"- Python version, compatible with the Pyomo and Gurobipy modeling languages,\n",
"- Python version, compatible with the Pyomo and Gurobipy modeling languages,\n",
"- Julia version, compatible with the JuMP modeling language.\n",
"- Julia version, compatible with the JuMP modeling language.\n",
"\n",
"\n",
"In this tutorial, we will demonstrate how to use and install the Python/Gurobipy 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`:"
"In this tutorial, we will demonstrate how to use and install the Python/Gurobipy 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",
{
"$ pip install MIPLearn==0.3\n",
"cell_type": "code",
"```\n",
"execution_count": 1,
"\n",
"id": "cd8a69c1",
"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",
"metadata": {
"\n",
"ExecuteTime": {
"```\n",
"end_time": "2023-06-06T20:18:02.381829278Z",
"$ pip install 'gurobipy>=10,<10.1'\n",
"start_time": "2023-06-06T20:18:02.381532300Z"
"```"
}
},
"outputs": [],
"source": [
"# !pip install MIPLearn==0.3.0"
]
},
{
"cell_type": "markdown",
"id": "e8274543",
"metadata": {},
"source": [
"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."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "dcc8756c",
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-06T20:18:15.537811992Z",
"start_time": "2023-06-06T20:18:13.449177860Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: gurobipy<10.1,>=10 in /home/axavier/Software/anaconda3/envs/miplearn/lib/python3.8/site-packages (10.0.1)\n"
"- Python version, compatible with the Pyomo and Gurobipy modeling languages,\n",
"- Python version, compatible with the Pyomo and Gurobipy modeling languages,\n",
"- Julia version, compatible with the JuMP modeling language.\n",
"- Julia version, compatible with the JuMP modeling language.\n",
"\n",
"\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`:"
"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",
{
"$ pip install MIPLearn==0.3\n",
"cell_type": "code",
"```\n",
"execution_count": 1,
"\n",
"id": "cd8a69c1",
"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",
"metadata": {
"\n",
"ExecuteTime": {
"```\n",
"end_time": "2023-06-06T19:57:33.202580815Z",
"$ pip install 'gurobipy>=10,<10.1'\n",
"start_time": "2023-06-06T19:57:33.198341886Z"
"```"
}
},
"outputs": [],
"source": [
"# !pip install MIPLearn==0.3.0"
]
},
{
"cell_type": "markdown",
"id": "e8274543",
"metadata": {},
"source": [
"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."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "dcc8756c",
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-06T19:57:35.756831801Z",
"start_time": "2023-06-06T19:57:33.201767088Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: gurobipy<10.1,>=10 in /home/axavier/Software/anaconda3/envs/miplearn/lib/python3.8/site-packages (10.0.1)\n"
"- Python version, compatible with the Pyomo and Gurobipy modeling languages,\n",
"- Python version, compatible with the Pyomo and Gurobipy modeling languages,\n",
"- Julia version, compatible with the JuMP modeling language.\n",
"- Julia version, compatible with the JuMP modeling language.\n",
"\n",
"\n",
"In this tutorial, we will demonstrate how to use and install the Python/Gurobipy 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`:"
"In this tutorial, we will demonstrate how to use and install the Python/Gurobipy 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",
{
"$ pip install MIPLearn==0.3\n",
"cell_type": "code",
"```\n",
"execution_count": 1,
"\n",
"id": "cd8a69c1",
"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",
"metadata": {
"\n",
"ExecuteTime": {
"```\n",
"end_time": "2023-06-06T20:18:02.381829278Z",
"$ pip install 'gurobipy>=10,<10.1'\n",
"start_time": "2023-06-06T20:18:02.381532300Z"
"```"
}
},
"outputs": [],
"source": [
"# !pip install MIPLearn==0.3.0"
]
},
{
"cell_type": "markdown",
"id": "e8274543",
"metadata": {},
"source": [
"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."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "dcc8756c",
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-06T20:18:15.537811992Z",
"start_time": "2023-06-06T20:18:13.449177860Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: gurobipy<10.1,>=10 in /home/axavier/Software/anaconda3/envs/miplearn/lib/python3.8/site-packages (10.0.1)\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/Gurobipy 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/Gurobipy 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>
Requirement already satisfied: gurobipy<10.1,>=10 in /home/axavier/Software/anaconda3/envs/miplearn/lib/python3.8/site-packages (10.0.1)
</pre></div></div>
</div>
<divclass="admonition note">
<divclass="admonition note">
<pclass="admonition-title">Note</p>
<pclass="admonition-title">Note</p>
<p>In the code above, we install specific version of all packages to ensure that this tutorial keeps running in the future, even when newer (and possibly incompatible) versions of the packages are released. This is usually a recommended practice for all Python projects.</p>
<p>In the code above, we install specific version of all packages to ensure that this tutorial keeps running in the future, even when newer (and possibly incompatible) versions of the packages are released. This is usually a recommended practice for all Python projects.</p>
@ -375,6 +357,7 @@ Requirement already satisfied: gurobipy<10.1,>=10 in /home/axavier/Softwar
"- Python version, compatible with the Pyomo and Gurobipy modeling languages,\n",
"- Python version, compatible with the Pyomo and Gurobipy modeling languages,\n",
"- Julia version, compatible with the JuMP modeling language.\n",
"- Julia version, compatible with the JuMP modeling language.\n",
"\n",
"\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`:"
"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",
{
"$ pip install MIPLearn==0.3\n",
"cell_type": "code",
"```\n",
"execution_count": 1,
"\n",
"id": "cd8a69c1",
"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",
"metadata": {
"\n",
"ExecuteTime": {
"```\n",
"end_time": "2023-06-06T19:57:33.202580815Z",
"$ pip install 'gurobipy>=10,<10.1'\n",
"start_time": "2023-06-06T19:57:33.198341886Z"
"```"
}
},
"outputs": [],
"source": [
"# !pip install MIPLearn==0.3.0"
]
},
{
"cell_type": "markdown",
"id": "e8274543",
"metadata": {},
"source": [
"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."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "dcc8756c",
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-06T19:57:35.756831801Z",
"start_time": "2023-06-06T19:57:33.201767088Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: gurobipy<10.1,>=10 in /home/axavier/Software/anaconda3/envs/miplearn/lib/python3.8/site-packages (10.0.1)\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>
Requirement already satisfied: gurobipy<10.1,>=10 in /home/axavier/Software/anaconda3/envs/miplearn/lib/python3.8/site-packages (10.0.1)
</pre></div></div>
</div>
<divclass="admonition note">
<divclass="admonition note">
<pclass="admonition-title">Note</p>
<pclass="admonition-title">Note</p>
<p>In the code above, we install specific version of all packages to ensure that this tutorial keeps running in the future, even when newer (and possibly incompatible) versions of the packages are released. This is usually a recommended practice for all Python projects.</p>
<p>In the code above, we install specific version of all packages to ensure that this tutorial keeps running in the future, even when newer (and possibly incompatible) versions of the packages are released. This is usually a recommended practice for all Python projects.</p>
@ -586,7 +568,7 @@ machines. The code below generates the files <code class="docutils literal notra
