From 4878fc0e9997efc4af3347c60b340b3fb81bb63a Mon Sep 17 00:00:00 2001 From: Zengor Date: Fri, 23 May 2025 11:58:57 +0900 Subject: [PATCH 01/13] Update introduction jp & en --- docs/en/introduction.ipynb | 4 ++-- docs/en/quickstart/scip.ipynb | 2 +- docs/ja/introduction.ipynb | 23 +++++++++++++++++------ 3 files changed, 20 insertions(+), 9 deletions(-) diff --git a/docs/en/introduction.ipynb b/docs/en/introduction.ipynb index 558897e..5b4ae1c 100644 --- a/docs/en/introduction.ipynb +++ b/docs/en/introduction.ipynb @@ -17,7 +17,7 @@ "\n", "`jijmodeling` is a tool for describing mathematical models using Python code, not for evaluating or solving mathematical models. This specification focuses on the algebraic structure of mathematical models, independent of instance data, allowing for logical consideration, verification, and faster modification of mathematical models. It also functions as a schema for generating the input format expected by solvers.\n", "\n", - "To solve the mathematical models described with `jijmodeling` using a solver, you need to combine them with actual instance data and convert them into the input format for a specific solver using other tools such as the [JijZept service](https://www.jijzept.com) or the freely available [jijmodeling-transpiler](https://www.documentation.jijzept.com/docs/jijmodelingtranspiler/)." + "To solve the mathematical models described with `jijmodeling` using a solver, you need to combine them with actual instance data and convert them into the input format for a specific solver using other tools such as the [JijZept service](https://www.jijzept.com)." ] }, { @@ -43,7 +43,7 @@ { "data": { "text/plain": [ - "'1.10.0'" + "'1.12.4'" ] }, "execution_count": 1, diff --git a/docs/en/quickstart/scip.ipynb b/docs/en/quickstart/scip.ipynb index a19c884..6ae242e 100644 --- a/docs/en/quickstart/scip.ipynb +++ b/docs/en/quickstart/scip.ipynb @@ -140,7 +140,7 @@ "source": [ "## Step2. Register Instance Data in the `Interpreter` Object\n", "\n", - "Prepare the instance data to be input into the `Placeholder` of the mathematical model formulated in Step1, and register it in the `Interpreter` object.\n", + "Prepare the instance data to be input into the `Placeholder`s of the mathematical model formulated in Step1, and register it in the `Interpreter` object.\n", "\n", "You can register instance data by passing a dictionary with the following keys and values to the constructor of the `Interpreter` class:\n", "\n", diff --git a/docs/ja/introduction.ipynb b/docs/ja/introduction.ipynb index 11ca0ef..6093bca 100644 --- a/docs/ja/introduction.ipynb +++ b/docs/ja/introduction.ipynb @@ -17,7 +17,7 @@ "\n", "`jijmodeling`は、Pythonコードを使用して数理モデルを記述するためのツールであり、数理モデルを評価または解決するものではありません。このような仕様になっているのは、インスタンスデータに依らず、数理モデルの代数構造に焦点を当てることで、数理モデルを論理的に考察し、検証し、より迅速に変更できるようにするためです。また、ソルバーが期待する入力形式を生成するためのスキーマとしても機能します。\n", "\n", - "`jijmodeling`で記述した数理モデルをソルバーで解くには、実際のインスタンスデータと組み合わせて、[JijZeptサービス](https://www.jijzept.com)や無料で利用できる[jijmodeling-transpiler](https://www.documentation.jijzept.com/docs/jijmodelingtranspiler/)などの別のツールによって特定のソルバー用の入力形式に変換する必要があります。" + "`jijmodeling`で記述した数理モデルをソルバーで解くには、実際のインスタンスデータと組み合わせて、[JijZeptサービス](https://www.jijzept.com)で提供されるツールでソルバー用の入力形式に変換する必要があります。" ] }, { @@ -37,9 +37,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'1.12.4'" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import jijmodeling\n", "jijmodeling.__version__" @@ -57,7 +68,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -71,9 +82,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.9.18" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } From 44ecb35a4c76edf6e06da0621db63603c8fc2e56 Mon Sep 17 00:00:00 2001 From: Zengor Date: Fri, 23 May 2025 12:02:13 +0900 Subject: [PATCH 02/13] Update quickstart openjij EN --- docs/en/introduction.ipynb | 6 +- docs/en/quickstart/openjij.ipynb | 95 ++++++++++++++++++-------------- 2 files changed, 58 insertions(+), 43 deletions(-) diff --git a/docs/en/introduction.ipynb b/docs/en/introduction.ipynb index 5b4ae1c..f3d6f6d 100644 --- a/docs/en/introduction.ipynb +++ b/docs/en/introduction.ipynb @@ -68,7 +68,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -82,9 +82,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.9.18" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/docs/en/quickstart/openjij.ipynb b/docs/en/quickstart/openjij.ipynb index 3cd9d24..b638b10 100644 --- a/docs/en/quickstart/openjij.ipynb +++ b/docs/en/quickstart/openjij.ipynb @@ -8,10 +8,10 @@ "\n", "Let's look at a basic example to understand how to use `jijmodeling`. Below, we will explain the steps to create a simple mathematical model, transform it, and run it with a solver. The first two sections are sufficient with just `jijmodeling`, but we recommend using [Jupyter Notebook](https://jupyter.org) to easily check LaTeX output.\n", "\n", - "In the third section, we will use [JijModelingTranspiler](https://www.documentation.jijzept.com/docs/jijmodelingtranspiler/) and [OpenJij](https://openjij.github.io/OpenJij/index.html). JijModelingTranspiler and OpenJij can be installed using `pip`.\n", + "In the third section, we will use [`ommx-openjij-adapter`](https://pypi.org/project/ommx-openjij-adapter/) to solve our model through [OpenJij](https://openjij.github.io/OpenJij/index.html). `ommx-openjij-adapter` can be installed through `pip`:\n", "\n", "```bash\n", - "pip install jijmodeling-transpiler openjij\n", + "pip install ommx-openjij-adapter\n", "```" ] }, @@ -60,7 +60,7 @@ "$$\\begin{array}{cccc}\\text{Problem:} & \\text{my problem} & & \\\\& & \\min \\quad \\displaystyle n \\cdot \\left(a + b + c\\right) & \\\\\\text{{s.t.}} & & & \\\\ & \\text{c1} & \\displaystyle 2 \\cdot \\left(b + c\\right) \\leq 75 & \\\\ & \\text{c2} & \\displaystyle a + b \\leq 40 & \\\\\\text{{where}} & & & \\\\& a & 0\\text{-dim integer variable}\\\\ & & \\text{lower bound: }5 & \\\\ & & \\text{upper bound: }20 & \\\\& b & 0\\text{-dim integer variable}\\\\ & & \\text{lower bound: }1 & \\\\ & & \\text{upper bound: }20 & \\\\& c & 0\\text{-dim integer variable}\\\\ & & \\text{lower bound: }20 & \\\\ & & \\text{upper bound: }30 & \\\\\\end{array}$$" ], "text/plain": [ - "" + "" ] }, "execution_count": 2, @@ -139,7 +139,7 @@ "$$\\begin{array}{cccc}\\text{Problem:} & \\text{knapsack} & & \\\\& & \\max \\quad \\displaystyle \\sum_{i = 0}^{N - 1} v_{i} \\cdot x_{i} & \\\\\\text{{s.t.}} & & & \\\\ & \\text{weight limit} & \\displaystyle \\sum_{i = 0}^{N - 1} w_{i} \\cdot x_{i} \\leq W & \\\\\\text{{where}} & & & \\\\& x & 1\\text{-dim binary variable}\\\\\\end{array}$$" ], "text/plain": [ - "" + "" ] }, "execution_count": 4, @@ -172,7 +172,7 @@ "$$\\begin{array}{cccc}\\text{Problem:} & \\text{knapsack} & & \\\\& & \\max \\quad \\displaystyle \\sum_{i = 0}^{N - 1} v_{i} \\cdot x_{i} & \\\\\\text{{s.t.