Kidney_matching_problem.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Mayurd9/KIDNEY-MATCHING-PROBLEM/blob/main/Kidney_matching_problem_using_integer_programming_(1).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "$\\textbf{Project Title: Implementation of integer programming models for kidney exchange problem}$"
      ],
      "metadata": {
        "id": "wYstKG999PUt"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "znJFW6OOHYPx"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import networkx as nx\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Problem Description:**"
      ],
      "metadata": {
        "id": "P8YMdYUoWCNx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "weight= np.array([[0,1,11,6,3,18,0,5,1,4],\n",
        "                  [4,0,9,5,15,9,8,0,9,1 ],\n",
        "                  [0,19,0,8,5,14,4,8,4,3],\n",
        "                  [1,13,17,0,0,8,0,1,0,7],\n",
        "                  [22,3,4,16,0,2,1,6,2,0],\n",
        "                  [19,3,0,6,23,0,7,3,1,8],\n",
        "                  [0,4,7,0,2,7,0,7,20,0 ],\n",
        "                  [8,5,2,3,9,0,6,0,8,18 ],\n",
        "                  [1,4,8,0,5,3,2,15,0,4 ],\n",
        "                  [4,0,1,6,9,6,18,4,6,0 ]])"
      ],
      "metadata": {
        "id": "hg-NA-M7X0_1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "G = nx.DiGraph()\n",
        "\n",
        "weight= np.array([[0,1,11,6,3,18,0,5,1,4],\n",
        "                  [4,0,9,5,15,9,8,0,9,1 ],\n",
        "                  [0,19,0,8,5,14,4,8,4,3],\n",
        "                  [1,13,17,0,0,8,0,1,0,7],\n",
        "                  [22,3,4,16,0,2,1,6,2,0],\n",
        "                  [19,3,0,6,23,0,7,3,1,8],\n",
        "                  [0,4,7,0,2,7,0,7,20,0 ],\n",
        "                  [8,5,2,3,9,0,6,0,8,18 ],\n",
        "                  [1,4,8,0,5,3,2,15,0,4 ],\n",
        "                  [4,0,1,6,9,6,18,4,6,0 ]])\n",
        "\n",
        "X=np.ones((10,10))\n",
        "\n",
        "# Define node names\n",
        "node_names = {0: '1', 1: '2', 2: '3', 3: '4', 4: '5', 5: '6',6:'7',7:'8',8:'9',9:'10'}\n",
        "\n",
        "# Add edges to the graph based on the decision variables matrix\n",
        "for i in range(X.shape[0]):\n",
        "    for j in range(X.shape[1]):\n",
        "        if X[i, j] == 1:\n",
        "            G.add_edge(node_names[i], node_names[j], weight=weight[i, j])\n",
        "\n",
        "# Plot the graph\n",
        "plt.figure(figsize=(8, 6))\n",
        "pos = nx.spring_layout(G)\n",
        "nx.draw(G, pos, with_labels=True, arrows=True, node_size=900, node_color='orange', font_size=10, font_weight='bold', edge_color='gray')\n",
        "edge_labels = {(u, v): str(G[u][v]['weight']) for u, v in G.edges()}\n",
        "nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels, font_color='black', font_size=10)\n",
        "plt.title('All possible edges')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 659
        },
        "id": "eBGH5Tu0WQ5q",
        "outputId": "8ff1d785-7fd8-4f7d-f623-0c843e444888"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Solution using different approaches:**"
      ],
      "metadata": {
        "id": "BdvCXi9VWKHG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **1) Edge formulation**"
      ],
      "metadata": {
        "id": "TmA0ewD5Y9Z1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip -q install pyomo\n",
        "from pyomo.environ import *\n",
        "!apt-get install -y -qq glpk-utils"
      ],
      "metadata": {
        "collapsed": true,
        "id": "q_hc1OzLHoBQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7d50ac6d-364a-4b7b-d393-43b03330bd8e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.8/12.8 MB\u001b[0m \u001b[31m69.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.6/49.6 kB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hSelecting previously unselected package libsuitesparseconfig5:amd64.\n",
            "(Reading database ... 121913 files and directories currently installed.)\n",
            "Preparing to unpack .../libsuitesparseconfig5_1%3a5.10.1+dfsg-4build1_amd64.deb ...\n",
            "Unpacking libsuitesparseconfig5:amd64 (1:5.10.1+dfsg-4build1) ...\n",
            "Selecting previously unselected package libamd2:amd64.\n",
            "Preparing to unpack .../libamd2_1%3a5.10.1+dfsg-4build1_amd64.deb ...\n",
            "Unpacking libamd2:amd64 (1:5.10.1+dfsg-4build1) ...\n",
            "Selecting previously unselected package libcolamd2:amd64.\n",
            "Preparing to unpack .../libcolamd2_1%3a5.10.1+dfsg-4build1_amd64.deb ...\n",
            "Unpacking libcolamd2:amd64 (1:5.10.1+dfsg-4build1) ...\n",
            "Selecting previously unselected package libglpk40:amd64.\n",
            "Preparing to unpack .../libglpk40_5.0-1_amd64.deb ...\n",
            "Unpacking libglpk40:amd64 (5.0-1) ...\n",
            "Selecting previously unselected package glpk-utils.\n",
