"Benchmark sets such as [MIPLIB](https://miplib.zib.de/) or [TSPLIB](http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/) are usually employed to evaluate the performance of conventional MIP solvers. Two shortcomings, however, make existing benchmark sets less suitable for evaluating the performance of learning-enhanced MIP solvers: (i) while existing benchmark sets typically contain hundreds or thousands of instances, machine learning (ML) methods typically benefit from having orders of magnitude more instances available for training; (ii) current machine learning methods typically provide best performance on sets of homogeneous instances, buch general-purpose benchmark sets contain relatively few examples of each problem type.\n",
"Benchmark sets such as [MIPLIB](https://miplib.zib.de/) or [TSPLIB](http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/) are usually employed to evaluate the performance of conventional MIP solvers. Two shortcomings, however, make existing benchmark sets less suitable for evaluating the performance of learning-enhanced MIP solvers: (i) while existing benchmark sets typically contain hundreds or thousands of instances, machine learning (ML) methods typically benefit from having orders of magnitude more instances available for training; (ii) current machine learning methods typically provide best performance on sets of homogeneous instances, buch general-purpose benchmark sets contain relatively few examples of each problem type.\n",
"\n",
"\n",
"To tackle this challenge, MIPLearn provides random instance generators for a wide variety of classical optimization problems, covering applications from different fields, that can be used to evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. As of MIPLearn 0.3, nine problem generators are available, each customizable with user-provided probability distribution and flexible parameters. The generators can be configured, for example, to produce large sets of very similar instances of same size, where only the objective function changes, or more diverse sets of instances, with various sizes and characteristics, belonging to a particular problem class.\n",
"To tackle this challenge, MIPLearn provides random instance generators for a wide variety of classical optimization problems, covering applications from different fields, that can be used to evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. Nine problem generators are available, each customizable with user-provided probability distribution and flexible parameters. The generators can be configured, for example, to produce large sets of very similar instances of same size, where only the objective function changes, or more diverse sets of instances, with various sizes and characteristics, belonging to a particular problem class.\n",
"\n",
"\n",
"In the following, we describe the problems included in the library, their MIP formulation and the generation algorithm."
"In the following, we describe the problems included in the library, their MIP formulation and the generation algorithm."
If you use MIPLearn in your research (either the solver or the included problem generators), we kindly request that you cite the package as follows:
If you use MIPLearn in your research (either the solver or the included problem generators), we kindly request that you cite the package as follows:
* **Alinson S. Xavier, Feng Qiu, Xiaoyi Gu, Berkay Becu, Santanu S. Dey.** *MIPLearn: An Extensible Framework for Learning-Enhanced Optimization (Version 0.3)*. Zenodo (2023). DOI: https://doi.org/10.5281/zenodo.4287567
* **Alinson S. Xavier, Feng Qiu, Xiaoyi Gu, Berkay Becu, Santanu S. Dey.** *MIPLearn: An Extensible Framework for Learning-Enhanced Optimization (Version 0.4)*. Zenodo (2024). DOI: https://doi.org/10.5281/zenodo.4287567
If you use MIPLearn in the field of power systems optimization, we kindly request that you cite the reference below, in which the main techniques implemented in MIPLearn were first developed:
If you use MIPLearn in the field of power systems optimization, we kindly request that you cite the reference below, in which the main techniques implemented in MIPLearn were first developed:
"In this tutorial, we will demonstrate how to use and install the Python/Pyomo version of the package. The first step is to install Julia in your machine. See the [official Julia website for more instructions](https://julialang.org/downloads/). After Julia is installed, launch the Julia REPL, type `]` to enter package mode, then install MIPLearn:\n",
