullname": "jijmodeling.Interpreter.insert_instance_data", "modulename": "jijmodeling", "qualname": "Interpreter.insert_instance_data", "kind": "function", "doc": "

\n", "signature": "(self, /, key, value):", "funcdef": "def"}, "jijmodeling.Interpreter.get_instance_data": {"fullname": "jijmodeling.Interpreter.get_instance_data", "modulename": "jijmodeling", "qualname": "Interpreter.get_instance_data", "kind": "function", "doc": "

\n", "signature": "(self, /, key):", "funcdef": "def"}, "jijmodeling.Interpreter.instance_data": {"fullname": "jijmodeling.Interpreter.instance_data", "modulename": "jijmodeling", "qualname": "Interpreter.instance_data", "kind": "function", "doc": "

\n", "signature": "(self, /):", "funcdef": "def"}, "jijmodeling.Interpreter.get_decision_variable_by_id": {"fullname": "jijmodeling.Interpreter.get_decision_variable_by_id", "modulename": "jijmodeling", "qualname": "Interpreter.get_decision_variable_by_id", "kind": "function", "doc": "

\n", "signature": "(self, /, id):", "funcdef": "def"}, "jijmodeling.Interpreter.get_decision_variable_by_name": {"fullname": "jijmodeling.Interpreter.get_decision_variable_by_name", "modulename": "jijmodeling", "qualname": "Interpreter.get_decision_variable_by_name", "kind": "function", "doc": "

\n", "signature": "(self, /, name, subscript=None):", "funcdef": "def"}, "jijmodeling.Interpreter.eval_scalar": {"fullname": "jijmodeling.Interpreter.eval_scalar", "modulename": "jijmodeling", "qualname": "Interpreter.eval_scalar", "kind": "function", "doc": "

\n", "signature": "(self, /, expr):", "funcdef": "def"}, "jijmodeling.Interpreter.eval_expr": {"fullname": "jijmodeling.Interpreter.eval_expr", "modulename": "jijmodeling", "qualname": "Interpreter.eval_expr", "kind": "function", "doc": "

\n", "signature": "(self, /, expr):", "funcdef": "def"}, "jijmodeling.Interpreter.eval_constraints": {"fullname": "jijmodeling.Interpreter.eval_constraints", "modulename": "jijmodeling", "qualname": "Interpreter.eval_constraints", "kind": "function", "doc": "

\n", "signature": "(self, /, constraints):", "funcdef": "def"}, "jijmodeling.Interpreter.eval_problem": {"fullname": "jijmodeling.Interpreter.eval_problem", "modulename": "jijmodeling", "qualname": "Interpreter.eval_problem", "kind": "function", "doc": "

Evaluate the problem and returns ommx.v1.Instance.

\n\n

Args

\n\n
    \n
  • problem: a jijmodeling.Problem object to compile.
    • \n
  • hints (optional): a list of constraint hints to be detected during evaluation. When omitted or given None, the default constraint hints are used. Pass empty list to disable hint detection entirely.
    • \n
\n\n

Returns

\n\n

instance: an OMMX v1 instance object.

\n", "signature": "(self, /, problem, hints=None):", "funcdef": "def"}, "jijmodeling.Interpreter.num_decision_variables": {"fullname": "jijmodeling.Interpreter.num_decision_variables", "modulename": "jijmodeling", "qualname": "Interpreter.num_decision_variables", "kind": "variable", "doc": "

\n"}, "jijmodeling.JaggedArray": {"fullname": "jijmodeling.JaggedArray", "modulename": "jijmodeling", "qualname": "JaggedArray", "kind": "class", "doc": "

Jagged array, a multi-dimensional array where each element can be an array of different length.

\n\n

Examples

\n\n
\n
>>> import jijmodeling as jm\n>>> arr = jm.JaggedArray([[[1, 2], [3, 4, 5]], [[6]]])\n\n# Three dimensional\n>>> assert arr.dim == 3\n\n# __getitem__ works\n>>> assert arr[0, 0, 0] == 1.0\n>>> assert arr[0, 0, 1] == 2.0\n>>> assert arr[0, 1, 0] == 3.0\n>>> assert arr[0, 1, 1] == 4.0\n>>> assert arr[0, 1, 2] == 5.0\n>>> assert arr[1, 0, 0] == 6.0\n\n# out of range\n>>> arr[0, 0, 2]\nTraceback (most recent call last):\n ...\nIndexError: Invalid index\n\n# dimension mismatch\n>>> arr[0, 0]\nTraceback (most recent call last):\n ...\nIndexError: Invalid index\n\n>>> assert arr.size_at([]) == 2  # [[1, 2], [3, 4, 5]] and [[6]]\n>>> assert arr.size_at([0]) == 2  # [1, 2] and [3, 4, 5]\n>>> assert arr.size_at([1]) == 1  # [[6]]\n>>> assert arr.size_at([0, 0]) == 2  # [1, 2]\n>>> assert arr.size_at([0, 1]) == 3  # [3, 4, 5]\n>>> assert arr.size_at([1, 0]) == 1  # [6]\n
\n
\n"}, "jijmodeling.JaggedArray.get": {"fullname": "jijmodeling.JaggedArray.get", "modulename": "jijmodeling", "qualname": "JaggedArray.get", "kind": "function", "doc": "

\n", "signature": "(self, /, index):", "funcdef": "def"}, "jijmodeling.JaggedArray.size_at": {"fullname": "jijmodeling.JaggedArray.size_at", "modulename": "jijmodeling", "qualname": "JaggedArray.size_at", "kind": "function", "doc": "

\n", "signature": "(self, /, index):", "funcdef": "def"}, "jijmodeling.JaggedArray.dim": {"fullname": "jijmodeling.JaggedArray.dim", "modulename": "jijmodeling", "qualname": "JaggedArray.dim", "kind": "variable", "doc": "

\n"}, "jijmodeling.dataset": {"fullname": "jijmodeling.dataset", "modulename": "jijmodeling.dataset", "kind": "module", "doc": "

\n"}, "jijmodeling.dataset.Miplib": {"fullname": "jijmodeling.dataset.Miplib", "modulename": "jijmodeling.dataset", "qualname": "Miplib", "kind": "class", "doc": "

Automatically load problems from the MIPLIB dataset.

\n\n

Warning: the whole dataset is downloaded and cached in your filesystem when\nfirst used. It is quite large (~300MB download, ~600MB extracted), so the\ntime it takes to download will likely be significant. We recommend first\ntrying to use this class in a REPL to assure the dataset has been cached.