            "Preparing to unpack .../glpk-utils_5.0-1_amd64.deb ...\n",
            "Unpacking glpk-utils (5.0-1) ...\n",
            "Setting up libsuitesparseconfig5:amd64 (1:5.10.1+dfsg-4build1) ...\n",
            "Setting up libamd2:amd64 (1:5.10.1+dfsg-4build1) ...\n",
            "Setting up libcolamd2:amd64 (1:5.10.1+dfsg-4build1) ...\n",
            "Setting up libglpk40:amd64 (5.0-1) ...\n",
            "Setting up glpk-utils (5.0-1) ...\n",
            "Processing triggers for man-db (2.10.2-1) ...\n",
            "Processing triggers for libc-bin (2.35-0ubuntu3.4) ...\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_0.so.3 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_5.so.3 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbb.so.12 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind.so.3 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc_proxy.so.2 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc.so.2 is not a symbolic link\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = ConcreteModel()\n",
        "\n",
        "# since variable are two dimensional ie., x_{ij}, therefore both row and column indicies are defined below\n",
        "n = 10\n",
        "row_indices = np.arange(n)\n",
        "col_indices = np.arange(n)\n",
        "\n",
        "# defining varibles used in model x_{ij}\n",
        "model.x = Var(row_indices, col_indices, domain = Binary)\n",
        "\n",
        "# Set objective function coefficients\n",
        "weight= np.array([[0,1,11,6,3,18,0,5,1,4],\n",
        "                  [4,0,9,5,15,9,8,0,9,1 ],\n",
        "                  [0,19,0,8,5,14,4,8,4,3],\n",
        "                  [1,13,17,0,0,8,0,1,0,7],\n",
        "                  [22,3,4,16,0,2,1,6,2,0],\n",
        "                  [19,3,0,6,23,0,7,3,1,8],\n",
        "                  [0,4,7,0,2,7,0,7,20,0 ],\n",
        "                  [8,5,2,3,9,0,6,0,8,18 ],\n",
        "                  [1,4,8,0,5,3,2,15,0,4 ],\n",
        "                  [4,0,1,6,9,6,18,4,6,0 ]])\n",
        "\n",
        "# Define the objective function\n",
        "model.Objective = Objective(expr = sum(weight[i,j]*model.x[i,j] for i in row_indices for j in col_indices), sense = maximize)\n",
        "\n",
        "# Define the constraints\n",
        "model.constraints = ConstraintList()\n",
        "\n",
        "#Adding constraints\n",
        "for i in row_indices:\n",
        "  model.constraints.add(sum(model.x[j,i] for j in col_indices) == sum(model.x[i,j] for j in col_indices))\n",
        "  model.constraints.add(sum(model.x[i,j] for j in col_indices)<=1)\n",
        "\n",
        "#Making subsets\n",
        "import itertools\n",
        "subsets=[]\n",
        "subset_length=5\n",
        "for subset in itertools.permutations(range(10), subset_length):\n",
        "  subsets.append(list(subset))\n",
        "\n",
        "for s in subsets:\n",
        "  model.constraints.add(sum(model.x[s[i],s[i+1]] for i in range(4))<=3)\n",
        "\n",
        "# Solve the problem using GLPK solver\n",
        "solver = SolverFactory('glpk',executable='/usr/bin/glpsol')\n",
        "results = solver.solve(model)\n",
        "\n",
        "print(results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5RmUDr_HHo0b",
        "outputId": "5f65f453-37c7-4746-d12c-4d41baecdde9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Problem: \n",
            "- Name: unknown\n",
            "  Lower bound: 175.0\n",
            "  Upper bound: 175.0\n",
            "  Number of objectives: 1\n",
            "  Number of constraints: 30260\n",
            "  Number of variables: 100\n",
            "  Number of nonzeros: 121240\n",
            "  Sense: maximize\n",
            "Solver: \n",
            "- Status: ok\n",
            "  Termination condition: optimal\n",
            "  Statistics: \n",
            "    Branch and bound: \n",
            "      Number of bounded subproblems: 1\n",
            "      Number of created subproblems: 1\n",
            "  Error rc: 0\n",
            "  Time: 0.6754093170166016\n",
            "Solution: \n",
            "- number of solutions: 0\n",
            "  number of solutions displayed: 0\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Displaying the solution\n",
        "\n",
        "print('Maximum objective value (Maximum weight) = ', value(model.Objective))\n",
        "print('\\nDecision Variables matrix:')\n",
        "X=np.zeros((10,10))\n",
        "for i in row_indices:\n",
        "  for j in col_indices:\n",
        "    X[i,j]=model.x[i,j].value\n",
        "\n",
        "print()\n",
        "print('Solution matrix:')\n",
        "print('X=',X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sqve1WqtQqPa",
        "outputId": "8d45127b-d7d8-4d76-fe62-c06af6b3a575"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Maximum objective value (Maximum weight) =  175.0\n",
            "\n",
            "Decision Variables matrix:\n",
            "\n",
            "Solution matrix:\n",