"In this tutorial, we will demonstrate how to use and install the Python/Pyomo version of the package. The first step is to install Julia in your machine. See the [official Julia website for more instructions](https://julialang.org/downloads/). After Julia is installed, launch the Julia REPL, type `]` to enter package mode, then install MIPLearn:\n",
"In this tutorial, we will demonstrate how to use and install the Python/Pyomo version of the package. The first step is to install Python 3.8+ in your computer. See the [official Python website for more instructions](https://www.python.org/downloads/). After Python is installed, we proceed to install MIPLearn using `pip`:\n",
"In this tutorial, we will demonstrate how to use and install the Python/Pyomo version of the package. The first step is to install Python 3.8+ in your computer. See the [official Python website for more instructions](https://www.python.org/downloads/). After Python is installed, we proceed to install MIPLearn using `pip`:\n",
"\n",
"\n",
"```\n",
"```\n",
"$ pip install MIPLearn==0.3\n",
"$ pip install MIPLearn==0.4\n",
"```\n",
"```\n",
"\n",
"\n",
"In addition to MIPLearn itself, we will also install Gurobi 10.0, a state-of-the-art commercial MILP solver. This step also install a demo license for Gurobi, which should able to solve the small optimization problems in this tutorial. A license is required for solving larger-scale problems.\n",
"In addition to MIPLearn itself, we will also install Gurobi 10.0, a state-of-the-art commercial MILP solver. This step also install a demo license for Gurobi, which should able to solve the small optimization problems in this tutorial. A license is required for solving larger-scale problems.\n",
"Benchmark sets such as [MIPLIB](https://miplib.zib.de/) or [TSPLIB](http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/) are usually employed to evaluate the performance of conventional MIP solvers. Two shortcomings, however, make existing benchmark sets less suitable for evaluating the performance of learning-enhanced MIP solvers: (i) while existing benchmark sets typically contain hundreds or thousands of instances, machine learning (ML) methods typically benefit from having orders of magnitude more instances available for training; (ii) current machine learning methods typically provide best performance on sets of homogeneous instances, buch general-purpose benchmark sets contain relatively few examples of each problem type.\n",
"Benchmark sets such as [MIPLIB](https://miplib.zib.de/) or [TSPLIB](http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/) are usually employed to evaluate the performance of conventional MIP solvers. Two shortcomings, however, make existing benchmark sets less suitable for evaluating the performance of learning-enhanced MIP solvers: (i) while existing benchmark sets typically contain hundreds or thousands of instances, machine learning (ML) methods typically benefit from having orders of magnitude more instances available for training; (ii) current machine learning methods typically provide best performance on sets of homogeneous instances, buch general-purpose benchmark sets contain relatively few examples of each problem type.\n",
"\n",
"\n",
"To tackle this challenge, MIPLearn provides random instance generators for a wide variety of classical optimization problems, covering applications from different fields, that can be used to evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. As of MIPLearn 0.3, nine problem generators are available, each customizable with user-provided probability distribution and flexible parameters. The generators can be configured, for example, to produce large sets of very similar instances of same size, where only the objective function changes, or more diverse sets of instances, with various sizes and characteristics, belonging to a particular problem class.\n",
"To tackle this challenge, MIPLearn provides random instance generators for a wide variety of classical optimization problems, covering applications from different fields, that can be used to evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. Nine problem generators are available, each customizable with user-provided probability distribution and flexible parameters. The generators can be configured, for example, to produce large sets of very similar instances of same size, where only the objective function changes, or more diverse sets of instances, with various sizes and characteristics, belonging to a particular problem class.\n",
"\n",
"\n",
"In the following, we describe the problems included in the library, their MIP formulation and the generation algorithm."
"In the following, we describe the problems included in the library, their MIP formulation and the generation algorithm."