\n\n

The dataset is stored at {data_dir}/jijmodeling/miplib, where\n{data_dir} depends on your operating system:

\n\n\n\n\n \n \n \n\n\n\n\n \n \n\n\n \n \n \n\n\n \n \n \n\n\n
Platform{data_dir} locationExample/System Default
Linux$XDG_DATA_HOME or $HOME/.local/share|/home/alice/.local/share
macOS$HOME/Library/Application Support/Users/Alice/Library/Application Support
Windows`%AppData%C:\\Users\\Alice\\AppData\\Roaming
\n\n

Examples

\n\n
\n
>>> import jijmodeling.dataset\n\nInitialize the dataset. If not cached in your filesystem, this will take several minutes.\n>>> miplib = jijmodeling.dataset.Miplib()\n\nShow names of each available instance.\n>>> miplib.available_names  # doctest: +ELLIPSIS\n[...]\n\nGet basic statistics of a specific instance.\n>>> miplib.instance_statistics["unitcal_7"]\n{'variable': 25755, 'constraint': 48939, 'binary': 2856, 'integer': 0, 'continuous': 22899, 'non_zero': 127595}\n\nLoad "revised-submissions/miplib2010_pg/instances/pg.mps.gz" in benchmark.zip\n>>> problem, instance_data = miplib.load("pg")\n
\n
\n"}, "jijmodeling.dataset.Miplib.load": {"fullname": "jijmodeling.dataset.Miplib.load", "modulename": "jijmodeling.dataset", "qualname": "Miplib.load", "kind": "function", "doc": "

Load a problem from the MIPLIB dataset.

\n", "signature": "(self, /, name):", "funcdef": "def"}, "jijmodeling.dataset.Miplib.available_names": {"fullname": "jijmodeling.dataset.Miplib.available_names", "modulename": "jijmodeling.dataset", "qualname": "Miplib.available_names", "kind": "variable", "doc": "

List of available problem names.

\n"}, "jijmodeling.dataset.Miplib.instance_statistics": {"fullname": "jijmodeling.dataset.Miplib.instance_statistics", "modulename": "jijmodeling.dataset", "qualname": "Miplib.instance_statistics", "kind": "variable", "doc": "

Dict of name to problem size, i.e. number of binary/integer/real variable and number of equality/inequality constraints

\n"}, "jijmodeling.dataset.Qplib": {"fullname": "jijmodeling.dataset.Qplib", "modulename": "jijmodeling.dataset", "qualname": "Qplib", "kind": "class", "doc": "

Automatically load problems from the MIPLIB dataset.

\n\n

Warning: the whole dataset is downloaded and cached in your filesystem when\nfirst used. It is quite large (~700MB download, ~1.5GB extracted), so the\ntime it takes to download will likely be significant. We recommend first\ntrying to use this class in a REPL to assure the dataset has been cached.

\n\n

The dataset is stored at {data_dir}/jijmodeling/qplib, where\n{data_dir} depends on your operating system:

\n\n\n\n\n \n \n \n\n\n\n\n \n \n\n\n \n \n \n\n\n \n \n \n\n\n
Platform{data_dir} locationExample/System Default
Linux$XDG_DATA_HOME or $HOME/.local/share|/home/alice/.local/share
macOS$HOME/Library/Application Support/Users/Alice/Library/Application Support
Windows`%AppData%C:\\Users\\Alice\\AppData\\Roaming
\n\n

Examples

\n\n
\n
>>> import jijmodeling.dataset\n\nInitialize the dataset. If not cached in your filesystem, this will take several minutes.\n>>> qplib = jijmodeling.dataset.Qplib()\n\nShow the names of each available instance.\n>>> qplib.available_names  # doctest: +ELLIPSIS\n[...]\n\nGet basic statistics of a specific instance.\n>>> qplib.instance_statistics["QPLIB_2205"]\n{'variables': 2884, 'constraints': 2874, 'binary': 958, 'integer': 0, 'continuous': 1926, 'non_zero': 13013}\n\nLoad a problem instance from QPLIB.\n>>> problem, instance_data = qplib.load("QPLIB_1976")\n
\n
\n"}, "jijmodeling.dataset.Qplib.load": {"fullname": "jijmodeling.dataset.Qplib.load", "modulename": "jijmodeling.dataset", "qualname": "Qplib.load", "kind": "function", "doc": "

Load a problem from the QPLIB dataset.

\n", "signature": "(self, /, name):", "funcdef": "def"}, "jijmodeling.dataset.Qplib.instance_statistics": {"fullname": "jijmodeling.dataset.Qplib.instance_statistics", "modulename": "jijmodeling.dataset", "qualname": "Qplib.instance_statistics", "kind": "variable", "doc": "

Dict of name to problem size, i.e. number of binary/integer/real variable and number of equality/inequality constraints

\n"}, "jijmodeling.dataset.Qplib.available_names": {"fullname": "jijmodeling.dataset.Qplib.available_names", "modulename": "jijmodeling.dataset", "qualname": "Qplib.available_names", "kind": "variable", "doc": "

List of available problem names.

\n"}, "jijmodeling.experimental": {"fullname": "jijmodeling.experimental", "modulename": "jijmodeling.experimental", "kind": "module", "doc": "

\n"}, "jijmodeling.experimental.Violation": {"fullname": "jijmodeling.experimental.Violation", "modulename": "jijmodeling.experimental", "qualname": "Violation", "kind": "class", "doc": "

\n"}, "jijmodeling.experimental.Violation.name": {"fullname": "jijmodeling.experimental.Violation.name", "modulename": "jijmodeling.experimental", "qualname": "Violation.name", "kind": "variable", "doc": "

\n"}, "jijmodeling.experimental.Violation.expr_values": {"fullname": "jijmodeling.experimental.Violation.expr_values", "modulename": "jijmodeling.experimental", "qualname": "Violation.expr_values", "kind": "variable", "doc": "

\n"}, "jijmodeling.experimental.Violation.total_violation": {"fullname": "jijmodeling.experimental.Violation.total_violation", "modulename": "jijmodeling.experimental", "qualname": "Violation.total_violation", "kind": "variable", "doc": "

\n"}, "jijmodeling.experimental.EvaluationResult": {"fullname": "jijmodeling.experimental.EvaluationResult", "modulename": "jijmodeling.experimental", "qualname": "EvaluationResult", "kind": "class", "doc": "