            "X= [[0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Displaying the solution using graph\n",
        "\n",
        "G = nx.DiGraph()\n",
        "\n",
        "# Define node names\n",
        "node_names = {0: '1', 1: '2', 2: '3', 3: '4', 4: '5', 5: '6',6:'7',7:'8',8:'9',9:'10'}\n",
        "\n",
        "# Add edges to the graph based on the decision variables matrix\n",
        "for i in range(X.shape[0]):\n",
        "    for j in range(X.shape[1]):\n",
        "        if X[i, j] == 1:\n",
        "            G.add_edge(node_names[i], node_names[j], weight=weight[i, j])\n",
        "\n",
        "# Plot the graph\n",
        "plt.figure(figsize=(8, 6))\n",
        "pos = nx.circular_layout(G)\n",
        "nx.draw(G, pos, with_labels=True, arrows=True, node_size=900, node_color='orange', font_size=10, font_weight='bold', edge_color='gray', arrowsize=20)\n",
        "edge_labels = {(u, v): str(G[u][v]['weight']) for u, v in G.edges()}\n",
        "nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels, font_color='black', font_size=10)\n",
        "plt.title('Cycles formed')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 659
        },
        "id": "dfVnC-RWUCEt",
        "outputId": "dbcba6ab-b24f-4ed0-be06-8eda4f852c17"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **2) Cycle formulation**"
      ],
      "metadata": {
        "id": "AMThrDuFZQtL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = ConcreteModel()\n",
        "\n",
        "import itertools\n",
        "all_cycles=[]\n",
        "for subset_length in range(3,5):\n",
        "  for subset in itertools.permutations(range(10), subset_length):\n",
        "    all_cycles.append(list(subset))\n",
        "\n",
        "weight= np.array([[0,1,11,6,3,18,0,5,1,4],\n",
        "                  [4,0,9,5,15,9,8,0,9,1 ],\n",
        "                  [0,19,0,8,5,14,4,8,4,3],\n",
        "                  [1,13,17,0,0,8,0,1,0,7],\n",
        "                  [22,3,4,16,0,2,1,6,2,0],\n",
        "                  [19,3,0,6,23,0,7,3,1,8],\n",
        "                  [0,4,7,0,2,7,0,7,20,0 ],\n",
        "                  [8,5,2,3,9,0,6,0,8,18 ],\n",
        "                  [1,4,8,0,5,3,2,15,0,4 ],\n",
        "                  [4,0,1,6,9,6,18,4,6,0 ]])\n",
        "\n",
        "cycle_weight=[]\n",
        "for C in all_cycles:\n",
        "  w=weight[C[-1],C[0]]\n",
        "  for i in range(len(C)-1):\n",
        "    w+=weight[C[i],C[i+1]]\n",
        "  cycle_weight.append(w)\n",
        "\n",
        "# defining varibles\n",
        "model.x = Var(np.arange(len(all_cycles)), domain = Binary)\n",
        "\n",
        "# Define the objective function\n",
        "model.Objective = Objective(expr = sum(cycle_weight[i]*model.x[i] for i in range(len(all_cycles))), sense = maximize)\n",
        "\n",
        "# Define the constraints\n",
        "model.constraints = ConstraintList()\n",
        "\n",
        "#Adding constraints\n",
        "for i in range(10):\n",
        "  term=0\n",
        "  for C in all_cycles:\n",
        "    if i in C:\n",
        "      term+=model.x[all_cycles.index(C)]\n",
        "\n",
        "  model.constraints.add(term <=1)\n",
        "\n",
        "# Solve the problem using GLPK solver\n",
        "solver = SolverFactory('glpk',executable='/usr/bin/glpsol')\n",
        "results = solver.solve(model)\n",
        "\n",
        "print(results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3hu2CphzPBJu",
        "outputId": "7ac99820-590b-44f4-8ba8-e5edab63484f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Problem: \n",
            "- Name: unknown\n",
            "  Lower bound: 175.0\n",
            "  Upper bound: 175.0\n",
            "  Number of objectives: 1\n",
            "  Number of constraints: 10\n",
            "  Number of variables: 5760\n",
            "  Number of nonzeros: 22320\n",
            "  Sense: maximize\n",
            "Solver: \n",
            "- Status: ok\n",
            "  Termination condition: optimal\n",
            "  Statistics: \n",
            "    Branch and bound: \n",
            "      Number of bounded subproblems: 1\n",
            "      Number of created subproblems: 1\n",
            "  Error rc: 0\n",
            "  Time: 0.061835527420043945\n",
            "Solution: \n",
            "- number of solutions: 0\n",
            "  number of solutions displayed: 0\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Displaying the solution\n",
        "\n",
        "print('Maximum objective value (Maximum weight) = ', value(model.Objective))\n",
        "print('\\nDecision Variables matrix:')\n",
        "chosen_cycles=[]\n",
        "for i in range(len(all_cycles)):\n",
        "    # print('C',i,'=', model.x[i].value)\n",
        "    if model.x[i].value==1:\n",
        "      chosen_cycles.append(i)\n",
        "\n",
        "X=np.zeros((10,10))\n",
        "for k in chosen_cycles:\n",
        "  for i in range(len(all_cycles[k])-1):\n",
        "    X[all_cycles[k][i],all_cycles[k][i+1]]=1\n",