<h2><spanclass="section-number">5.1. </span>Overview<aclass="headerlink"href="#Overview"title="Link to this heading">¶</a></h2>
<h2><spanclass="section-number">5.1. </span>Overview<aclass="headerlink"href="#Overview"title="Link to this heading">¶</a></h2>
<p>Benchmark sets such as <aclass="reference external"href="https://miplib.zib.de/">MIPLIB</a> or <aclass="reference external"href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/">TSPLIB</a> are usually employed to evaluate the performance of conventional MIP solvers. Two shortcomings, however, make existing benchmark sets less suitable for evaluating the performance of learning-enhanced MIP solvers: (i) while existing benchmark sets typically contain hundreds or thousands of instances, machine learning (ML) methods typically benefit from having orders of
<p>Benchmark sets such as <aclass="reference external"href="https://miplib.zib.de/">MIPLIB</a> or <aclass="reference external"href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/">TSPLIB</a> are usually employed to evaluate the performance of conventional MIP solvers. Two shortcomings, however, make existing benchmark sets less suitable for evaluating the performance of learning-enhanced MIP solvers: (i) while existing benchmark sets typically contain hundreds or thousands of instances, machine learning (ML) methods typically benefit from having orders of
magnitude more instances available for training; (ii) current machine learning methods typically provide best performance on sets of homogeneous instances, buch general-purpose benchmark sets contain relatively few examples of each problem type.</p>
magnitude more instances available for training; (ii) current machine learning methods typically provide best performance on sets of homogeneous instances, buch general-purpose benchmark sets contain relatively few examples of each problem type.</p>
<p>To tackle this challenge, MIPLearn provides random instance generators for a wide variety of classical optimization problems, covering applications from different fields, that can be used to evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. As of MIPLearn 0.3, nine problem generators are available, each customizable with user-provided probability distribution and flexible parameters. The generators can be configured, for example, to produce large sets of very
<p>To tackle this challenge, MIPLearn provides random instance generators for a wide variety of classical optimization problems, covering applications from different fields, that can be used to evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. Nine problem generators are available, each customizable with user-provided probability distribution and flexible parameters. The generators can be configured, for example, to produce large sets of very similar instances of
similar instances of same size, where only the objective function changes, or more diverse sets of instances, with various sizes and characteristics, belonging to a particular problem class.</p>
same size, where only the objective function changes, or more diverse sets of instances, with various sizes and characteristics, belonging to a particular problem class.</p>
<p>In the following, we describe the problems included in the library, their MIP formulation and the generation algorithm.</p>
<p>In the following, we describe the problems included in the library, their MIP formulation and the generation algorithm.</p>
<h2>Citing MIPLearn<aclass="headerlink"href="#citing-miplearn"title="Link to this heading">¶</a></h2>
<h2>Citing MIPLearn<aclass="headerlink"href="#citing-miplearn"title="Link to this heading">¶</a></h2>
<p>If you use MIPLearn in your research (either the solver or the included problem generators), we kindly request that you cite the package as follows:</p>
<p>If you use MIPLearn in your research (either the solver or the included problem generators), we kindly request that you cite the package as follows:</p>
<ulclass="simple">
<ulclass="simple">
<li><p><strong>Alinson S. Xavier, Feng Qiu, Xiaoyi Gu, Berkay Becu, Santanu S. Dey.</strong><em>MIPLearn: An Extensible Framework for Learning-Enhanced Optimization (Version 0.3)</em>. Zenodo (2023). DOI: <aclass="reference external"href="https://doi.org/10.5281/zenodo.4287567">https://doi.org/10.5281/zenodo.4287567</a></p></li>
<li><p><strong>Alinson S. Xavier, Feng Qiu, Xiaoyi Gu, Berkay Becu, Santanu S. Dey.</strong><em>MIPLearn: An Extensible Framework for Learning-Enhanced Optimization (Version 0.4)</em>. Zenodo (2024). DOI: <aclass="reference external"href="https://doi.org/10.5281/zenodo.4287567">https://doi.org/10.5281/zenodo.4287567</a></p></li>
</ul>
</ul>
<p>If you use MIPLearn in the field of power systems optimization, we kindly request that you cite the reference below, in which the main techniques implemented in MIPLearn were first developed:</p>