}} & & & \\\\ & \\text{weight limit} & \\displaystyle \\sum_{i = 0}^{N - 1} w_{i} \\cdot x_{i} \\leq W & \\\\\\text{{where}} & & & \\\\& x & 1\\text{-dim binary variable}\\\\\\end{array}$$" ], "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -206,11 +206,12 @@ "source": [ "## Solving the Model\n", "\n", - "With the above code, we were able to build a model, but `jijmodeling` alone doesn't do much besides LaTeX output. You need to combine it with instance data to generate input for the solver. As an example, let's see how to transform the mathematical model built with `jijmodeling` into a QUBO using the freely available [JijModelingTranspiler](https://www.documentation.jijzept.com/docs/jijmodelingtranspiler/) and solve the model using [OpenJij](https://openjij.github.io/OpenJij/index.html). Note that tools provided by [JijZept](https://www.documentation.jijzept.com/docs/jijzept/) also accept mathematical models built with `jijmodeling` as input.\n", + "With the above code, we were able to build a model, but that model alone doesn't do much besides LaTeX output. You need to combine it with instance data to generate input for the solver. First, we define the instance data to be input into the `Placeholder`s in our model, and register that data in the `Interpreter` object.\n", "\n", - "This section aims to provide a simple demonstration, so we won't explain JijModelingTranspiler or OpenJij in detail. Please refer to their respective documentation for more information.\n", + "You can register instance data by passing a dictionary with the following keys and values to the constructor of the `Interpreter` class:\n", "\n", - "Below, we use JijModelingTranspiler to transform the mathematical model built with `jijmodeling` and instance data into a QUBO." + "- Key: String set in the `name` property of the `Placeholder` object\n", + "- Value: Data to be input" ] }, { @@ -219,66 +220,80 @@ "metadata": {}, "outputs": [], "source": [ - "from jijmodeling_transpiler.core import compile_model\n", - "from jijmodeling_transpiler.core.pubo import transpile_to_pubo\n", - "\n", - "data = {\n", - " \"W\": 100,\n", - " \"v\": [100, 90, 80, 70, 60, 50, 40, 30],\n", - " \"w\": [1, 5, 10, 20, 30, 40, 50, 60, 70]\n", + "instance_data = {\n", + " \"W\": 100, # Data of the knapsack's weight capacity\n", + " \"v\": [100, 90, 80, 70, 60, 50, 40, 30], # Data of item values\n", + " \"w\": [1, 5, 10, 20, 30, 40, 50, 60, 70] # Data of item weights\n", "}\n", - "\n", - "compiled = compile_model(problem, data)\n", - "qubo, _ = transpile_to_pubo(compiled).get_qubo_dict()" + "interpreter = jm.Interpreter(instance_data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Now we have obtained a QUBO that can be accepted as input by OpenJij. Writing out QUBOs manually is prone to errors, but using `jijmodeling` and `jijmodeling_transpiler` eliminates such errors and makes verification easier. Let's actually input the QUBO into `openjij` and solve the problem." + "To convert the mathematical model to an instance, use the `Interpreter.eval_problem` method. By passing the `Problem` object to the `eval_problem` method of the `Interpreter` object with registered instance data, the `Placeholder`s in the `Problem` object will be filled with the instance data and converted to an OMMX instance:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, + "outputs": [], + "source": [ + "instance = interpreter.eval_problem(problem)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + ":::{hint}\n", + "The return value of `Interpreter.eval_problem` is an `ommx.v1.Instance` object. For more details about OMMX, please refer to [here](https://github.com/Jij-Inc/ommx).\n", + ":::" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have an OMMX instance which we can use as input to the OpenJij sampler. Let’s use `ommx-openjij-adapter` to actually sample the problem:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "Sample(sample={0: 1, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1, 6: 0, 7: 0}, energy=-5.501111111111113, num_occurrences=1)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal value of the objective function: 369.234375\n" + ] } ], "source": [ - "import openjij as oj\n", + "from ommx_openjij_adapter import OMMXOpenJijSAAdapter\n", "\n", - "sampler = oj.SASampler()\n", - "response = sampler.sample_qubo(qubo, num_reads=1)\n", - "response.first" + "# Solve through SCIP and retrieve results as an ommx.v1.Solution\n", + "samples = OMMXOpenJijSAAdapter.sample(instance, num_reads=1)\n", + "\n", + "print(f\"Optimal value of the objective function: {samples.best_feasible().objective}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The above code uses simulated annealing from `openjij`, and `num_reads=1` indicates sampling only once. By increasing the value of `num_reads`, you can sample multiple times and explore various results using the response object. However, for this problem, all samples reach the optimal solution, so we will sample once and look at the \"best\" solution found.\n", - "\n", - "```\n", - "Sample(sample={0: 1, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1, 6: 0, 7: 0}, energy=-5.501111111111113, num_occurrences=1)\n", - "```" + "The above code uses simulated annealing from `openjij`, and `num_reads=1` indicates sampling only once. By increasing the value of `num_reads`, you can sample multiple times and explore various results using the response object. However, for this problem, all samples reach the optimal solution, so we sample once and look at the \"best\" solution found." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The above response object shows the values found by `openjij` for each decision variable. You can do much more to better process and visualize the results using JijModelingTranspiler and OpenJij, or reuse the same mathematical model for different purposes, but please refer to their respective documentation pages for more information." + "The above samples object shows the values found by `openjij` for each decision variable. You can do much more to better process and visualize the results using the OMMX Adapter and OpenJij, or reuse the same mathematical model for different purposes, but please refer to their respective documentation pages for more information." ] }, { @@ -295,7 +310,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -309,9 +324,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.9.18" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } From f4526e5a91b157eb85866f0b0dc944f3f4e8bbc4 Mon Sep 17 00:00:00 2001 From: Zengor Date: Fri, 23 May 2025 12:03:02 +0900 Subject: [PATCH 03/13] Add openjij adapter to pyproject --- pyproject.toml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/pyproject.toml b/pyproject.toml index 555f006..94824c8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -10,9 +10,11 @@ dependencies = [ "jijmodeling-transpiler>=0.6.15", "ommx >= 1.8.2, < 2.0.0", "ommx-pyscipopt-adapter >= 1.8.2, < 2.0.0", + "ommx-openjij-adapter >= 1.8.2, < 2.0.0", "openjij>=0.9.2", "pytest>=8.3.3", ] [dependency-groups] dev = ["nbconvert>=7.16.4", "jupyter-book >= 1.0.0, < 2.0.0"] + From a972b2aa1a3b879cbaac659a43946d810c0bad70 Mon Sep 17 00:00:00 2001 From: Zengor Date: Mon, 26 May 2025 18:09:25 +0900 Subject: [PATCH 04/13] Fix hint at end of SCIP tutorial --- docs/en/quickstart/openjij.ipynb | 2 +- docs/en/quickstart/scip.ipynb | 2 +- docs/ja/quickstart/scip.ipynb | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/en/quickstart/openjij.ipynb b/docs/en/quickstart/openjij.ipynb index b638b10..3361e4f 100644 --- a/docs/en/quickstart/openjij.ipynb +++ b/docs/en/quickstart/openjij.ipynb @@ -304,7 +304,7 @@ "\n", "- [Examples](https://www.documentation.jijzept.com/docs/category/learn)\n", "- [JijZept](https://www.documentation.jijzept.com/docs/jijzept/)\n", - "- [JijModelingTranspiler](https://www.documentation.jijzept.com/docs/jijmodelingtranspiler/)" + "- [OMMX](https://jij-inc.github.io/ommx/en/introduction.html)" ] } ], diff --git a/docs/en/quickstart/scip.ipynb b/docs/en/quickstart/scip.ipynb index 6ae242e..ffba61e 100644 --- a/docs/en/quickstart/scip.ipynb +++ b/docs/en/quickstart/scip.ipynb @@ -349,7 +349,7 @@ "metadata": {}, "source": [ ":::{hint}\n", - "`ommx_pyscipopt_adapter` の `model_to_solution` の返却値は `ommx.v1.Solution` オブジェクトです。OMMXについて詳しく知りたい場合は[こちら](https://github.com/Jij-Inc/ommx)を参照してください。\n", + "`OMMXPySCIPOptAdapter.solve` returns an `ommx.v1.Solution` object. For more information on OMMX, see [here](https://github.com/Jij-Inc/ommx).\n", ":::" ] } diff --git a/docs/ja/quickstart/scip.ipynb b/docs/ja/quickstart/scip.ipynb index 2bd34fe..e759023 100644 --- a/docs/ja/quickstart/scip.ipynb +++ b/docs/ja/quickstart/scip.ipynb @@ -341,7 +341,7 @@ "metadata": {}, "source": [ ":::{hint}\n", - "`ommx_pyscipopt_adapter` の `model_to_solution` の返却値は `ommx.v1.Solution` オブジェクトです。OMMXについて詳しく知りたい場合は[こちら](https://github.com/Jij-Inc/ommx)を参照してください。\n", + "`OMMXPySCIPOptAdapter.solve` の返却値は `ommx.v1.Solution` オブジェクトです。OMMXについて詳しく知りたい場合は[こちら](https://github.com/Jij-Inc/ommx)を参照してください。\n", ":::" ] } From d482c3a259bb8bba71ae5c1154d3399d2fb3c313 Mon Sep 17 00:00:00 2001 From: Zengor Date: Mon, 26 May 2025 18:28:25 +0900 Subject: [PATCH 05/13] Update jp quickstart --- docs/ja/quickstart/openjij.ipynb | 126 ++++++++++++++++++------------- 1 file changed, 75 insertions(+), 51 deletions(-) diff --git a/docs/ja/quickstart/openjij.ipynb b/docs/ja/quickstart/openjij.ipynb index 1ca486a..480d502 100644 --- a/docs/ja/quickstart/openjij.ipynb +++ b/docs/ja/quickstart/openjij.ipynb @@ -8,10 +8,10 @@ "\n", "`jijmodeling`の使い方を理解するために基本的な例を見ていきましょう。以下では、シンプルな数理モデルを作成し、それを変換し、ソルバーで実行する手順を説明します。最初の2つのセクションでは、`jijmodeling`だけで十分ですが、LaTeX出力を簡単に確認するために[Jupyterノートブック](https://jupyter.org)を使用することをお勧めします。\n", "\n", - "3つ目のセクションでは、[JijModelingTranspiler](https://www.documentation.jijzept.com/docs/jijmodelingtranspiler/)と[OpenJij](https://openjij.github.io/OpenJij/index.html)を使用します。JijModelingTranspilerとOpenJijは`pip`を使用してインストールできます。\n", + "3つ目のセクションでは、 [`ommx-openjij-adapter`](https://pypi.org/project/ommx-openjij-adapter/)を使用します。`ommx-open-jij-adapter`は`pip`を使用してインストールできます。\n", "\n", "```bash\n", - "pip install jijmodeling-transpiler openjij\n", + "pip install ommx-openjij-adapter\n", "```" ] }, @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -60,10 +60,10 @@ "$$\\begin{array}{cccc}\\text{Problem:} & \\text{my problem} & & \\\\& & \\min \\quad \\displaystyle n \\cdot \\left(a + b + c\\right) & \\\\\\text{{s.t.}} & & & \\\\ & \\text{c1} & \\displaystyle 2 \\cdot \\left(b + c\\right) \\leq 75 & \\\\ & \\text{c2} & \\displaystyle a + b \\leq 40 & \\\\\\text{{where}} & & & \\\\& a & 0\\text{-dim integer variable}\\\\ & & \\text{lower bound: }5 & \\\\ & & \\text{upper bound: }20 & \\\\& b & 0\\text{-dim integer variable}\\\\ & & \\text{lower bound: }1 & \\\\ & & \\text{upper bound: }20 & \\\\& c & 0\\text{-dim integer variable}\\\\ & & \\text{lower bound: }20 & \\\\ & & \\text{upper bound: }30 & \\\\\\end{array}$$" ], "text/plain": [ - "" + "" ] }, - "execution_count": 13, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -96,7 +96,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -130,7 +130,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -139,10 +139,10 @@ "$$\\begin{array}{cccc}\\text{Problem:} & \\text{knapsack} & & \\\\& & \\max \\quad \\displaystyle \\sum_{i = 0}^{N - 1} v_{i} \\cdot x_{i} & \\\\\\text{{s.t.}} & & & \\\\ & \\text{weight limit} & \\displaystyle \\sum_{i = 0}^{N - 1} w_{i} \\cdot x_{i} \\leq W & \\\\\\text{{where}} & & & \\\\& x & 1\\text{-dim binary variable}\\\\\\end{array}$$" ], "text/plain": [ - "" + "" ] }, - "execution_count": 15, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -163,7 +163,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -172,10 +172,10 @@ "$$\\begin{array}{cccc}\\text{Problem:} & \\text{knapsack} & & \\\\& & \\max \\quad \\displaystyle \\sum_{i = 0}^{N - 1} v_{i} \\cdot x_{i} & \\\\\\text{{s.t.}} & & & \\\\ & \\text{weight limit} & \\displaystyle \\sum_{i = 0}^{N - 1} w_{i} \\cdot x_{i} \\leq W & \\\\\\text{{where}} & & & \\\\& x & 1\\text{-dim binary variable}\\\\\\end{array}$$" ], "text/plain": [ - "" + "" ] }, - "execution_count": 16, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -206,79 +206,96 @@ "source": [ "## モデルを解く\n", "\n", - "前述のコードによりモデルを構築することができましたが、`jijmodeling`だけではLaTeX出力する以外にはあまりできることはありません。インスタンスデータと組み合わせて、ソルバーの入力を生成すること必要になります。例として、無料で利用できる[JijModelingTranspiler](https://www.documentation.jijzept.com/docs/jijmodelingtranspiler/)を使用して数理モデルを変換し、[OpenJij](https://openjij.github.io/OpenJij/index.html)を使用してモデルを解く方法を見てみましょう。なお、[JijZept](https://www.documentation.jijzept.com/docs/jijzept/)で提供されているツールも`jijmodeling`によって構築された数理モデルを入力として受け入れるようになっています。\n", + "前述のコードによりモデルを構築することができましたが、`jijmodeling`だけではLaTeX出力する以外にはあまりできることはありません。インスタンスデータと組み合わせて、ソルバーの入力を生成することが必要になります。\n", + "\n", + "まずは数理モデルの `Placeholder` に入力するインスタンスデータを用意し、 `Interpreter` オブジェクトに登録します。\n", "\n", - "本説は簡易的なデモンストレーションを目的としているため、JijModelingTranspilerやOpenJijの説明は行いません。詳細については、それぞれのドキュメントを参照してください。\n", + "`Interpreter` クラスのコンストラクタの引数に、以下のキーと値を持つ辞書を渡すことでインスタンスデータを登録できます：\n", "\n", - "以下では、JijModelingTranspilerを使用して、`jijmodeling`で構築した数理モデルとインスタンスデータを渡してQUBOに変換することとします。" + "- キー：`Placeholder` オブジェクトの `name` プロパティに設定した文字列\n", + "- 値：入力するデータ" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "from jijmodeling_transpiler.core import compile_model\n", - "from jijmodeling_transpiler.core.pubo import transpile_to_pubo\n", - "\n", - "data = {\n", - " \"W\": 100,\n", - " \"v\": [100, 90, 80, 70, 60, 50, 40, 30],\n", - " \"w\": [1, 5, 10, 20, 30, 40, 50, 60, 70]\n", + "instance_data = {\n", + " \"W\": 100, # ナップサックの耐荷重のデータ\n", + " \"v\": [100, 90, 80, 70, 60, 50, 40, 30], # アイテムの価値のデータ\n", + " \"w\": [1, 5, 10, 20, 30, 40, 50, 60, 70] # アイテムの重さのデータ\n", "}\n", - "\n", - "compiled = compile_model(problem, data)\n", - "qubo, _ = transpile_to_pubo(compiled).get_qubo_dict()" + "interpreter = jm.Interpreter(instance_data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "これでOpenJijが入力として受け入れることができるQUBOを取得できました。QUBOを手作業で書き出すことはミスが非常に発生しやすいのですが、`jijmodeling`および`jijmodeling_transpiler`を利用すれば、そのようなミスを無くすことができ、検証が容易になります。では、実際にQUBOを`openjij`に入力して問題を解いてみましょう。" + "数理モデルをインスタンスに変換するには、`Interpreter.eval_problem` メソッドを使用します。