        "  X[all_cycles[k][-1],all_cycles[k][0]]=1\n",
        "\n",
        "print()\n",
        "print('Solution matrix:')\n",
        "print('X=',X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uwI4d9VYcJfY",
        "outputId": "9bab11c3-16f6-4878-f6db-f05047488282"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Maximum objective value (Maximum weight) =  175.0\n",
            "\n",
            "Decision Variables matrix:\n",
            "\n",
            "Solution matrix:\n",
            "X= [[0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Displaying the solution using graph\n",
        "\n",
        "G = nx.DiGraph()\n",
        "\n",
        "# Define node names\n",
        "node_names = {0: '1', 1: '2', 2: '3', 3: '4', 4: '5', 5: '6',6:'7',7:'8',8:'9',9:'10'}\n",
        "\n",
        "# Add edges to the graph based on the decision variables matrix\n",
        "for i in range(X.shape[0]):\n",
        "    for j in range(X.shape[1]):\n",
        "        if X[i, j] == 1:\n",
        "            G.add_edge(node_names[i], node_names[j], weight=weight[i, j])\n",
        "\n",
        "# Plot the graph\n",
        "plt.figure(figsize=(8, 6))\n",
        "pos = nx.circular_layout(G)\n",
        "nx.draw(G, pos, with_labels=True, arrows=True, node_size=900, node_color='skyblue', font_size=10, font_weight='bold', edge_color='gray', arrowsize=15)\n",
        "edge_labels = {(u, v): str(G[u][v]['weight']) for u, v in G.edges()}\n",
        "nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels, font_color='black', font_size=10)\n",
        "plt.title('Cycles formed')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 659
        },
        "id": "G1Lv74z73Mtw",
        "outputId": "a5c8073d-2609-47cd-fae5-0c9a595c1a40"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **3) Reduced Edge Assignment Formulation**"
      ],
      "metadata": {
        "id": "Tn7aG1BZqbnS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = ConcreteModel()\n",
        "\n",
        "# since variable are two dimensional ie., x_{ij}, therefore both row and column indicies are defined below\n",
        "n = 10\n",
        "row_indices = np.arange(n)\n",
        "col_indices = np.arange(n)\n",
        "L= np.arange(n)                            #length of V\n",
        "k=4                            #maximum allowable cycle length\n",
        "\n",
        "# defining varibles used in model\n",
        "model.x = Var(row_indices, col_indices, domain = Binary)       #6(h)\n",
        "model.y = Var(row_indices, L , domain = Binary)                #6(i)\n",
        "\n",
        "weight= np.array([[0,1,11,6,3,18,0,5,1,4],\n",
        "                  [4,0,9,5,15,9,8,0,9,1 ],\n",
        "                  [0,19,0,8,5,14,4,8,4,3],\n",
        "                  [1,13,17,0,0,8,0,1,0,7],\n",
        "                  [22,3,4,16,0,2,1,6,2,0],\n",
        "                  [19,3,0,6,23,0,7,3,1,8],\n",
        "                  [0,4,7,0,2,7,0,7,20,0 ],\n",
        "                  [8,5,2,3,9,0,6,0,8,18 ],\n",
        "                  [1,4,8,0,5,3,2,15,0,4 ],\n",
        "                  [4,0,1,6,9,6,18,4,6,0 ]])\n",
        "\n",
        "# Define the objective function   6(a)\n",
        "model.Objective = Objective(expr = sum(weight[i,j]*model.x[i,j] for i in row_indices for j in col_indices), sense = maximize)\n",
        "\n",
        "# Define the constraints\n",
        "model.constraints = ConstraintList()\n",
        "\n",
        "#Adding constraints\n",
        "for i in row_indices:\n",
        "  for l in L:\n",
        "    if l>i:\n",
        "      model.constraints.add(model.y[i,l]==0)\n",
        "\n",
        "    model.constraints.add(model.y[i,l]<=model.y[l,l])   #6(g)\n",
        "\n",
        "for i in row_indices:\n",
        "  model.constraints.add(sum(model.x[j,i] for j in col_indices) == sum(model.x[i,j] for j in col_indices))    #6(b)\n",
        "  model.constraints.add(sum(model.x[i,j] for j in col_indices)<=1)                                           #6(c)\n",
        "  model.constraints.add(sum(model.y[i,l] for l in L) == sum(model.x[i,j] for j in col_indices))              #6(e)\n",
        "\n",
        "for l in L:\n",
        "  model.constraints.add(sum(model.y[i,l] for i in row_indices)<=k)     #6(d)\n",
        "\n",
        "for l in L:\n",
        "  for i in row_indices:\n",
        "    for j in col_indices:\n",
        "      model.constraints.add(model.y[i,l] + model.x[i,j] <= 1 + model.y[j,l])\n",
        "\n",
        "# Solve the problem using GLPK solver\n",
        "solver = SolverFactory('glpk',executable='/usr/bin/glpsol')\n",
        "results = solver.solve(model)\n",
        "\n",
        "print(results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sZB5gU3K42R5",
        "outputId": "3cb4b5ee-2e77-43fa-82f7-e76c38188494"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Problem: \n",
            "- Name: unknown\n",
            "  Lower bound: 175.0\n",