\n"}, "jijmodeling.experimental.EvaluationResult.penalties": {"fullname": "jijmodeling.experimental.EvaluationResult.penalties", "modulename": "jijmodeling.experimental", "qualname": "EvaluationResult.penalties", "kind": "variable", "doc": "

\n"}, "jijmodeling.experimental.EvaluationResult.objective": {"fullname": "jijmodeling.experimental.EvaluationResult.objective", "modulename": "jijmodeling.experimental", "qualname": "EvaluationResult.objective", "kind": "variable", "doc": "

\n"}, "jijmodeling.experimental.EvaluationResult.constraints": {"fullname": "jijmodeling.experimental.EvaluationResult.constraints", "modulename": "jijmodeling.experimental", "qualname": "EvaluationResult.constraints", "kind": "variable", "doc": "

\n"}, "jijmodeling.experimental.VarType": {"fullname": "jijmodeling.experimental.VarType", "modulename": "jijmodeling.experimental", "qualname": "VarType", "kind": "class", "doc": "

\n"}, "jijmodeling.experimental.VarType.CONTINUOUS": {"fullname": "jijmodeling.experimental.VarType.CONTINUOUS", "modulename": "jijmodeling.experimental", "qualname": "VarType.CONTINUOUS", "kind": "variable", "doc": "

\n", "default_value": "VarType.CONTINUOUS"}, "jijmodeling.experimental.VarType.INTEGER": {"fullname": "jijmodeling.experimental.VarType.INTEGER", "modulename": "jijmodeling.experimental", "qualname": "VarType.INTEGER", "kind": "variable", "doc": "

\n", "default_value": "VarType.INTEGER"}, "jijmodeling.experimental.VarType.BINARY": {"fullname": "jijmodeling.experimental.VarType.BINARY", "modulename": "jijmodeling.experimental", "qualname": "VarType.BINARY", "kind": "variable", "doc": "

\n", "default_value": "VarType.BINARY"}, "jijmodeling.experimental.SparseVarValues": {"fullname": "jijmodeling.experimental.SparseVarValues", "modulename": "jijmodeling.experimental", "qualname": "SparseVarValues", "kind": "class", "doc": "

\n"}, "jijmodeling.experimental.SparseVarValues.from_array": {"fullname": "jijmodeling.experimental.SparseVarValues.from_array", "modulename": "jijmodeling.experimental", "qualname": "SparseVarValues.from_array", "kind": "function", "doc": "

\n", "signature": "(name, array, var_type=Ellipsis):", "funcdef": "def"}, "jijmodeling.experimental.SparseVarValues.to_dense": {"fullname": "jijmodeling.experimental.SparseVarValues.to_dense", "modulename": "jijmodeling.experimental", "qualname": "SparseVarValues.to_dense", "kind": "function", "doc": "

\n", "signature": "(self, /):", "funcdef": "def"}, "jijmodeling.experimental.SparseVarValues.name": {"fullname": "jijmodeling.experimental.SparseVarValues.name", "modulename": "jijmodeling.experimental", "qualname": "SparseVarValues.name", "kind": "variable", "doc": "

\n"}, "jijmodeling.experimental.SparseVarValues.values": {"fullname": "jijmodeling.experimental.SparseVarValues.values", "modulename": "jijmodeling.experimental", "qualname": "SparseVarValues.values", "kind": "variable", "doc": "

\n"}, "jijmodeling.experimental.SparseVarValues.var_type": {"fullname": "jijmodeling.experimental.SparseVarValues.var_type", "modulename": "jijmodeling.experimental", "qualname": "SparseVarValues.var_type", "kind": "variable", "doc": "

\n"}, "jijmodeling.experimental.SparseVarValues.shape": {"fullname": "jijmodeling.experimental.SparseVarValues.shape", "modulename": "jijmodeling.experimental", "qualname": "SparseVarValues.shape", "kind": "variable", "doc": "

\n"}, "jijmodeling.experimental.SolvingTime": {"fullname": "jijmodeling.experimental.SolvingTime", "modulename": "jijmodeling.experimental", "qualname": "SolvingTime", "kind": "class", "doc": "

\n"}, "jijmodeling.experimental.SolvingTime.total": {"fullname": "jijmodeling.experimental.SolvingTime.total", "modulename": "jijmodeling.experimental", "qualname": "SolvingTime.total", "kind": "function", "doc": "

\n", "signature": "(self, /):", "funcdef": "def"}, "jijmodeling.experimental.SolvingTime.transpiling_time": {"fullname": "jijmodeling.experimental.SolvingTime.transpiling_time", "modulename": "jijmodeling.experimental", "qualname": "SolvingTime.transpiling_time", "kind": "variable", "doc": "

\n"}, "jijmodeling.experimental.SolvingTime.solving_time": {"fullname": "jijmodeling.experimental.SolvingTime.solving_time", "modulename": "jijmodeling.experimental", "qualname": "SolvingTime.solving_time", "kind": "variable", "doc": "

\n"}, "jijmodeling.experimental.SolvingTime.compiling_time": {"fullname": "jijmodeling.experimental.SolvingTime.compiling_time", "modulename": "jijmodeling.experimental", "qualname": "SolvingTime.compiling_time", "kind": "variable", "doc": "

\n"}, "jijmodeling.experimental.SolvingTime.preprocess_time": {"fullname": "jijmodeling.experimental.SolvingTime.preprocess_time", "modulename": "jijmodeling.experimental", "qualname": "SolvingTime.preprocess_time", "kind": "variable", "doc": "

\n"}, "jijmodeling.experimental.SolvingTime.decoding_time": {"fullname": "jijmodeling.experimental.SolvingTime.decoding_time", "modulename": "jijmodeling.experimental", "qualname": "SolvingTime.decoding_time", "kind": "variable", "doc": "

\n"}, "jijmodeling.experimental.SolvingTime.postprocess_time": {"fullname": "jijmodeling.experimental.SolvingTime.postprocess_time", "modulename": "jijmodeling.experimental", "qualname": "SolvingTime.postprocess_time", "kind": "variable", "doc": "

\n"}, "jijmodeling.experimental.SystemTime": {"fullname": "jijmodeling.experimental.SystemTime", "modulename": "jijmodeling.experimental", "qualname": "SystemTime", "kind": "class", "doc": "