<p>If you use MIPLearn in the field of power systems optimization, we kindly request that you cite the reference below, in which the main techniques implemented in MIPLearn were first developed:</p>
"In this tutorial, we will demonstrate how to use and install the Python/Pyomo version of the package. The first step is to install Julia in your machine. See the [official Julia website for more instructions](https://julialang.org/downloads/). After Julia is installed, launch the Julia REPL, type `]` to enter package mode, then install MIPLearn:\n",
"In this tutorial, we will demonstrate how to use and install the Python/Pyomo version of the package. The first step is to install Julia in your machine. See the [official Julia website for more instructions](https://julialang.org/downloads/). After Julia is installed, launch the Julia REPL, type `]` to enter package mode, then install MIPLearn:\n",
<li><p>Julia version, compatible with the JuMP modeling language.</p></li>
<li><p>Julia version, compatible with the JuMP modeling language.</p></li>
</ul>
</ul>
<p>In this tutorial, we will demonstrate how to use and install the Python/Pyomo version of the package. The first step is to install Julia in your machine. See the <aclass="reference external"href="https://julialang.org/downloads/">official Julia website for more instructions</a>. After Julia is installed, launch the Julia REPL, type <codeclass="docutils literal notranslate"><spanclass="pre">]</span></code> to enter package mode, then install MIPLearn:</p>
<p>In this tutorial, we will demonstrate how to use and install the Python/Pyomo version of the package. The first step is to install Julia in your machine. See the <aclass="reference external"href="https://julialang.org/downloads/">official Julia website for more instructions</a>. After Julia is installed, launch the Julia REPL, type <codeclass="docutils literal notranslate"><spanclass="pre">]</span></code> to enter package mode, then install MIPLearn:</p>
"In this tutorial, we will demonstrate how to use and install the Python/Pyomo version of the package. The first step is to install Python 3.8+ in your computer. See the [official Python website for more instructions](https://www.python.org/downloads/). After Python is installed, we proceed to install MIPLearn using `pip`:\n",
"In this tutorial, we will demonstrate how to use and install the Python/Pyomo version of the package. The first step is to install Python 3.8+ in your computer. See the [official Python website for more instructions](https://www.python.org/downloads/). After Python is installed, we proceed to install MIPLearn using `pip`:\n",
"\n",
"\n",
"```\n",
"```\n",
"$ pip install MIPLearn==0.3\n",
"$ pip install MIPLearn==0.4\n",
"```\n",
"```\n",
"\n",
"\n",
"In addition to MIPLearn itself, we will also install Gurobi 10.0, a state-of-the-art commercial MILP solver. This step also install a demo license for Gurobi, which should able to solve the small optimization problems in this tutorial. A license is required for solving larger-scale problems.\n",
"In addition to MIPLearn itself, we will also install Gurobi 10.0, a state-of-the-art commercial MILP solver. This step also install a demo license for Gurobi, which should able to solve the small optimization problems in this tutorial. A license is required for solving larger-scale problems.\n",
<li><p>Julia version, compatible with the JuMP modeling language.</p></li>
<li><p>Julia version, compatible with the JuMP modeling language.</p></li>
</ul>
</ul>
<p>In this tutorial, we will demonstrate how to use and install the Python/Pyomo version of the package. The first step is to install Python 3.8+ in your computer. See the <aclass="reference external"href="https://www.python.org/downloads/">official Python website for more instructions</a>. After Python is installed, we proceed to install MIPLearn using <codeclass="docutils literal notranslate"><spanclass="pre">pip</span></code>:</p>
<p>In this tutorial, we will demonstrate how to use and install the Python/Pyomo version of the package. The first step is to install Python 3.8+ in your computer. See the <aclass="reference external"href="https://www.python.org/downloads/">official Python website for more instructions</a>. After Python is installed, we proceed to install MIPLearn using <codeclass="docutils literal notranslate"><spanclass="pre">pip</span></code>:</p>
<p>In addition to MIPLearn itself, we will also install Gurobi 10.0, a state-of-the-art commercial MILP solver. This step also install a demo license for Gurobi, which should able to solve the small optimization problems in this tutorial. A license is required for solving larger-scale problems.</p>
<p>In addition to MIPLearn itself, we will also install Gurobi 10.0, a state-of-the-art commercial MILP solver. This step also install a demo license for Gurobi, which should able to solve the small optimization problems in this tutorial. A license is required for solving larger-scale problems.</p>