            "  Upper bound: 175.0\n",
            "  Number of objectives: 1\n",
            "  Number of constraints: 1185\n",
            "  Number of variables: 201\n",
            "  Number of nonzeros: 3605\n",
            "  Sense: maximize\n",
            "Solver: \n",
            "- Status: ok\n",
            "  Termination condition: optimal\n",
            "  Statistics: \n",
            "    Branch and bound: \n",
            "      Number of bounded subproblems: 17\n",
            "      Number of created subproblems: 17\n",
            "  Error rc: 0\n",
            "  Time: 0.027985095977783203\n",
            "Solution: \n",
            "- number of solutions: 0\n",
            "  number of solutions displayed: 0\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Displaying the solution\n",
        "\n",
        "print('Maximum objective value (Maximum weight) = ', value(model.Objective))\n",
        "print('\\nDecision Variables matrix:')\n",
        "X=np.zeros((10,10))\n",
        "for i in row_indices:\n",
        "  for j in col_indices:\n",
        "    #print('X',i,j,'=', model.x[i,j].value)\n",
        "    X[i,j]=model.x[i,j].value\n",
        "\n",
        "Y=np.zeros((10,10))\n",
        "for i in row_indices:\n",
        "  for l in L:\n",
        "    Y[i,l]= model.y[i,l].value\n",
        "\n",
        "print()\n",
        "print('Solution matrix:')\n",
        "print('X=',X)\n",
        "print()\n",
        "print('y=',Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J6oaTxF8pXOF",
        "outputId": "36997669-1a3c-47c1-d348-b1dae09e0506"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Maximum objective value (Maximum weight) =  175.0\n",
            "\n",
            "Decision Variables matrix:\n",
            "\n",
            "Solution matrix:\n",
            "X= [[0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]]\n",
            "\n",
            "y= [[1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Displaying the solution using graph\n",
        "\n",
        "G = nx.DiGraph()\n",
        "\n",
        "# Define node names\n",
        "node_names = {0: '1', 1: '2', 2: '3', 3: '4', 4: '5', 5: '6',6:'7',7:'8',8:'9',9:'10'}\n",
        "\n",
        "# Add edges to the graph based on the decision variables matrix\n",
        "for i in range(X.shape[0]):\n",
        "    for j in range(X.shape[1]):\n",
        "        if X[i, j] == 1:\n",
        "            G.add_edge(node_names[i], node_names[j], weight=weight[i, j])\n",
        "\n",
        "# Plot the graph\n",
        "plt.figure(figsize=(8, 6))\n",
        "pos = nx.circular_layout(G)\n",
        "nx.draw(G, pos, with_labels=True, arrows=True, node_size=900, node_color='lightgreen', font_size=10, font_weight='bold', edge_color='gray', arrowsize=15)\n",
        "edge_labels = {(u, v): str(G[u][v]['weight']) for u, v in G.edges()}\n",
        "nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels, font_color='black', font_size=10)\n",
        "plt.title('Cycles formed')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 659
        },
        "id": "5KczlvP5o4gT",
        "outputId": "3143a262-a5b4-46a2-b5cc-901b29246599"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **4) Extended Edge Formulation**"
      ],
      "metadata": {
        "id": "AEduls5r1gD_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = ConcreteModel()\n",
        "\n",
        "# since variable are two dimensional ie., x_{ij}, therefore both row and column indicies are defined below\n",
        "n = 10\n",
        "row_indices = np.arange(n)\n",
        "col_indices = np.arange(n)\n",
        "L= np.arange(n)\n",
        "k=4       #upper limit of cycle length\n",
        "\n",
        "# defining varibles\n",
        "model.x = Var(row_indices, col_indices, L , domain = Binary)   #7(e)\n",
        "\n",
        "# Set objective function coefficients\n",
        "weight= np.array([[0,1,11,6,3,18,0,5,1,4],\n",
        "                  [4,0,9,5,15,9,8,0,9,1 ],\n",
        "                  [0,19,0,8,5,14,4,8,4,3],\n",
        "                  [1,13,17,0,0,8,0,1,0,7],\n",
        "                  [22,3,4,16,0,2,1,6,2,0],\n",
        "                  [19,3,0,6,23,0,7,3,1,8],\n",
        "                  [0,4,7,0,2,7,0,7,20,0 ],\n",
        "                  [8,5,2,3,9,0,6,0,8,18 ],\n",
        "                  [1,4,8,0,5,3,2,15,0,4 ],\n",
        "                  [4,0,1,6,9,6,18,4,6,0 ]])\n",
        "\n",
        "# Define the objective function  7(a)\n",
        "model.Objective = Objective(expr = sum(sum(weight[i,j]*model.x[i,j,l] for i in row_indices for j in col_indices) for l in L), sense = maximize)\n",
        "\n",
        "# Define the constraints\n",
        "model.constraints = ConstraintList()\n",
        "\n",
        "#Adding constraints\n",
        "for l in L:\n",
        "  for i in row_indices:\n",
        "    model.constraints.add(sum(model.x[j,i,l] for j in col_indices) == sum(model.x[i,j,l] for j in col_indices))   #7(b)\n",