\n"}, "jijmodeling.experimental.SystemTime.request_queuing_time": {"fullname": "jijmodeling.experimental.SystemTime.request_queuing_time", "modulename": "jijmodeling.experimental", "qualname": "SystemTime.request_queuing_time", "kind": "variable", "doc": "

\n"}, "jijmodeling.experimental.SystemTime.posting_time": {"fullname": "jijmodeling.experimental.SystemTime.posting_time", "modulename": "jijmodeling.experimental", "qualname": "SystemTime.posting_time", "kind": "variable", "doc": "

\n"}, "jijmodeling.experimental.SystemTime.fetching_problem_time": {"fullname": "jijmodeling.experimental.SystemTime.fetching_problem_time", "modulename": "jijmodeling.experimental", "qualname": "SystemTime.fetching_problem_time", "kind": "variable", "doc": "

\n"}, "jijmodeling.experimental.SystemTime.fetching_result_time": {"fullname": "jijmodeling.experimental.SystemTime.fetching_result_time", "modulename": "jijmodeling.experimental", "qualname": "SystemTime.fetching_result_time", "kind": "variable", "doc": "

\n"}, "jijmodeling.experimental.SystemTime.deserialize_time": {"fullname": "jijmodeling.experimental.SystemTime.deserialize_time", "modulename": "jijmodeling.experimental", "qualname": "SystemTime.deserialize_time", "kind": "variable", "doc": "

\n"}, "jijmodeling.experimental.MeasuringTime": {"fullname": "jijmodeling.experimental.MeasuringTime", "modulename": "jijmodeling.experimental", "qualname": "MeasuringTime", "kind": "class", "doc": "

\n"}, "jijmodeling.experimental.MeasuringTime.total": {"fullname": "jijmodeling.experimental.MeasuringTime.total", "modulename": "jijmodeling.experimental", "qualname": "MeasuringTime.total", "kind": "function", "doc": "

Returns the total time measurred, across both SolvingTime and SystemTime.

\n", "signature": "(self, /):", "funcdef": "def"}, "jijmodeling.experimental.MeasuringTime.view_solving_time": {"fullname": "jijmodeling.experimental.MeasuringTime.view_solving_time", "modulename": "jijmodeling.experimental", "qualname": "MeasuringTime.view_solving_time", "kind": "function", "doc": "

Returns a readonly view of the internal SolvingTime field.

\n", "signature": "(self, /):", "funcdef": "def"}, "jijmodeling.experimental.MeasuringTime.view_system_time": {"fullname": "jijmodeling.experimental.MeasuringTime.view_system_time", "modulename": "jijmodeling.experimental", "qualname": "MeasuringTime.view_system_time", "kind": "function", "doc": "

Returns a readonly view of the internal SystemTime field.

\n", "signature": "(self, /):", "funcdef": "def"}, "jijmodeling.experimental.Sample": {"fullname": "jijmodeling.experimental.Sample", "modulename": "jijmodeling.experimental", "qualname": "Sample", "kind": "class", "doc": "

A Sample representing an individual solution found by running the mathematical optimization model.

\n\n

Variables in var_values are stored in instances of SparseVarValues. This uses a dictionary\nstyle, retaining only non-zero elements. For example, if the values for a two-dimensional\ndecision variable are x = [[0, 1, 2], [1, 0, 0]], they will be stored as\n{(0,1): 1, (0,2): 2, (1,0): 1}. To retrieve this, use sample.var_values[\"x\"].values.\nIf you want a dense array of decision variables, you can use the to_dense() method.

\n\n

run_id is a unique identifier of the run in which this sample was found.\nNote that this is not the same as a unique identifier of the Sample.

\n"}, "jijmodeling.experimental.Sample.from_dense_arrays": {"fullname": "jijmodeling.experimental.Sample.from_dense_arrays", "modulename": "jijmodeling.experimental", "qualname": "Sample.from_dense_arrays", "kind": "function", "doc": "

\n", "signature": "(dict, num_occurrences=1, var_types=None, run_id=None, meta_info=None):", "funcdef": "def"}, "jijmodeling.experimental.Sample.is_feasible": {"fullname": "jijmodeling.experimental.Sample.is_feasible", "modulename": "jijmodeling.experimental", "qualname": "Sample.is_feasible", "kind": "function", "doc": "

\n", "signature": "(self, /, epsilon=1e-08):", "funcdef": "def"}, "jijmodeling.experimental.Sample.to_dense": {"fullname": "jijmodeling.experimental.Sample.to_dense", "modulename": "jijmodeling.experimental", "qualname": "Sample.to_dense", "kind": "function", "doc": "

\n", "signature": "(self, /):", "funcdef": "def"}, "jijmodeling.experimental.Sample.from_dict": {"fullname": "jijmodeling.experimental.Sample.from_dict", "modulename": "jijmodeling.experimental", "qualname": "Sample.from_dict", "kind": "function", "doc": "

Converts a python dictionary into a SampleSet.

\n\n

This is intended to be used primarily with dictionaries generated by\nthe to_dict() method. As such sparse value maps must be represented as\nassociation lists.

\n", "signature": "(dict):", "funcdef": "def"}, "jijmodeling.experimental.Sample.to_dict": {"fullname": "jijmodeling.experimental.Sample.to_dict", "modulename": "jijmodeling.experimental", "qualname": "Sample.to_dict", "kind": "function", "doc": "

Converts this SampleSet into a regular python dictionary.

\n\n

Note that this dictionary has a slightly different structure to better support JSON\nserialization of the output dictionary: sparse values are stored differently. Any mapping\nwith tuples as keys is transformed into an association list of key-value pairs,\nthat is, [(k1, v1), (k2, v2), ...].

