Pyomo_example_on_Google_Colab.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/OptimizationExpert/Pyomo/blob/main/Pyomo_example_on_Google_Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GxSxNDFYuq6n",
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      "source": [
        "Developed by Alireza Soroudi\n",
        "alireza.soroudi@gmail.com\n",
        "\n",
        "Linkedin News letter : https://www.linkedin.com/newsletters/optimization-in-open-source-6874020019009859585/\n",
        "\n",
        "Git hub: https://github.com/OptimizationExpert/Pyomo\n",
        "Keywords: installation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pJHU-Rpkglqq",
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      "source": [
        "This note notebook shows how to install the basic pyomo package on Google\n",
        "\n",
        "Colab, and then demonstrates the subsequent installation of glpk solver\n",
        "also shows how to use a data read from a csv\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jJrxAaqcuzFw",
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      },
      "source": [
        "## Basic installation of Pyomo\n",
        "\n",
        "We'll do a quiet installation of pyomo using `pip`.  This needs to be done once at the start of each Colab session."
      ]
    },
    {
      "cell_type": "code",
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      "metadata": {
        "id": "yTGBrqQO3vT2",
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      "outputs": [],
      "source": [
        "!pip install -q pyomo\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pa5VjPfw7Xfm",
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      "source": [
        "The installation of pyomo can be verified by entering a simple model. We'll use the model again in subsequent cells to demonstrate the installation and execution of various solvers."
      ]
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      "execution_count": null,
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        "id": "LkpGUtiS60lD",
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      "outputs": [],
      "source": [
        "from pyomo.environ import *\n",
        "\n",
        "# create a model\n",
        "\n",
        "N = 5\n",
        "\n",
        "coefficients = [i**2 for i in range(N)]\n",
        "model = AbstractModel()\n",
        "model.i = RangeSet(N)\n",
        "# declare decision variables\n",
        "model.x = Var(model.i, bounds=(0,1), within=NonNegativeReals)\n",
        "\n",
        "def ruke_coef(model,i):\n",
        "  return i**3 + i**2\n",
        "model.C = Param(model.i, rule=ruke_coef, mutable=True)\n",
        "\n",
        "# declare objective\n",
        "\n",
        "def rule_of(model):\n",
        "  return sum(model.x[i]*model.C[i] for i in model.i)\n",
        "\n",
        "model.profit = Objective(rule=rule_of, sense=maximize)\n",
        "instance = model.create_instance()\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dGbhGS4u7vyV",
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      "source": [
        "## GLPK installation\n",
        "\n",
        "Keywords: GLPK\n",
        "\n",
        "[GLPK](https://en.wikibooks.org/wiki/GLPK) is a the open-source **G**NU **L**inear **P**rogramming **K**it available for use under the GNU General Public License 3. GLPK is a single-threaded simplex solver generally suited to small to medium scale linear-integer programming problems. It is written in C with minimal dependencies and is therefore highly portable among computers and operating systems. GLPK is often 'good enough' for many examples. For larger problems users should consider higher-performance solvers, such as COIN-OR CBC, that can take advantage of multi-threaded processors."
      ]
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      "execution_count": null,
      "metadata": {
        "id": "aXNQCk6ywINU",
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      "outputs": [],
      "source": [
        "!apt-get install -y -qq glpk-utils\n",
        "opt = SolverFactory('glpk', executable='/usr/bin/glpsol')\n"
      ]
    },
    {
      "cell_type": "code",
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        "outputId": "a15a0abd-fa2f-4fd7-c6fb-dcc2241265de",
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          "text": [
            "value of x1 =  1.0\n",
            "value of x2 =  1.0\n",
            "value of x3 =  1.0\n",
            "value of x4 =  1.0\n",
            "value of x5 =  1.0\n",
            "OF =  280.0\n"
          ]
        }
      ],
      "source": [
        "results= opt.solve(instance)\n",
        "# display solution\n",
        "for i in instance.i:\n",
        "  print(f\"value of x{i} = \", value(instance.x[i]))\n",
        "print(\"OF = \" ,value(instance.profit) )"
      ]
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        "# Suppose we have a dateframe read from csv like this"
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        "df = pd.DataFrame()\n",
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        "df"
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              "   C\n",
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        "Update your instance based on dataframe"
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        "  instance.C[i] = df.loc[i-1,'C']"
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        "id": "SciO7YcKuOrG"
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      "outputs": []
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        "results= opt.solve(instance)\n",
        "# display solution\n",
        "for i in instance.i:\n",
        "  print(f\"value of x{i} = \", value(instance.x[i]))\n",
        "print(\"OF = \" ,value(instance.profit) )"
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            "value of x1 =  1.0\n",
            "value of x2 =  1.0\n",
            "value of x3 =  0.0\n",
            "value of x4 =  0.0\n",
            "value of x5 =  1.0\n",
            "OF =  12.0\n"
          ]
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