        "\n",
        "for i in row_indices:\n",
        "  term=0\n",
        "  for l in L:\n",
        "    term+= sum(model.x[i,j,l] for j in col_indices)       #7(c)\n",
        "\n",
        "  model.constraints.add(term<=1)\n",
        "\n",
        "for l in L:\n",
        "  model.constraints.add(sum(model.x[i,j,l] for i in row_indices for j in col_indices)<=k)    #7(d)\n",
        "\n",
        "# Solve the problem using GLPK solver\n",
        "solver = SolverFactory('glpk',executable='/usr/bin/glpsol')\n",
        "results = solver.solve(model)\n",
        "\n",
        "print(results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fa4T4eXrqWK7",
        "outputId": "c3778e7d-854f-4d26-ac6e-bc49f2940581"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Problem: \n",
            "- Name: unknown\n",
            "  Lower bound: 175.0\n",
            "  Upper bound: 175.0\n",
            "  Number of objectives: 1\n",
            "  Number of constraints: 120\n",
            "  Number of variables: 1000\n",
            "  Number of nonzeros: 3800\n",
            "  Sense: maximize\n",
            "Solver: \n",
            "- Status: ok\n",
            "  Termination condition: optimal\n",
            "  Statistics: \n",
            "    Branch and bound: \n",
            "      Number of bounded subproblems: 1\n",
            "      Number of created subproblems: 1\n",
            "  Error rc: 0\n",
            "  Time: 0.017389774322509766\n",
            "Solution: \n",
            "- number of solutions: 0\n",
            "  number of solutions displayed: 0\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Displaying the solution\n",
        "\n",
        "print('Maximum objective value (Maximum weight) = ', value(model.Objective))\n",
        "print('\\nDecision Variables matrix:')\n",
        "print()\n",
        "print('Solution matrix:')\n",
        "X_list=[]\n",
        "for l in L:\n",
        "  X=np.zeros((10,10))\n",
        "  for i in row_indices:\n",
        "    for j in col_indices:\n",
        "      X[i,j]=model.x[i,j,l].value\n",
        "\n",
        "  X_list.append(X)\n",
        "  print('X',l+1,'=',X)\n",
        "  print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kTm9J3flv1L2",
        "outputId": "e970a2af-1ac3-41ca-f463-8f96d7b405f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Maximum objective value (Maximum weight) =  175.0\n",
            "\n",
            "Decision Variables matrix:\n",
            "\n",
            "Solution matrix:\n",
            "X 1 = [[0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n",
            "\n",
            "X 2 = [[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]]\n",
            "\n",
            "X 3 = [[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n",
            "\n",
            "X 4 = [[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n",
            "\n",
            "X 5 = [[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n",
            "\n",
            "X 6 = [[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n",
            "\n",
            "X 7 = [[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n",
            "\n",
            "X 8 = [[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n",
            "\n",
            "X 9 = [[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n",
            "\n",
            "X 10 = [[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Displaying the solution using graph\n",
        "\n",
        "num=1\n",
        "\n",
        "for X in X_list:\n",
        "\n",
        "  G = nx.DiGraph()\n",
        "\n",
        "  # Define node names\n",
        "  node_names = {0: '1', 1: '2', 2: '3', 3: '4', 4: '5', 5: '6',6:'7',7:'8',8:'9',9:'10'}\n",
        "\n",
        "  # Add edges to the graph based on the decision variables matrix\n",
        "  for i in range(X.shape[0]):\n",
        "      for j in range(X.shape[1]):\n",
        "          if X[i, j] == 1:\n",
        "              G.add_edge(node_names[i], node_names[j], weight=weight[i, j])\n",
        "\n",
        "  # Plot the graph\n",
        "  print('Graph number',num,':')\n",
        "  plt.figure(figsize=(8, 6))\n",
        "  pos = nx.circular_layout(G)\n",
        "  nx.draw(G, pos, with_labels=True, arrows=True, node_size=900, node_color='lightgreen', font_size=10, font_weight='bold', edge_color='gray', arrowsize=15)\n",
        "  edge_labels = {(u, v): str(G[u][v]['weight']) for u, v in G.edges()}\n",
        "  nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels, font_color='black', font_size=10)\n",
        "  plt.title('Cycles formed')\n",
        "  plt.show()\n",
        "  num+=1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "VirinYWW0YII",