\n", "signature": "(self, /):", "funcdef": "def"}, "jijmodeling.experimental.Sample.eval": {"fullname": "jijmodeling.experimental.Sample.eval", "modulename": "jijmodeling.experimental", "qualname": "Sample.eval", "kind": "variable", "doc": "

\n"}, "jijmodeling.experimental.Sample.var_values": {"fullname": "jijmodeling.experimental.Sample.var_values", "modulename": "jijmodeling.experimental", "qualname": "Sample.var_values", "kind": "variable", "doc": "

\n"}, "jijmodeling.experimental.Sample.run_info": {"fullname": "jijmodeling.experimental.Sample.run_info", "modulename": "jijmodeling.experimental", "qualname": "Sample.run_info", "kind": "variable", "doc": "

\n"}, "jijmodeling.experimental.Sample.run_id": {"fullname": "jijmodeling.experimental.Sample.run_id", "modulename": "jijmodeling.experimental", "qualname": "Sample.run_id", "kind": "variable", "doc": "

\n"}, "jijmodeling.experimental.Sample.num_occurrences": {"fullname": "jijmodeling.experimental.Sample.num_occurrences", "modulename": "jijmodeling.experimental", "qualname": "Sample.num_occurrences", "kind": "variable", "doc": "

\n"}, "jijmodeling.experimental.SampleSet": {"fullname": "jijmodeling.experimental.SampleSet", "modulename": "jijmodeling.experimental", "qualname": "SampleSet", "kind": "class", "doc": "

\n"}, "jijmodeling.experimental.SampleSet.from_array": {"fullname": "jijmodeling.experimental.SampleSet.from_array", "modulename": "jijmodeling.experimental", "qualname": "SampleSet.from_array", "kind": "function", "doc": "

Builds a SampleSet from a list of dictionaries, where each entry is interpreted as a sample.

\n\n

Args

\n\n
    \n
  • samples(list[dict[str, list | numpy.ndarray]])
    • \n
\n", "signature": "(samples):", "funcdef": "def"}, "jijmodeling.experimental.SampleSet.concat": {"fullname": "jijmodeling.experimental.SampleSet.concat", "modulename": "jijmodeling.experimental", "qualname": "SampleSet.concat", "kind": "function", "doc": "

Creates a single SampleSet by concatenating a list of multiple SampleSets.

\n\n

Args

\n\n
    \n
  • family (list[SampleSet])
    • \n
\n", "signature": "(family):", "funcdef": "def"}, "jijmodeling.experimental.SampleSet.from_dict": {"fullname": "jijmodeling.experimental.SampleSet.from_dict", "modulename": "jijmodeling.experimental", "qualname": "SampleSet.from_dict", "kind": "function", "doc": "

Converts a python dictionary into a SampleSet.

\n\n

This is intended to be used primarily with dictionaries generated by\nthe to_dict() method. As such sparse value maps must be represented as\nassociation lists.

\n", "signature": "(dict):", "funcdef": "def"}, "jijmodeling.experimental.SampleSet.to_dict": {"fullname": "jijmodeling.experimental.SampleSet.to_dict", "modulename": "jijmodeling.experimental", "qualname": "SampleSet.to_dict", "kind": "function", "doc": "

Converts this SampleSet into a regular python dictionary.

\n\n

Note that this dictionary has a slightly different structure to better support JSON\nserialization of the output dictionary: sparse values are stored differently. Any mapping\nwith tuples as keys is transformed into an association list of key-value pairs,\nthat is, [(k1, v1), (k2, v2), ...].

\n", "signature": "(self, /):", "funcdef": "def"}, "jijmodeling.experimental.SampleSet.feasibles": {"fullname": "jijmodeling.experimental.SampleSet.feasibles", "modulename": "jijmodeling.experimental", "qualname": "SampleSet.feasibles", "kind": "function", "doc": "

Returns a SampleSet containing only the feasible samples.

\n\n

Args

\n\n
    \n
  • epsilon (float, optional): Tolerance threshold for constraint violations. Defaults to $1e-8$.
    • \n
\n\n

Returns

\n\n
    \n
  • SampleSet: a feasible subset of the current set.
    • \n
\n", "signature": "(self, /, epsilon=1e-08):", "funcdef": "def"}, "jijmodeling.experimental.SampleSet.separate": {"fullname": "jijmodeling.experimental.SampleSet.separate", "modulename": "jijmodeling.experimental", "qualname": "SampleSet.separate", "kind": "function", "doc": "

Splits this SampleSet based on the run_id of the samples.

\n\n

In other words, for each distinct run_id among the Samples contained in this instance, a\nnew SampleSet is created to store all Samples with that ID.

\n\n

Returns

\n\n
    \n
  • sets (dict[str, SampleSet]): The separated SampleSets. Keys are the run IDs.
    • \n
\n", "signature": "(self, /):", "funcdef": "def"}, "jijmodeling.experimental.SampleSet.lowest": {"fullname": "jijmodeling.experimental.SampleSet.lowest", "modulename": "jijmodeling.experimental", "qualname": "SampleSet.lowest", "kind": "function", "doc": "

Returns a list of the feasible samples which have the lowest objective value.\nIf there are no feasible solutions, this returns an empty list.

\n\n

Args

\n\n
    \n
  • epsilon (float, optional): Tolerance threshold. Objective values within this tolerance are included, even if not exactly the minimum value. Defaults to $1e-8$.
    • \n
\n\n

Returns

\n\n

lowest_samples: A list of Sample objects with the lowest ojective value in this SampleSet.

\n", "signature": "(self, /, epsilon=1e-08):", "funcdef": "def"}, "jijmodeling.experimental.SampleSet.set_info": {"fullname": "jijmodeling.experimental.SampleSet.set_info", "modulename": "jijmodeling.experimental", "qualname": "SampleSet.set_info", "kind": "variable", "doc": "

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\n"}, "jijmodeling.experimental.SampleSet.measuring_time": {"fullname": "jijmodeling.experimental.SampleSet.measuring_time", "modulename": "jijmodeling.experimental", "qualname": "SampleSet.measuring_time", "kind": "variable", "doc": "

\n"}, "jijmodeling.experimental.SampleSet.set_id": {"fullname": "jijmodeling.experimental.SampleSet.set_id", "modulename": "jijmodeling.experimental", "qualname": "SampleSet.set_id", "kind": "variable", "doc": "

\n"}, "jijmodeling.experimental.SampleSet.run_times": {"fullname": "jijmodeling.experimental.SampleSet.run_times", "modulename": "jijmodeling.experimental", "qualname": "SampleSet.run_times", "kind": "variable", "doc": "

\n"}, "jijmodeling.range": {"fullname": "jijmodeling.range", "modulename": "jijmodeling.range", "kind": "module", "doc": "

\n"}, "jijmodeling.range.size": {"fullname": "jijmodeling.range.size", "modulename": "jijmodeling.range.size", "kind": "module", "doc": "

\n"}, "jijmodeling.range.value": {"fullname": "jijmodeling.range.value", "modulename": "jijmodeling.range.value", "kind": "module", "doc": "