インスタンスデータが登録された `Interpreter` オブジェクトの `eval_problem` メソッドに `Problem` オブジェクトを渡すと、その `Problem` オブジェクトが持つ `Placeholder` にインスタンスデータを入力され、インスタンスに変換されます:" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "instance = interpreter.eval_problem(problem)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + ":::{hint}\n", + "`Interpreter.eval_problem` の返却値は `ommx.v1.Instance` オブジェクトです。OMMXについて詳しく知りたい場合は[こちら](https://github.com/Jij-Inc/ommx)を参照してください。\n", + ":::" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "OpenJijサンプラーの入力値に変換できるOMMXインスタンスを取得できました。`ommx-openjij-adapter`で実際サンプリングを行ってみましょう。" + ] + }, + { + "cell_type": "code", + "execution_count": 10, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "Sample(sample={0: 1, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1, 6: 0, 7: 0}, energy=-5.501111111111113, num_occurrences=1)" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "目的関数の最適値:: 389.0\n" + ] } ], "source": [ - "import openjij as oj\n", + "from ommx_openjij_adapter import OMMXOpenJijSAAdapter\n", "\n", - "sampler = oj.SASampler()\n", - "response = sampler.sample_qubo(qubo, num_reads=1)\n", - "response.first" + "# Solve through SCIP and retrieve results as an ommx.v1.Solution\n", + "samples = OMMXOpenJijSAAdapter.sample(instance, num_reads=1)\n", + "\n", + "print(f\"目的関数の最適値:: {samples.best_feasible().objective}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "上記のコードは`openjij`のシミュレーテッドアニーリングを使用しており、`num_reads=1`は1回だけサンプリングすることを示しています。`num_reads`の値を増やすことで複数回サンプリングし、レスポンスオブジェクトを使用してさまざまな結果を探索することができます。しかし、今回の問題では、すべてのサンプルが最適解に到達するため、ここでは1回のサンプルを行い、見つかった「最良」の解を見てみることとします。\n", - "\n", - "```\n", - "Sample(sample={0: 1, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1, 6: 0, 7: 0}, energy=-5.501111111111113, num_occurrences=1)\n", - "```" + "上記のコードは`openjij`のシミュレーテッドアニーリングを使用しており、`num_reads=1`は1回だけサンプリングすることを示しています。`num_reads`の値を増やすことで複数回サンプリングし、レスポンスオブジェクトを使用してさまざまな結果を探索することができます。しかし、今回の問題では、すべてのサンプルが最適解に到達するため、ここでは1回のサンプルを行い、見つかった「最良」の解を見てみることとします。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "上記のレスポンスオブジェクトは各決定変数に対して`openjij`が求めた値を示しています。JijModelingTranspilerやOpenJijを使用して結果をより良く処理および視覚化したり、同じ数理モデルを異なる目的で再利用したりするために、さらに多くのことができますが、それについてはそれぞれのドキュメントページを参照してください。" + "上記のレスポンスオブジェクトは各決定変数に対して`openjij`が求めた値を示しています。OMMX AdapterやOpenJijを使用して結果をより良く処理および視覚化したり、同じ数理モデルを異なる目的で再利用したりするために、さらに多くのことができますが、それについてはそれぞれのドキュメントページを参照してください。" ] }, { @@ -289,13 +306,20 @@ "\n", "- [Examples](https://www.documentation.jijzept.com/docs/category/learn)\n", "- [JijZept](https://www.documentation.jijzept.com/docs/jijzept/)\n", - "- [JijModelingTranspiler](https://www.documentation.jijzept.com/docs/jijmodelingtranspiler/)" + "- [OMMX](https://jij-inc.github.io/ommx/en/introduction.html)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -309,9 +333,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.9.18" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } From d6a2c2fa26aee339010a40167e4a42e6ca6b184e Mon Sep 17 00:00:00 2001 From: Zengor Date: Wed, 28 May 2025 16:59:51 +0900 Subject: [PATCH 06/13] Add tips header --- docs/en/references/tips.ipynb | 6 ++++-- docs/ja/references/tips.ipynb | 6 ++++-- 2 files changed, 8 insertions(+), 4 deletions(-) diff --git a/docs/en/references/tips.ipynb b/docs/en/references/tips.ipynb index 57aee98..cb6b3ac 100644 --- a/docs/en/references/tips.ipynb +++ b/docs/en/references/tips.ipynb @@ -5,6 +5,8 @@ "id": "4bb9ca8e-e701-4e76-863a-c39115b5b33e", "metadata": {}, "source": [ + "# Tips\n", + "\n", "## Defining `Element` Along a Set\n", "\n", "Some users may want to create an index $i \\in \\{0, 1, 2\\}$ using Python's set `Set` as follows:\n", @@ -533,7 +535,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -547,7 +549,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/docs/ja/references/tips.ipynb b/docs/ja/references/tips.ipynb index 4b2c0b5..94798ef 100644 --- a/docs/ja/references/tips.ipynb +++ b/docs/ja/references/tips.ipynb @@ -5,6 +5,8 @@ "id": "4bb9ca8e-e701-4e76-863a-c39115b5b33e", "metadata": {}, "source": [ + "# Tips\n", + "\n", "## 集合に沿った`Element`を定義したい場合\n", "\n", "一部のユーザーはPythonの集合`Set`を使用してインデックス$i \\in \\{0, 1, 2\\}$を以下のように作成したいと思うかもしれません。\n", @@ -534,7 +536,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -548,7 +550,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.9.18" } }, "nbformat": 4, From 0c44443bae2668a7a953be7c13047de017c37247 Mon Sep 17 00:00:00 2001 From: Zengor Date: Wed, 28 May 2025 17:00:42 +0900 Subject: [PATCH 07/13] Remove transpiler from expressions tutorial --- docs/en/tutorials/expressions.ipynb | 11 +++++------ docs/ja/tutorials/expressions.ipynb | 11 +++++------ 2 files changed, 10 insertions(+), 12 deletions(-) diff --git a/docs/en/tutorials/expressions.ipynb b/docs/en/tutorials/expressions.ipynb index fa33b8a..a98772a 100644 --- a/docs/en/tutorials/expressions.ipynb +++ b/docs/en/tutorials/expressions.ipynb @@ -397,13 +397,12 @@ "metadata": {}, "outputs": [], "source": [ - "import jijmodeling_transpiler as jmt\n", - "\n", "problem = jm.Problem('Iterating over a Set')\n", "problem += sum_v\n", "\n", "instance_data = { \"N\": 10, \"V\": [1, 4, 5, 9]}\n", - "compiled_model = jmt.core.compile_model(problem, instance_data)" + "interpreter = jm.Interpreter(instance_data)\n", + "ommx_instance = interpreter.eval_problem(problem)" ] }, { @@ -654,7 +653,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -668,9 +667,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.9.18" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/docs/ja/tutorials/expressions.ipynb b/docs/ja/tutorials/expressions.ipynb index b3edadf..a884fec 100644 --- a/docs/ja/tutorials/expressions.ipynb +++ b/docs/ja/tutorials/expressions.ipynb @@ -397,13 +397,12 @@ "metadata": {}, "outputs": [], "source": [ - "import jijmodeling_transpiler as jmt\n", - "\n", "problem = jm.Problem('Iterating over a Set')\n", "problem += sum_v\n", "\n", "instance_data = { \"N\": 10, \"V\": [1, 4, 5, 9]}\n", - "compiled_model = jmt.core.compile_model(problem, instance_data)" + "interpreter = jm.Interpreter(instance_data)\n", + "ommx_instance = interpreter.eval_problem(problem)" ] }, { @@ -654,7 +653,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -668,9 +667,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.9.18" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } From d3d87bafb8437d4f4e8ec8f30a05e3829576980e Mon Sep 17 00:00:00 2001 From: Zengor Date: Wed, 28 May 2025 17:23:55 +0900 Subject: [PATCH 08/13] Remove CustomPenaltyTerm section --- docs/en/references/tips.ipynb | 2 +- .../en/tutorials/constraint_and_penalty.ipynb | 171 +----------------- docs/ja/references/tips.ipynb | 2 +- .../ja/tutorials/constraint_and_penalty.ipynb | 171 +----------------- 4 files changed, 6 insertions(+), 340 deletions(-) diff --git a/docs/en/references/tips.ipynb b/docs/en/references/tips.ipynb index cb6b3ac..947b9d3 100644 --- a/docs/en/references/tips.ipynb +++ b/docs/en/references/tips.ipynb @@ -506,7 +506,7 @@ "id": "4c3a952a-d1c4-47b7-98df-0d843c9bf6a7", "metadata": {}, "source": [ - "It is recommended to construct instance data for use with `jijmodeling_transpiler` or JijZept as follows." + "It is recommended to construct instance data for use with `jm.Interpreter` or JijZept as follows." ] }, { diff --git a/docs/en/tutorials/constraint_and_penalty.ipynb b/docs/en/tutorials/constraint_and_penalty.ipynb index ff84f86..037790e 100644 --- a/docs/en/tutorials/constraint_and_penalty.ipynb +++ b/docs/en/tutorials/constraint_and_penalty.ipynb @@ -358,178 +358,11 @@ "\n", ":::" ] - }, - { - "cell_type": "markdown", - "id": "25bb0d89-ffd6-4a8d-9968-9593f069b12a", - "metadata": {}, - "source": [ - "### `CustomPenaltyTerm`\n", - "\n", - "The task of converting constraints into penalty terms is the role of `jijmodeling_transpiler` or various services of JijZept, but there may be cases where you want to set an original penalty term as needed. In `jijmodeling`, you can customize the penalty term using `CustomPenaltyTerm`. Here, we will explain how to define the same penalty term as in the previous example using `CustomPenaltyTerm`.\n", - "\n", - "$$\n", - "\\text{Minimize} \\quad \\sum_{i=0}^{N-1} a_i x_i + \\alpha \\left(\\sum_{i = 0}^{N - 1} x_i - 1\\right)^2\n", - "$$\n", - "\n", - "This problem can be expressed using `CustomPenaltyTerm` as follows." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "0950ad6a-6a95-4f4c-8b7b-62dc2f06e8d1", - "metadata": {}, - "outputs": [], - "source": [ - "import jijmodeling as jm\n", - "\n", - "a = jm.Placeholder(\"a\", ndim=1)\n", - "N = a.len_at(0, latex=\"N\")\n", - "\n", - "i = jm.Element('i', belong_to=(0, N))\n", - "x = jm.BinaryVar('x', shape=(N,))\n", - "\n", - "problem = jm.Problem('penalty_sample')\n", - "problem += jm.sum(i, a[i]*x[i])\n", - "problem += jm.CustomPenaltyTerm(\n", - " 'onehot',\n", - " (jm.sum(i, x[i]) - 1)**2,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "ded0e121-e881-4a2e-9e57-f36fc2f747f0", - "metadata": {}, - "source": [ - "This point does not yet set the multiplier $\\alpha$. In this case, you can still use `jijmodeling_transpiler` as before." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "39ab04e0-a45a-411c-afd7-15010a80d56e", - "metadata": {}, - "outputs": [], - "source": [ - "import jijmodeling_transpiler as jmt\n", - "\n", - "instance_data = {\n", - " \"a\": [1, 2],\n", - "}\n", - "\n", - "compiled_model = jmt.core.compile_model(problem, instance_data)\n", - "pubo_builder = jmt.core.pubo.transpile_to_pubo(\n", - " compiled_model,\n", - " normalize=False # Disable normalization for simplicity\n", - ")\n", - "qubo, constant = pubo_builder.get_qubo_dict(multipliers={ 'onehot': 5 })" - ] - }, - { - "cell_type": "markdown", - "id": "63d59c4a", - "metadata": {}, - "source": [ - "Of course, the result is the same." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "0b029506", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{(0, 0): -4.0, (1, 1): -3.0, (0, 1): 10.0}" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "qubo" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "4215230e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "5.0" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "constant" - ] - }, - { - "cell_type": "markdown", - "id": "3e6ec543-b68a-4ef2-823c-75879644a355", - "metadata": {}, - "source": [ - "Also, `CustomPenaltyTerm` has a `forall` argument." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "09ee70c1-7a14-4e22-ab92-49be8dcb768a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$$\\begin{array}{cccc}\\text{Problem:} & \\text{forall\\_penalty\\_sample} & & \\\\& & \\min \\quad \\displaystyle \\sum_{i = 0}^{N - 1} \\sum_{j = 0}^{M - 1} a_{i, j} \\cdot x_{i, j} & \\\\\\text{{penalty terms}} & & & \\\\ & \\text{onehot} & \\displaystyle \\left(\\left(\\sum_{j = 0}^{M - 1} x_{i, j} - 1\\right)^{2}\\right) & \\forall i \\in \\left\\{0,\\ldots,N - 1\\right\\} \\\\\\text{{where}} & & & \\\\& x & 2\\text{-dim binary variable}\\\\\\end{array}$$" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import jijmodeling as jm\n", - "\n", - "a = jm.Placeholder(\"a\", ndim=2)\n", - "N = a.len_at(0, latex=\"N\")\n", - "M = a.len_at(1, latex=\"M\")\n", - "\n", - "i = jm.Element('i', belong_to=(0, N))\n", - "j = jm.Element('j', belong_to=(0, M))\n", - "x = jm.BinaryVar('x', shape=(N, M))\n", - "\n", - "problem = jm.Problem('forall_penalty_sample')\n", - "problem += jm.sum([i, j], a[i, j]*x[i, j])\n", - "problem += jm.CustomPenaltyTerm(\n", - " 'onehot',\n", - " (jm.sum(j, x[i, j]) - 1)**2,\n", - " forall=i\n", - ")\n", - "problem" - ] } ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -543,7 +376,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/docs/ja/references/tips.ipynb b/docs/ja/references/tips.ipynb index 94798ef..54faec0 100644 --- a/docs/ja/references/tips.ipynb +++ b/docs/ja/references/tips.ipynb @@ -507,7 +507,7 @@ "id": "4c3a952a-d1c4-47b7-98df-0d843c9bf6a7", "metadata": {}, "source": [ - "`jijmodeling_transpiler`やJijZeptで使用するインスタンスデータは以下のように構築すると良いでしょう。" + "`jm.Interpreter`やJijZeptで使用するインスタンスデータは以下のように構築すると良いでしょう。" ] }, { diff --git a/docs/ja/tutorials/constraint_and_penalty.ipynb b/docs/ja/tutorials/constraint_and_penalty.ipynb index 50c2d19..ad23c0f 100644 --- a/docs/ja/tutorials/constraint_and_penalty.ipynb +++ b/docs/ja/tutorials/constraint_and_penalty.ipynb @@ -358,178 +358,11 @@ "\n", ":::" ] - }, - { - "cell_type": "markdown", - "id": "25bb0d89-ffd6-4a8d-9968-9593f069b12a", - "metadata": {}, - "source": [ - "### `CustomPenaltyTerm`\n", - "\n", - "制約をペナルティ項に変換する作業は`jijmodeling_transpiler`やJijZeptの各種サービスの役割ですが、必要に応じてオリジナルのペナルティ項を設定したい場合も存在します。`jijmodeling`では`CustomPenaltyTerm`を使用することでペナルティ項のカスタマイズが可能になります。ここでは、前述の例と同じペナルティ項を`CustomPenaltyTerm`で定義する方法を説明します。\n", - "\n", - "$$\n", - "\\text{Minimize} \\quad \\sum_{i=0}^{N-1} a_i x_i + \\alpha \\left(\\sum_{i = 0}^{N - 1} x_i - 1\\right)^2\n", - "$$\n", - "\n", - "この問題は次のように`CustomPenaltyTerm`を使用して表現できます。" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0950ad6a-6a95-4f4c-8b7b-62dc2f06e8d1", - "metadata": {}, - "outputs": [], - "source": [ - "import jijmodeling as jm\n", - "\n", - "a = jm.Placeholder(\"a\", ndim=1)\n", - "N = a.len_at(0, latex=\"N\")\n", - "\n", - "i = jm.Element('i', belong_to=(0, N))\n", - "x = jm.BinaryVar('x', shape=(N,))\n", - "\n", - "problem = jm.Problem('penalty_sample')\n", - "problem += jm.sum(i, a[i]*x[i])\n", - "problem += jm.CustomPenaltyTerm(\n", - " 'onehot',\n", - " (jm.sum(i, x[i]) - 1)**2,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "ded0e121-e881-4a2e-9e57-f36fc2f747f0", - "metadata": {}, - "source": [ - "この時点では未定乗数$\\alpha$が設定されていないことに注意してください。この場合においても前述と同じように`jijmodeling_transpiler`を利用することができます。" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "39ab04e0-a45a-411c-afd7-15010a80d56e", - "metadata": {}, - "outputs": [], - "source": [ - "import jijmodeling_transpiler as jmt\n", - "\n", - "instance_data = {\n", - " \"a\": [1, 2],\n", - "}\n", - "\n", - "compiled_model = jmt.core.compile_model(problem, instance_data)\n", - "pubo_builder = jmt.core.pubo.transpile_to_pubo(\n", - " compiled_model,\n", - " normalize=False # 簡単のために正規化を無効化\n", - ")\n", - "qubo, constant = pubo_builder.get_qubo_dict(multipliers={ 'onehot': 5 })" - ] - }, - { - "cell_type": "markdown", - "id": "63d59c4a", - "metadata": {}, - "source": [ - "当然、結果は同じになります。" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "0b029506", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{(0, 0): -4.0, (1, 1): -3.0, (0, 1): 10.0}" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "qubo" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "4215230e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "5.0" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "constant" - ] - }, - { - "cell_type": "markdown", - "id": "3e6ec543-b68a-4ef2-823c-75879644a355", - "metadata": {}, - "source": [ - "また、`CustomPenaltyTerm`にも`forall`引数があります。" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "09ee70c1-7a14-4e22-ab92-49be8dcb768a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$$\\begin{array}{cccc}\\text{Problem:} & \\text{forall\\_penalty\\_sample} & & \\\\& & \\min \\quad \\displaystyle \\sum_{i = 0}^{N - 1} \\sum_{j = 0}^{M - 1} a_{i, j} \\cdot x_{i, j} & \\\\\\text{{penalty terms}} & & & \\\\ & \\text{onehot} & \\displaystyle \\left(\\left(\\sum_{j = 0}^{M - 1} x_{i, j} - 1\\right)^{2}\\right) & \\forall i \\in \\left\\{0,\\ldots,N - 1\\right\\} \\\\\\text{{where}} & & & \\\\& x & 2\\text{-dim binary variable}\\\\\\end{array}$$" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import jijmodeling as jm\n", - "\n", - "a = jm.Placeholder(\"a\", ndim=2)\n", - "N = a.len_at(0, latex=\"N\")\n", - "M = a.len_at(1, latex=\"M\")\n", - "\n", - "i = jm.Element('i', belong_to=(0, N))\n", - "j = jm.Element('j', belong_to=(0, M))\n", - "x = jm.BinaryVar('x', shape=(N, M))\n", - "\n", - "problem = jm.Problem('forall_penalty_sample')\n", - "problem += jm.sum([i, j], a[i, j]*x[i, j])\n", - "problem += jm.CustomPenaltyTerm(\n", - " 'onehot',\n", - " (jm.sum(j, x[i, j]) - 1)**2,\n", - " forall=i\n", - ")\n", - "problem" - ] } ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -543,7 +376,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.9.18" } }, "nbformat": 4, From 0c040e00bf5e7a65b904002051d94a8cc9f739ea Mon Sep 17 00:00:00 2001 From: Zengor Date: Wed, 28 May 2025 17:49:28 +0900 Subject: [PATCH 09/13] Replace transpiler usage in penalty/qubo tutorial --- .../en/tutorials/constraint_and_penalty.ipynb | 42 ++++++++---------- .../ja/tutorials/constraint_and_penalty.ipynb | 44 +++++++++---------- 2 files changed, 38 insertions(+), 48 deletions(-) diff --git a/docs/en/tutorials/constraint_and_penalty.ipynb b/docs/en/tutorials/constraint_and_penalty.ipynb index 037790e..3fad1fe 100644 --- a/docs/en/tutorials/constraint_and_penalty.ipynb +++ b/docs/en/tutorials/constraint_and_penalty.ipynb @@ -168,7 +168,7 @@ "\n", "In this conversion, $\\alpha$ (penalty coefficient or Lagrange multiplier) and $p(x)$ (penalty term) play important roles. Typically, $p(x)$ is defined as $p(x) = g(x)^2$. If the minimum value of $f(x) + \\alpha p(x)$ satisfies $p(x) = 0$, then that $x$ is the minimum value of the original constrained optimization problem. If the penalty $p(x)$ is positive, increasing the value of the penalty coefficient $\\alpha$ and solving the above unconstrained optimization problem increases the likelihood of obtaining the solution to the original optimization problem.\n", "\n", - "Some solvers only accept unconstrained optimization problems. The \"U\" in QUBO stands for \"Unconstrained\". To input a mathematical model formulated as a constrained optimization problem in `jijmodeling` into a solver with a QUBO input format, it is necessary to convert it into an unconstrained optimization problem using `jijmodeling_transpiler` or JijZept. However, in actual optimization problems, the definition of $p$ is also important, so `jijmodeling` provides a feature to customize the penalty term." + "Some solvers only accept unconstrained optimization problems. The \"U\" in QUBO stands for \"Unconstrained\". To input a mathematical model formulated as a constrained optimization problem in `jijmodeling` into a solver with a QUBO input format, it is necessary to convert it into an unconstrained optimization problem using [OMMX](https://github.com/Jij-Inc/ommx/tree/main) through `jijmodeling.Interpreter`." ] }, { @@ -178,7 +178,7 @@ "source": [ "### Converting Constraints to Penalty Terms\n", "\n", - "`jijmodeling` does not have the function to convert constraints into penalty terms. Here, we will explain how `jijmodeling_transpiler` converts constraints into penalty terms. Consider a simple problem like the following.\n", + "`jijmodeling` does not have the function to convert constraints into penalty terms. Here, we will explain how OMMX converts constraints into penalty terms. Consider a simple problem like the following.\n", "\n", "$$\n", "\\begin{aligned}\n", @@ -202,7 +202,7 @@ "$$\\begin{array}{cccc}\\text{Problem:} & \\text{translate\\_constraint\\_to\\_penalty} & & \\\\& & \\min \\quad \\displaystyle \\sum_{i = 0}^{N - 1} a_{i} \\cdot x_{i} & \\\\\\text{{s.t.}} & & & \\\\ & \\text{onehot} & \\displaystyle \\sum_{i = 0}^{N - 1} x_{i} = 1 & \\\\\\text{{where}} & & & \\\\& x & 1\\text{-dim binary variable}\\\\\\end{array}$$" ], "text/plain": [ - "" + "" ] }, "execution_count": 4, @@ -233,7 +233,7 @@ "id": "3ad65535-6e38-45fd-ad69-628dc49d822d", "metadata": {}, "source": [ - "In `jijmodeling_transpiler`, this constrained optimization problem is converted into the following unconstrained optimization problem.\n", + "Through OMMX's `to_qubo`, this constrained optimization problem is converted into the following unconstrained optimization problem.\n", "\n", "$$\n", "\\text{Minimize} \\quad \\sum_{i=0}^{N-1} a_i x_i + \\alpha \\left(\\sum_{i = 0}^{N - 1} x_i - 1\\right)^2\n", @@ -244,23 +244,19 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 19, "id": "18e5a4d6-abe3-439f-950c-fd63a6f5c5c9", "metadata": {}, "outputs": [], "source": [ - "import jijmodeling_transpiler as jmt\n", - "\n", "instance_data = {\n", " \"a\": [1, 2],\n", "}\n", "\n", - "compiled_model = jmt.core.compile_model(problem, instance_data)\n", - "pubo_builder = jmt.core.pubo.transpile_to_pubo(\n", - " compiled_model,\n", - " normalize=False # Disable normalization for simplicity\n", - ")\n", - "qubo, constant = pubo_builder.get_qubo_dict(multipliers={ 'onehot': 5 })" + "interpreter = jm.Interpreter(instance_data)\n", + "ommx_instance = interpreter.eval_problem(problem)\n", + "\n", + "qubo, constant = ommx_instance.to_qubo(penalty_weights={0: 5}) " ] }, { @@ -273,17 +269,17 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 20, "id": "5d99026f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{(0, 0): -4.0, (1, 1): -3.0, (0, 1): 10.0}" + "{(0, 0): -4.0, (0, 1): 10.0, (1, 1): -3.0}" ] }, - "execution_count": 6, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -294,7 +290,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 21, "id": "7859ea27", "metadata": {}, "outputs": [ @@ -304,7 +300,7 @@ "5.0" ] }, - "execution_count": 7, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -341,10 +337,10 @@ "\n", "The above calculation uses the fact that the binary variable $x_i$ satisfies $x_i^2 = x_i$.