        "outputId": "06bbfc58-6047-4260-a28e-44c49867faef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Graph number 1 :\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Graph number 2 :\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Graph number 3 :\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Graph number 4 :\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Graph number 5 :\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Graph number 6 :\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Graph number 7 :\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Graph number 8 :\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Graph number 9 :\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Graph number 10 :\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Reduced Extended Edge formulation**"
      ],
      "metadata": {
        "id": "Ryf1yH4oP7Zr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = ConcreteModel()\n",
        "\n",
        "# since variable are two dimensional ie., x_{ij}, therefore both row and column indicies are defined below\n",
        "n = 10\n",
        "row_indices = np.arange(n)\n",
        "col_indices = np.arange(n)\n",
        "L= np.arange(n)\n",
        "k=4       #upper limit of cycle length\n",
        "\n",
        "# defining varibles\n",
        "model.x = Var(row_indices, col_indices, L , domain = Binary)   #7(e)\n",
        "\n",
        "# Set objective function coefficients\n",
        "weight= np.array([[0,1,11,6,3,18,0,5,1,4],\n",
        "                  [4,0,9,5,15,9,8,0,9,1 ],\n",
        "                  [0,19,0,8,5,14,4,8,4,3],\n",
        "                  [1,13,17,0,0,8,0,1,0,7],\n",
        "                  [22,3,4,16,0,2,1,6,2,0],\n",
        "                  [19,3,0,6,23,0,7,3,1,8],\n",
        "                  [0,4,7,0,2,7,0,7,20,0 ],\n",
        "                  [8,5,2,3,9,0,6,0,8,18 ],\n",
        "                  [1,4,8,0,5,3,2,15,0,4 ],\n",
        "                  [4,0,1,6,9,6,18,4,6,0 ]])\n",
        "\n",
        "# Define the objective function  7(a)\n",
        "model.Objective = Objective(expr = sum(sum(weight[i,j]*model.x[i,j,l] for i in row_indices for j in col_indices) for l in L), sense = maximize)\n",
        "\n",
        "# Define the constraints\n",
        "model.constraints = ConstraintList()\n",
        "\n",
        "#Adding constraints\n",
        "for l in L:\n",
        "  for i in row_indices:\n",
        "    for j in col_indices:\n",
        "      if i<l or j<l:\n",
        "        model.constraints.add(model.x[i,j,l]==0)\n",
        "\n",
        "for l in L:\n",
        "  for i in row_indices:\n",
        "    model.constraints.add(sum(model.x[j,i,l] for j in col_indices) == sum(model.x[i,j,l] for j in col_indices))   #7(b)\n",
        "\n",
        "for i in row_indices:\n",
        "  term=0\n",
        "  for l in L:\n",
        "    term+= sum(model.x[i,j,l] for j in col_indices)       #7(c)\n",
        "\n",
        "  model.constraints.add(term<=1)\n",
        "\n",
        "for l in L:\n",
        "  model.constraints.add(sum(model.x[i,j,l] for i in row_indices for j in col_indices)<=k)    #7(d)\n",
        "\n",
        "for l in L:\n",
        "  for i in row_indices:\n",
        "    model.constraints.add(sum(model.x[i,j,l] for j in col_indices) <= sum(model.x[l,j,l] for j in col_indices))\n",
        "\n",
        "# Solve the problem using GLPK solver\n",
        "solver = SolverFactory('glpk',executable='/usr/bin/glpsol')\n",
        "results = solver.solve(model)\n",
        "\n",
        "print(results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "97AyUv7d07N7",
        "outputId": "165fda98-4995-4999-afb6-a52a9edfcfba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Problem: \n",
            "- Name: unknown\n",
            "  Lower bound: 175.0\n",
            "  Upper bound: 175.0\n",
            "  Number of objectives: 1\n",
            "  Number of constraints: 835\n",
            "  Number of variables: 1001\n",
            "  Number of nonzeros: 6215\n",
            "  Sense: maximize\n",
            "Solver: \n",
            "- Status: ok\n",
            "  Termination condition: optimal\n",
            "  Statistics: \n",
            "    Branch and bound: \n",
            "      Number of bounded subproblems: 7\n",
            "      Number of created subproblems: 7\n",
            "  Error rc: 0\n",
            "  Time: 0.02176070213317871\n",
            "Solution: \n",
            "- number of solutions: 0\n",
            "  number of solutions displayed: 0\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Displaying the solution\n",
        "\n",
        "print('Maximum objective value (Maximum weight) = ', value(model.Objective))\n",
        "print('\\nDecision Variables matrix:')\n",
        "print()\n",
        "print('Solution matrix:')\n",
        "X_list=[]\n",
        "for l in L:\n",
        "  X=np.zeros((10,10))\n",
        "  for i in row_indices:\n",
        "    for j in col_indices:\n",
        "      X[i,j]=model.x[i,j,l].value\n",
        "\n",
        "  X_list.append(X)\n",
        "  print('X',l+1,'=',X)\n",
        "  print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x44oeJ3dOusM",