\n"}}, "docInfo": {"jijmodeling": {"qualname": 0, "fullname": 1, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "jijmodeling.ModelingError": {"qualname": 1, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 2, "doc": 8}, "jijmodeling.InterpreterError": {"qualname": 1, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 2, "doc": 8}, "jijmodeling.ProtobufSerializationError": {"qualname": 1, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 2, "doc": 11}, "jijmodeling.ProtobufDeserializationError": {"qualname": 1, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 2, "doc": 11}, "jijmodeling.DataType": {"qualname": 1, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "jijmodeling.DataType.FLOAT": {"qualname": 2, "fullname": 3, "annotation": 0, "default_value": 2, "signature": 0, "bases": 0, "doc": 3}, 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this.addField("bases"); - this.addField("doc"); - this.setRef("fullname"); - }); - for (let doc of docs) { - searchIndex.addDoc(doc); - } - console.timeEnd("building search index"); - } - - return (term) => searchIndex.search(term, { - fields: { - qualname: {boost: 4}, - fullname: {boost: 2}, - annotation: {boost: 2}, - default_value: {boost: 2}, - signature: {boost: 2}, - bases: {boost: 2}, - doc: {boost: 1}, - }, - expand: true - }); -})(); \ No newline at end of file From 60456eea911ff661a41de7f0b2d123c65292f7b8 Mon Sep 17 00:00:00 2001 From: Yoshiki Sakamoto Date: Wed, 17 Sep 2025 08:21:36 +0900 Subject: [PATCH 12/18] =?UTF-8?q?dependabot.yml=E3=82=92=E6=96=B0=E8=A6=8F?= =?UTF-8?q?=E4=BD=9C=E6=88=90=E3=81=97=E3=80=81GitHub=20Actions=E3=81=A8pi?= =?UTF-8?q?p=E3=81=AE=E4=BE=9D=E5=AD=98=E9=96=A2=E4=BF=82=E3=81=AE?= =?UTF-8?q?=E6=9B=B4=E6=96=B0=E3=82=B9=E3=82=B1=E3=82=B8=E3=83=A5=E3=83=BC?= =?UTF-8?q?=E3=83=AB=E3=82=92=E8=A8=AD=E5=AE=9A?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .github/dependabot.yml | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) create mode 100644 .github/dependabot.yml diff --git a/.github/dependabot.yml b/.github/dependabot.yml new file mode 100644 index 00000000..0e52b58f --- /dev/null +++ b/.github/dependabot.yml @@ -0,0 +1,18 @@ +version: 2 + +updates: + - package-ecosystem: github-actions + directory: "/" + schedule: + interval: "daily" + allow: + # Allow both direct and indirect updates for all packages + - dependency-type: "all" + + - package-ecosystem: pip + directory: "/" + schedule: + interval: "daily" + allow: + # Allow both direct and indirect updates for all packages + - dependency-type: "all" From ec0a10645161fc050212a50c53a4aa5e926fb076 Mon Sep 17 00:00:00 2001 From: Yoshiki Sakamoto Date: Wed, 17 Sep 2025 11:32:21 +0900 Subject: [PATCH 13/18] =?UTF-8?q?sphinx.ext.mathjax=E3=82=92sphinxcontrib.?= =?UTF-8?q?katex=E3=81=AB=E7=BD=AE=E3=81=8D=E6=8F=9B=E3=81=88=E3=80=81requ?= =?UTF-8?q?irements.txt=E3=81=ABsphinxcontrib-katex=E3=81=AE=E4=BE=9D?= =?UTF-8?q?=E5=AD=98=E9=96=A2=E4=BF=82=E3=82=92=E8=BF=BD=E5=8A=A0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- reference/conf.py | 3 ++- reference/requirements.txt | 1 + 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/reference/conf.py b/reference/conf.py index c845df07..241d4d2a 100644 --- a/reference/conf.py +++ b/reference/conf.py @@ -13,8 +13,9 @@ extensions = [ "sphinx.ext.autodoc", # 自動API生成（標準） "sphinx.ext.intersphinx", # 外部ドキュメントへのリンク - "sphinx.ext.mathjax", # 数式サポート + "sphinxcontrib.katex", # 数式サポート ] +katex_prerender = True # --- Autodoc 設定 --- autodoc_default_options = { diff --git a/reference/requirements.txt b/reference/requirements.txt index 2b001e11..fd4026f1 100644 --- a/reference/requirements.txt +++ b/reference/requirements.txt @@ -1,3 +1,4 @@ jijmodeling==1.13.2 sphinx>=8.0,<9.0 sphinx-rtd-theme>=3.0,<4.0 +sphinxcontrib-katex>=0.9,<0.10 From b0fab4ba2454b801329e386ad01386f70b65a139 Mon Sep 17 00:00:00 2001 From: Yoshiki Sakamoto Date: Wed, 17 Sep 2025 13:13:10 +0900 Subject: [PATCH 14/18] =?UTF-8?q?Node.js=E3=81=AE=E3=83=90=E3=83=BC?= =?UTF-8?q?=E3=82=B8=E3=83=A7=E3=83=B3=E3=82=92=E6=8C=87=E5=AE=9A=E3=81=97?= =?UTF-8?q?=E3=80=81=E5=87=BA=E5=8A=9B=E3=83=95=E3=82=A9=E3=83=BC=E3=83=9E?= =?UTF-8?q?=E3=83=83=E3=83=88=E3=82=92=E3=81=99=E3=81=B9=E3=81=A6=E3=81=AB?= =?UTF-8?q?=E5=A4=89=E6=9B=B4?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- reference/.readthedocs.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/reference/.readthedocs.yaml b/reference/.readthedocs.yaml index 113d1b22..c1cbfaf8 100644 --- a/reference/.readthedocs.yaml +++ b/reference/.readthedocs.yaml @@ -4,7 +4,7 @@ build: os: ubuntu-24.04 tools: python: "3.12" - + nodejs: "22" python: install: - requirements: reference/requirements.txt @@ -12,4 +12,4 @@ python: sphinx: configuration: reference/conf.py -formats: ["htmlzip", "pdf", "epub"] +formats: all From 43ef4768674bf8ef78ff010bb04ca1cd42a07397 Mon Sep 17 00:00:00 2001 From: Yoshiki Sakamoto Date: Fri, 26 Sep 2025 11:45:44 +0900 Subject: [PATCH 15/18] =?UTF-8?q?JijModeling=E3=81=AE=E3=83=91=E3=82=B9?