\n", "\n", - "The conversion process of `jijmodeling_transpiler` is divided into two phases.\n", + "The conversion process of getting QUBO through OMMX is divided into two phases.\n", "\n", - "- Convert to `CompiledInstance` object using `Problem` object and `instance_data`.\n", - "- Convert `CompiledInstance` object to QUBO by specifying multipliers." + "- Convert to an `ommx.v1.Instance` object using `Problem` object and `instance_data`.\n", + "- Convert `ommx.v1.Instance` object to QUBO by using the `to_qubo` method and specifying multipliers." ] }, { @@ -353,9 +349,7 @@ "metadata": {}, "source": [ ":::{note}\n", - "\n", - "`jijmodeling_transpiler` allows you to set the multipliers for each penalty term using the `multipliers` argument of the `get_qubo_dict` method. Additionally, `jijmodeling_transpiler` supports other relaxation methods such as the [Augmented Lagrangian method](https://en.wikipedia.org/wiki/Augmented_Lagrangian_method). For more details, refer to the [jijmodeling_transpiler reference](https://www.documentation.jijzept.com/docs/jijmodelingtranspiler/references/jijmodeling_transpiler/core/pubo/).\n", - "\n", + "For more details on converting to QUBO, refer to the [OMMX reference](https://jij-inc.github.io/ommx/python/ommx/autoapi/ommx/v1/index.html#ommx.v1.Instance.to_qubo)\n", ":::" ] } diff --git a/docs/ja/tutorials/constraint_and_penalty.ipynb b/docs/ja/tutorials/constraint_and_penalty.ipynb index ad23c0f..e676bdc 100644 --- a/docs/ja/tutorials/constraint_and_penalty.ipynb +++ b/docs/ja/tutorials/constraint_and_penalty.ipynb @@ -168,7 +168,7 @@ "\n", "この変換では$\\alpha$（ペナルティ係数またはラグランジュ乗数）と$p(x)$（ペナルティ項）が重要な役割を果たします。通常、$p(x)$は$p(x) = g(x)^2$として定義されます。$f(x) + \\alpha p(x)$の最小値が$p(x) = 0$を満たす場合、その$x$は元の制約付き最適化問題の最小値になります。ペナルティ$p(x)$が正の場合、ペナルティ係数$\\alpha$の値を増やして上記の制約なし最適化問題を解くと元の最適化問題の解を得られる可能性が高まります。\n", "\n", - "一部のソルバーは制約なし最適化問題のみしか受け入れません。QUBOの「U」は「Unconstrained（無制約）」を意味しています。`jijmodeling`で制約付き最適化問題として定式化した数理モデルを入力形式がQUBOのソルバーに与えるためには、`jijmodeling_transpiler`またはJijZeptを利用して制約なし最適化問題に変換する必要があります。しかし、実際の最適化問題においては$p$の定義も重要であり、そのため、`jijmodeling`ではペナルティ項をカスタマイズする機能を提供しています。" + "一部のソルバーは制約なし最適化問題のみしか受け入れません。QUBOの「U」は「Unconstrained（無制約）」を意味しています。`jijmodeling`で制約付き最適化問題として定式化した数理モデルを入力形式がQUBOのソルバーに与えるためには、`jijmodeling.Interpreter`と[OMMX](https://github.com/Jij-Inc/ommx/tree/main) を利用して制約なし最適化問題に変換する必要があります。" ] }, { @@ -178,7 +178,7 @@ "source": [ "### 制約をペナルティ項に変換する\n", "\n", - "`jijmodeling`は制約をペナルティ項に変換する機能を持ちません。ここでは`jijmodeling_transpiler`が制約をペナルティ項に変換する方法を説明します。以下のような簡単な問題を考えてみましょう。\n", + "`jijmodeling`は制約をペナルティ項に変換する機能を持ちません。ここではOMMXが制約をペナルティ項に変換する方法を説明します。以下のような簡単な問題を考えてみましょう。\n", "\n", "$$\n", "\\begin{aligned}\n", @@ -192,7 +192,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 1, "id": "b5cbc45d-7e1a-4c7a-b3ac-4dbeb5a7152f", "metadata": {}, "outputs": [ @@ -202,10 +202,10 @@ "$$\\begin{array}{cccc}\\text{Problem:} & \\text{translate\\_constraint\\_to\\_penalty} & & \\\\& & \\min \\quad \\displaystyle \\sum_{i = 0}^{N - 1} a_{i} \\cdot x_{i} & \\\\\\text{{s.t.}} & & & \\\\ & \\text{onehot} & \\displaystyle \\sum_{i = 0}^{N - 1} x_{i} = 1 & \\\\\\text{{where}} & & & \\\\& x & 1\\text{-dim binary variable}\\\\\\end{array}$$" ], "text/plain": [ - "" + "" ] }, - "execution_count": 4, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -233,7 +233,7 @@ "id": "3ad65535-6e38-45fd-ad69-628dc49d822d", "metadata": {}, "source": [ - "`jijmodeling_transpiler`では、この制約付き最適化問題は次の制約なし最適化問題に変換されます。\n", + "OMMXでは、この制約付き最適化問題は次の制約なし最適化問題に変換されます。\n", "\n", "$$\n", "\\text{Minimize} \\quad \\sum_{i=0}^{N-1} a_i x_i + \\alpha \\left(\\sum_{i = 0}^{N - 1} x_i - 1\\right)^2\n", @@ -244,23 +244,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "18e5a4d6-abe3-439f-950c-fd63a6f5c5c9", "metadata": {}, "outputs": [], "source": [ - "import jijmodeling_transpiler as jmt\n", "\n", "instance_data = {\n", " \"a\": [1, 2],\n", "}\n", "\n", - "compiled_model = jmt.core.compile_model(problem, instance_data)\n", - "pubo_builder = jmt.core.pubo.transpile_to_pubo(\n", - " compiled_model,\n", - " normalize=False # 簡単のために正規化を無効化\n", - ")\n", - "qubo, constant = pubo_builder.get_qubo_dict(multipliers={ 'onehot': 5 })" + "interpreter = jm.Interpreter(instance_data)\n", + "ommx_instance = interpreter.eval_problem(problem)\n", + "\n", + "qubo, constant = ommx_instance.to_qubo(penalty_weights={0: 5}) " ] }, { @@ -273,17 +270,17 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "id": "5d99026f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{(0, 0): -4.0, (1, 1): -3.0, (0, 1): 10.0}" + "{(0, 0): -4.0, (0, 1): 10.0, (1, 1): -3.0}" ] }, - "execution_count": 6, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -294,7 +291,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "id": "7859ea27", "metadata": {}, "outputs": [ @@ -304,7 +301,7 @@ "5.0" ] }, - "execution_count": 7, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -341,10 +338,10 @@ "\n", "上記の計算は、バイナリ変数$x_i$が$x_i^2 = x_i$であることを使って式変形しています。\n", "\n", - "`jijmodeling_transpiler`の変換過程には2つのフェーズに分かれています。\n", + "OMMXの変換過程には2つのフェーズに分かれています。\n", "\n", - "- `Problem`オブジェクトと`instance_data`を使用して`CompiledInstance`オブジェクトに変換する。\n", - "- `CompiledInstance`オブジェクトに未定乗数を指定してQUBOに変換する。" + "- `Problem`オブジェクトと`instance_data`を使用して`ommx.v1.Instance`オブジェクトに変換する。\n", + "- `ommx.v1.Instance`オブジェクトの`to_qubo`メソッドに未定乗数を指定してQUBOに変換する。" ] }, { @@ -354,8 +351,7 @@ "source": [ ":::{note}\n", "\n", - 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{ url = "https://files.pythonhosted.org/packages/ff/21/abdedb4cdf6ff41ebf01a74087740a709e2edb146490e4d9beea054b0b7a/wrapt-1.16.0-py3-none-any.whl", hash = "sha256:6906c4100a8fcbf2fa735f6059214bb13b97f75b1a61777fcf6432121ef12ef1", size = 23362 }, -] - [[package]] name = "zipp" version = "3.20.2" From 14edaaa6929cb7c283c3ac4cede273863bdef53f Mon Sep 17 00:00:00 2001 From: Zengor Date: Wed, 28 May 2025 17:52:29 +0900 Subject: [PATCH 11/13] Update requirements.txt --- requirements.txt | 58 ++++++++---------------------------------------- 1 file changed, 9 insertions(+), 49 deletions(-) diff --git a/requirements.txt b/requirements.txt index 1273b8a..cfbe94a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,68 +1,47 @@ # This file was autogenerated by uv via the following command: # uv pip compile pyproject.toml -o requirements.txt -annotated-types==0.7.0 - # via pydantic certifi==2024.8.30 # via requests -cffi==1.15.1 - # via mip charset-normalizer==3.4.0 # via requests -deprecated==1.2.14 - # via pyqubo dimod==0.12.18 # via - # dwave-samplers # jij-cimod - # jijmodeling-transpiler # openjij - # pyqubo -dwave-neal==0.6.0 - # via pyqubo -dwave-samplers==1.4.0 - # via dwave-neal exceptiongroup==1.2.2 # via pytest idna==3.10 # via requests -importlib-metadata==8.6.1 - # via typeguard