= =?UTF-8?q?=E3=82=92=E5=8B=95=E7=9A=84=E3=81=AB=E5=8F=96=E5=BE=97=E3=81=97?= =?UTF-8?q?=E3=80=81autodoc2=E3=81=AE=E8=A8=AD=E5=AE=9A=E3=82=92=E8=BF=BD?= =?UTF-8?q?=E5=8A=A0=E3=80=82index.rst=E3=81=AE=E5=86=85=E5=AE=B9=E3=82=92?= =?UTF-8?q?=E6=95=B4=E7=90=86=E3=81=97=E3=80=81requirements.txt=E3=81=AE?= =?UTF-8?q?=E4=BE=9D=E5=AD=98=E9=96=A2=E4=BF=82=E3=82=92=E6=9B=B4=E6=96=B0?= =?UTF-8?q?=E3=80=82?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- reference/conf.py | 45 ++++++++++++++++++++++------- reference/index.rst | 59 ++++++-------------------------------- reference/requirements.txt | 7 +++-- 3 files changed, 46 insertions(+), 65 deletions(-) diff --git a/reference/conf.py b/reference/conf.py index 241d4d2a..fcc21060 100644 --- a/reference/conf.py +++ b/reference/conf.py @@ -1,9 +1,24 @@ +import os from datetime import date +# JijModelingのパスを動的に取得 +import jijmodeling + +# 相対パスに変換（Sphinx向け） +jijmodeling_path = os.path.relpath( + os.path.dirname(jijmodeling.__file__), start=os.path.dirname(__file__) +) +jijmodeling_version = jijmodeling.__version__ # type: ignore +print( + f"JijModeling {jijmodeling_version} found at: {jijmodeling_path}, Version: {jijmodeling_version}" +) + # --- 基本 --- project = "JijModeling API" author = "Jij Inc." copyright = f"{date.today().year}, {author}" +version = jijmodeling_version # 上で取得済み +release = version language = "en" # --- テーマ --- @@ -11,22 +26,30 @@ # --- 拡張 --- extensions = [ - "sphinx.ext.autodoc", # 自動API生成（標準） "sphinx.ext.intersphinx", # 外部ドキュメントへのリンク + "autodoc2", # 自動ドキュメント生成 "sphinxcontrib.katex", # 数式サポート + "myst_parser", # Markdownサポート ] katex_prerender = True -# --- Autodoc 設定 --- -autodoc_default_options = { - "members": True, - "undoc-members": True, - "show-inheritance": True, - "inherited-members": True, - "special-members": "__init__", -} -autodoc_member_order = "bysource" -autodoc_docstring_signature = True +# sphinx-autodoc2のパッケージ設定（.pyiファイル優先） +autodoc2_packages = [ + { + "path": jijmodeling_path, # pythonディレクトリのパス + "module": "jijmodeling", # モジュール名 + "exclude_files": ["*.py"], # .pyファイルを除外して.pyiを優先 + } +] + +autodoc2_render_plugin = "myst" # Markdownレンダリングプラグインを指定 + +myst_enable_extensions = [ + "smartquotes", # スマートクォート + "amsmath", # 数式サポート + "dollarmath", # $記法での数式 +] +myst_dmath_double_inline = True # インタースフィンクス（Python 標準ライブラリなどへリンク） intersphinx_mapping = { diff --git a/reference/index.rst b/reference/index.rst index f4d81c13..87d22ad8 100644 --- a/reference/index.rst +++ b/reference/index.rst @@ -3,62 +3,19 @@ JijModeling API Reference **JijModeling** is a mathematical optimization modeling library that provides an intuitive interface for formulating optimization problems. -Core API +Overview ======== -The main ``jijmodeling`` module contains all the essential classes and functions for mathematical optimization modeling. +JijModeling provides a powerful and intuitive way to formulate mathematical optimization problems. This API reference contains comprehensive documentation for all modules, classes, functions, and methods available in the library. -.. automodule:: jijmodeling - :members: - :undoc-members: - :show-inheritance: - :special-members: __init__ - -Dataset Module -============== - -Utilities for handling optimization data. - -.. automodule:: jijmodeling.dataset - :members: - :undoc-members: - :show-inheritance: - -Experimental Features -===================== - -Experimental and preview features. - -.. automodule:: jijmodeling.experimental - :members: - :undoc-members: - :show-inheritance: - -Range Operations -================ - -Tools for working with variable ranges and indices. - -.. automodule:: jijmodeling.range - :members: - :undoc-members: - :show-inheritance: - -Range Size Utilities ---------------------- - -.. automodule:: jijmodeling.range.size - :members: - :undoc-members: - :show-inheritance: +Contents +======== -Range Value Utilities ----------------------- +.. toctree:: + :maxdepth: -1 + :titlesonly: -.. automodule:: jijmodeling.range.value - :members: - :undoc-members: - :show-inheritance: + apidocs/index Indices and Search ================== diff --git a/reference/requirements.txt b/reference/requirements.txt index fd4026f1..cde2c6b3 100644 --- a/reference/requirements.txt +++ b/reference/requirements.txt @@ -1,4 +1,5 @@ -jijmodeling==1.13.2 -sphinx>=8.0,<9.0 +sphinx>=8.2,<9.0 sphinx-rtd-theme>=3.0,<4.0 -sphinxcontrib-katex>=0.9,<0.10 +myst-parser>=4.0,<5.0 +sphinxcontrib-katex>=0.9,<0.10 +sphinx-autodoc2>=0.5,<0.6 From 646e447066cf381f5a249e2207efcacce1832594 Mon Sep 17 00:00:00 2001 From: Yoshiki Sakamoto Date: Fri, 26 Sep 2025 11:47:20 +0900 Subject: [PATCH 16/18] =?UTF-8?q?requirements.txt=E3=81=ABjijmodeling?= =?UTF-8?q?=E3=81=AE=E3=83=90=E3=83=BC=E3=82=B8=E3=83=A7=E3=83=B3=E3=82=92?= =?UTF-8?q?=E8=BF=BD=E5=8A=A0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- reference/requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/reference/requirements.txt b/reference/requirements.txt index cde2c6b3..0aa14e9c 100644 --- a/reference/requirements.txt +++ b/reference/requirements.txt @@ -3,3 +3,4 @@ sphinx-rtd-theme>=3.0,<4.0 myst-parser>=4.0,<5.0 sphinxcontrib-katex>=0.9,<0.10 sphinx-autodoc2>=0.5,<0.6 +jijmodeling==1.13.2 From 69585889e3be3cc59507552fa2d87eb4b518b888 Mon Sep 17 00:00:00 2001 From: Yoshiki Sakamoto Date: Fri, 26 Sep 2025 13:01:34 +0900 Subject: [PATCH 17/18] =?UTF-8?q?conf.py=E3=81=AE=E8=A8=AD=E5=AE=9A?= =?UTF-8?q?=E3=82=92=E6=9B=B4=E6=96=B0=E3=81=97=E3=80=81=E5=BF=85=E8=A6=81?= =?UTF-8?q?=E3=81=AA=E6=8B=A1=E5=BC=B5=E6=A9=9F=E8=83=BD=E3=81=A8=E3=81=9D?= =?UTF-8?q?=E3=81=AE=E3=83=90=E3=83=BC=E3=82=B8=E3=83=A7=E3=83=B3=E3=82=92?= =?UTF-8?q?=E8=BF=BD=E5=8A=A0=E3=80=82index.rst=E3=81=8B=E3=82=89=E4=B8=8D?= =?UTF-8?q?=E8=A6=81=E3=81=AA=E7=A9=BA=E8=A1=8C=E3=82=92=E5=89=8A=E9=99=A4?= =?UTF-8?q?=E3=81=97=E3=80=81requirements.txt=E3=81=AEmyst-parser=E3=81=AE?= =?UTF-8?q?=E4=BE=9D=E5=AD=98=E9=96=A2=E4=BF=82=E3=82=92=E4=BF=AE=E6=AD=A3?= =?UTF-8?q?=E3=80=82?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- reference/conf.py | 37 ++++++++++++++++++++++++++++++++----- reference/index.rst | 1 - reference/requirements.txt | 3 ++- 3 files changed, 34 insertions(+), 7 deletions(-) diff --git a/reference/conf.py b/reference/conf.py index fcc21060..b7f0d5ce 100644 --- a/reference/conf.py +++ b/reference/conf.py @@ -17,20 +17,37 @@ project = "JijModeling API" author = "Jij Inc." copyright = f"{date.today().year}, {author}" +project_copyright = copyright version = jijmodeling_version # 上で取得済み release = version language = "en" +needs_sphinx = "8.2.3" # Sphinxの最低バージョン +html_last_updated_use_utc = True # 最終更新日時をUTCで表示 + # --- テーマ --- html_theme = "sphinx_rtd_theme" +html_theme_options = { + "collapse_navigation": False, # ナビゲーションの折りたたみ + "navigation_depth": -1, # ナビゲーションの深さ（-1で無制限） +} # --- 拡張 --- extensions = [ "sphinx.ext.intersphinx", # 外部ドキュメントへのリンク + "sphinx.ext.napoleon", # Google, Numpyスタイルのdocstringサポート + "sphinx_rtd_theme", # Read the Docsテーマ "autodoc2", # 自動ドキュメント生成 - "sphinxcontrib.katex", # 数式サポート "myst_parser", # Markdownサポート + "sphinxcontrib.katex", # 数式サポート ] +needs_extensions = { + "autodoc2": "0.5.0", # autodoc2の最低バージョン + "myst_parser": "4.0.1", # myst_parserの最低バージョン + "sphinxcontrib.katex": "0.9.11", # sphinxcontrib.katexの最低バージョン +} + +# sphinxcontrib-katexの設定 katex_prerender = True # sphinx-autodoc2のパッケージ設定（.pyiファイル優先） @@ -41,13 +58,23 @@ "exclude_files": ["*.py"], # .pyファイルを除外して.pyiを優先 } ] - autodoc2_render_plugin = "myst" # Markdownレンダリングプラグインを指定 myst_enable_extensions = [ - "smartquotes", # スマートクォート - "amsmath", # 数式サポート - "dollarmath", # $記法での数式 + "amsmath", + "attrs_inline", + "colon_fence", + "deflist", + "dollarmath", + "fieldlist", + "html_admonition", + "html_image", + "linkify", + "replacements", + "smartquotes", + "strikethrough", + "substitution", + "tasklist", ] myst_dmath_double_inline = True diff --git a/reference/index.rst b/reference/index.rst index 87d22ad8..605935ee 100644 --- a/reference/index.rst +++ b/reference/index.rst @@ -19,7 +19,6 @@ Contents Indices and Search ================== - * :ref:`genindex` * :ref:`modindex` * :ref:`search` diff --git a/reference/requirements.txt b/reference/requirements.txt index 0aa14e9c..ea30a48f 100644 --- a/reference/requirements.txt +++ b/reference/requirements.txt @@ -1,6 +1,7 @@ sphinx>=8.2,<9.0 sphinx-rtd-theme>=3.0,<4.0 -myst-parser>=4.0,<5.0 +myst-parser[linkify]>=4.0,<5.0 sphinxcontrib-katex>=0.9,<0.10 sphinx-autodoc2>=0.5,<0.6 + jijmodeling==1.13.2 From 725dc33f390b87343b2fe51481753796d314a77e Mon Sep 17 00:00:00 2001 From: Yoshiki Sakamoto Date: Tue, 30 Sep 2025 09:59:48 +0900 Subject: [PATCH 18/18] =?UTF-8?q?API=E3=83=AA=E3=83=95=E3=82=A1=E3=83=AC?= =?UTF-8?q?=E3=83=B3=E3=82=B9=E3=81=AE=E7=A7=BB=E5=8B=95=E5=87=A6=E7=90=86?= =?UTF-8?q?=E3=82=92=E3=82=B3=E3=83=A1=E3=83=B3=E3=83=88=E3=82=A2=E3=82=A6?= =?UTF-8?q?=E3=83=88=E3=80=82?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .github/workflows/docs.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index 58f56dce..972eb9d1 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -36,7 +36,7 @@ jobs: # Replace GA4_TRACKING_ID placeholder with actual value from secrets # Using | as delimiter to avoid issues with / in the GA4 ID sed -i "s|GA4_TRACKING_ID|${GA4_TRACKING_ID}|g" docs/${{ matrix.lang }}/_config.yml - + # Build the book uv run jupyter-book build docs/${{ matrix.lang }} ERROR_LOGS=$(find docs/${{ matrix.lang }}/_build -type f -name "*.err.log") @@ -59,7 +59,7 @@ jobs: - name: API reference run: | mkdir -p package - mv apis package + # mv apis package - name: Setup redirect run: mv docs/redirect/* package/