iniconfig==2.0.0 # via pytest jij-cimod==1.6.2 # via openjij -jijmodeling==1.10.1 - # via - # jijmodeling-tutorial (pyproject.toml) - # jijmodeling-transpiler -jijmodeling-transpiler==0.6.15 +jijmodeling==1.12.4 # via jijmodeling-tutorial (pyproject.toml) -mip==1.15.0 - # via jijmodeling-transpiler -networkx==3.2.1 - # via dwave-samplers numpy==1.26.4 # via # dimod - # dwave-samplers # jij-cimod # jijmodeling - # jijmodeling-transpiler # ommx # openjij # pandas # pyarrow - # pyqubo # scipy -ommx==1.8.2 +ommx==1.9.5 # via # jijmodeling-tutorial (pyproject.toml) # jijmodeling + # ommx-openjij-adapter # ommx-pyscipopt-adapter +ommx-openjij-adapter==1.9.5 + # via jijmodeling-tutorial (pyproject.toml) ommx-pyscipopt-adapter==1.8.2 # via jijmodeling-tutorial (pyproject.toml) openjij==0.9.2 - # via jijmodeling-tutorial (pyproject.toml) + # via + # jijmodeling-tutorial (pyproject.toml) + # ommx-openjij-adapter orjson==3.10.10 # via jijmodeling packaging==24.1 @@ -77,14 +56,6 @@ protobuf==5.28.3 # via ommx pyarrow==16.1.0 # via ommx -pycparser==2.22 - # via cffi -pydantic==2.9.2 - # via jijmodeling-transpiler -pydantic-core==2.23.4 - # via pydantic -pyqubo==1.4.0 - # via jijmodeling-transpiler pyscipopt==5.2.1 # via ommx-pyscipopt-adapter pytest==8.3.3 @@ -102,25 +73,14 @@ scipy==1.11.4 # jij-cimod # openjij six==1.16.0 - # via - # pyqubo - # python-dateutil + # via python-dateutil tomli==2.1.0 # via pytest -typeguard==4.4.1 - # via jijmodeling-transpiler typing-extensions==4.12.2 # via # ommx # openjij - # pydantic - # pydantic-core - # typeguard tzdata==2024.2 # via pandas urllib3==2.2.3 # via requests -wrapt==1.16.0 - # via deprecated -zipp==3.21.0 - # via importlib-metadata From 09626d81cc148857d192b40d249de7be9b94db15 Mon Sep 17 00:00:00 2001 From: Zengor Date: Wed, 28 May 2025 17:55:49 +0900 Subject: [PATCH 12/13] Remove remaining usage of transpiler --- docs/en/tutorials/expressions.ipynb | 3 ++- docs/ja/tutorials/expressions.ipynb | 5 +++-- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/docs/en/tutorials/expressions.ipynb b/docs/en/tutorials/expressions.ipynb index a98772a..971654b 100644 --- a/docs/en/tutorials/expressions.ipynb +++ b/docs/en/tutorials/expressions.ipynb @@ -480,7 +480,8 @@ " [3, 7, 8]],\n", " \"K\": [1, 1, 2],\n", "}\n", - "compiled_model = jmt.core.compile_model(problem, instance_data)" + "interpreter = jm.Interpreter(instance_data)\n", + "ommx_instance = interpreter.eval_problem(problem)" ] }, { diff --git a/docs/ja/tutorials/expressions.ipynb b/docs/ja/tutorials/expressions.ipynb index a884fec..0c0a959 100644 --- a/docs/ja/tutorials/expressions.ipynb +++ b/docs/ja/tutorials/expressions.ipynb @@ -466,7 +466,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -480,7 +480,8 @@ " [3, 7, 8]],\n", " \"K\": [1, 1, 2],\n", "}\n", - "compiled_model = jmt.core.compile_model(problem, instance_data)" + "interpreter = jm.Interpreter(instance_data)\n", + "ommx_instance = interpreter.eval_problem(problem)" ] }, { From 41481e153889d48b81cee59f8e4eb1eeded85a3e Mon Sep 17 00:00:00 2001 From: Zengor Date: Wed, 28 May 2025 18:02:49 +0900 Subject: [PATCH 13/13] Fix wrong dvar shape in jagged array example --- docs/en/tutorials/expressions.ipynb | 4 +-- docs/ja/tutorials/expressions.ipynb | 44 ++++++++++++++--------------- 2 files changed, 24 insertions(+), 24 deletions(-) diff --git a/docs/en/tutorials/expressions.ipynb b/docs/en/tutorials/expressions.ipynb index 971654b..79140ae 100644 --- a/docs/en/tutorials/expressions.ipynb +++ b/docs/en/tutorials/expressions.ipynb @@ -466,7 +466,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -474,7 +474,7 @@ "problem += k_hot\n", "\n", "instance_data = {\n", - " \"N\": 4,\n", + " \"N\": 10,\n", " \"C\": [[1, 4, 5, 9],\n", " [2, 6],\n", " [3, 7, 8]],\n", diff --git a/docs/ja/tutorials/expressions.ipynb b/docs/ja/tutorials/expressions.ipynb index 0c0a959..3c66bb8 100644 --- a/docs/ja/tutorials/expressions.ipynb +++ b/docs/ja/tutorials/expressions.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -45,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -54,7 +54,7 @@ "\"BinaryVar(name='x', shape=[]) + BinaryVar(name='y', shape=[])\"" ] }, - "execution_count": 2, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -99,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -108,7 +108,7 @@ "\"BinaryVar(name='x', shape=[]) == BinaryVar(name='y', shape=[])\"" ] }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -121,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -130,7 +130,7 @@ "False" ] }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -150,7 +150,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -162,7 +162,7 @@ "BinaryVar(name='x', shape=[NumberLit(value=3), NumberLit(value=4)])[NumberLit(value=0), NumberLit(value=2)]" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -185,7 +185,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -197,7 +197,7 @@ "BinaryVar(name='x', shape=[NumberLit(value=3), NumberLit(value=4)])[NumberLit(value=2) * Placeholder(name='n', ndim=0), NumberLit(value=3) * Placeholder(name='n', ndim=0)]" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -223,7 +223,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -235,7 +235,7 @@ "sum(Element(name='n', belong_to=(NumberLit(value=0), Placeholder(name='N', ndim=0))), BinaryVar(name='x', shape=[Placeholder(name='N', ndim=0)])[Element(name='n', belong_to=(NumberLit(value=0), Placeholder(name='N', ndim=0)))])" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -288,7 +288,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -300,7 +300,7 @@ "sum(Element(name='n', belong_to=(NumberLit(value=0), Placeholder(name='N', ndim=0))), Element(name='n', belong_to=(NumberLit(value=0), Placeholder(name='N', ndim=0))) * BinaryVar(name='x', shape=[Placeholder(name='N', ndim=0)])[Element(name='n', belong_to=(NumberLit(value=0), Placeholder(name='N', ndim=0)))])" ] }, - "execution_count": 11, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -318,7 +318,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -330,7 +330,7 @@ "sum(Element(name='n', belong_to=(NumberLit(value=0), Placeholder(name='N', ndim=0))), BinaryVar(name='x', shape=[Placeholder(name='N', ndim=0)])[Element(name='n', belong_to=(NumberLit(value=0), Placeholder(name='N', ndim=0)))]) * (- sum(Element(name='n', belong_to=(NumberLit(value=0), Placeholder(name='N', ndim=0))), BinaryVar(name='x', shape=[Placeholder(name='N', ndim=0)])[Element(name='n', belong_to=(NumberLit(value=0), Placeholder(name='N', ndim=0)))]) + NumberLit(value=1))" ] }, - "execution_count": 12, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -367,7 +367,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -393,7 +393,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -432,7 +432,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -466,7 +466,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -474,7 +474,7 @@ "problem += k_hot\n", "\n", "instance_data = {\n", - " \"N\": 4,\n", + " \"N\": 10,\n", " \"C\": [[1, 4, 5, 9],\n", " [2, 6],\n", " [3, 7, 8]],\n",