        "outputId": "b73c80fd-4172-4b3c-d1ce-2cd0d414961d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Maximum objective value (Maximum weight) =  175.0\n",
            "\n",
            "Decision Variables matrix:\n",
            "\n",
            "Solution matrix:\n",
            "X 1 = [[0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n",
            "\n",
            "X 2 = [[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n",
            "\n",
            "X 3 = [[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n",
            "\n",
            "X 4 = [[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n",
            "\n",
            "X 5 = [[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n",
            "\n",
            "X 6 = [[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n",
            "\n",
            "X 7 = [[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]]\n",
            "\n",
            "X 8 = [[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n",
            "\n",
            "X 9 = [[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n",
            "\n",
            "X 10 = [[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Displaying the solution using graph\n",
        "\n",
        "num=1\n",
        "\n",
        "for X in X_list:\n",
        "\n",
        "  G = nx.DiGraph()\n",
        "\n",
        "  # Define node names\n",
        "  node_names = {0: '1', 1: '2', 2: '3', 3: '4', 4: '5', 5: '6',6:'7',7:'8',8:'9',9:'10'}\n",
        "\n",
        "  # Add edges to the graph based on the decision variables matrix\n",
        "  for i in range(X.shape[0]):\n",
        "      for j in range(X.shape[1]):\n",
        "          if X[i, j] == 1:\n",
        "              G.add_edge(node_names[i], node_names[j], weight=weight[i, j])\n",
        "\n",
        "  # Plot the graph\n",
        "  print('Graph number',num,':')\n",
        "  plt.figure(figsize=(8, 6))\n",
        "  pos = nx.circular_layout(G)\n",
        "  nx.draw(G, pos, with_labels=True, arrows=True, node_size=900, node_color='gray', font_size=10, font_weight='bold', edge_color='gray', arrowsize=15)\n",
        "  edge_labels = {(u, v): str(G[u][v]['weight']) for u, v in G.edges()}\n",
        "  nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels, font_color='black', font_size=10)\n",
        "  plt.title('Cycles formed')\n",
        "  plt.show()\n",
        "  num+=1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "U6ZiDfBiOw-M",
        "outputId": "85092d6b-d5c5-41ea-cafb-455956258bd0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Graph number 1 :\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Graph number 2 :\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Graph number 3 :\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzMAAAKCCAYAAADlSofSAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAY40lEQVR4nO3df6yWdf3H8feBb4KiBzNPkoGIkERDsKQtcUGiJaZCm0vDCbHxq8iBrCTbcuAqJ5UJulnQDBWsAFtoI8cs0PBHW7WZWBpKQuVKVooEkQzO9f2jdeb5IorfxOMrH4+N7dyf67o/1+c+/MOT68fd0jRNUwAAAGG6dfUCAAAA/j/EDAAAEEnMAAAAkcQMAAAQScwAAACRxAwAABBJzAAAAJHEDAAAEEnMAAAAkcQMwJvMiSeeWJMnT35dj7lz586aOnVq9enTp1paWuryyy9/XY9/KLW0tNT8+fO7ehkAb0piBqALbd68uWbMmFEnnXRS9ezZs1pbW+uMM86oRYsW1e7du7t6ea+Za665pm655Zb69Kc/XcuWLauJEyd29ZIA+C/wP129AIA3qzVr1tTHP/7x6tGjR02aNKmGDh1ae/bsqfvvv7+uuOKK+s1vflNLlizp6mW+JtatW1cf+MAHat68eV29FAD+i4gZgC7w1FNP1Sc+8Ynq379/rVu3rt7xjnd0bPvMZz5TTz75ZK1Zs6YLV/ja2rZtW73nPe95zebbu3dvtbe312GHHfaazQlAHpeZAXSBr371q7Vz5866+eabO4XMvw0aNKhmz55dVVWjR4+u4cOHv+Q8gwcPrnPOOafjdXt7ey1atKhOOeWU6tmzZ7W1tdXYsWPrl7/85cuuZ/v27XX55ZdXv379qkePHjVo0KBasGBBtbe3d9rv+9//fp122ml11FFHVWtra51yyim1aNGiA8577733VktLSz311FO1Zs2aamlpqZaWltqyZUtV/StypkyZUscdd1z17Nmzhg8fXrfeemunObZs2VItLS319a9/vRYuXFgDBw6sHj161G9/+9uaP39+tbS01KZNm+rSSy+t3r17V1tbW1111VXVNE398Y9/rPHjx1dra2v16dOnrrvuuv3W+MILL9S8efNq0KBB1aNHj+rXr1/NnTu3Xnjhhf32mzNnTrW1tdVRRx1V48aNqz/96U8v+3sF4NByZgagC/zoRz+qk046qUaOHPmK+06cOLGmTZtWjz76aA0dOrRj/Be/+EVt2rSpvvjFL3aMTZkypW655ZY699xza+rUqbV3797asGFD/fznP68RI0a85Pz/+Mc/avTo0fX000/XjBkz6oQTTqgHH3ywvvCFL9Sf//znWrhwYVVV3XPPPTVhwoQ666yzasGCBVVV9dhjj9UDDzzQEV7/15AhQ2rZsmU1Z86c6tu3b332s5+tqqq2trbavXt3fehDH6onn3yyLrvsshowYECtWrWqJk+eXNu3b99vzqVLl9Y///nPmj59evXo0aOOOeaYjm0XX3xxDRkypK699tpas2ZNffnLX65jjjmmFi9eXGPGjKkFCxbU7bffXp/73Ofq/e9/f40aNaqq/hV/48aNq/vvv7+mT59eQ4YMqY0bN9b1119fmzZtqtWrV3ccY+rUqbV8+fK65JJLauTIkbVu3bo677zzXvHvD4BDqAHgdfX88883VdWMHz/+oPbfvn1707Nnz+bzn/98p/FZs2Y1vXr1anbu3Nk0TdOsW7euqapm1qxZ+83R3t7e8XP//v2bT37ykx2vv/SlLzW9evVqNm3a1Ok9V155ZdO9e/fmD3/4Q9M0TTN79uymtbW12bt370Gt+8X69+/fnHfeeZ3GFi5c2FRVs3z58o6xPXv2NKeffnpz5JFHNjt27Giapmmeeuqppqqa1tbWZtu2bZ3mmDdvXlNVzfTp0zvG9u7d2/Tt27dpaWlprr322o7x5557rjn88MM7ffZly5Y13bp1azZs2NBp3m9961tNVTUPPPBA0zRN8/DDDzdV1cycObPTfpdccklTVc28efNe9e8EgP+cy8wAXmc7duyoqqqjjjrqoPbv3bt3jR8/vr73ve9V0zRVVbVv375asWJFfexjH6tevXpVVdUPfvCDamlpecmb7FtaWg44/6pVq+qDH/xgvfWtb62//vWvHX/OPvvs2rdvX/3sZz+rqqqjjz66du3aVffcc8+r+rwH8uMf/7j69OlTEyZM6Bh7y1veUrNmzaqdO3fWfffd12n/Cy+8sNra2l5yrqlTp3b83L179xoxYkQ1TVNTpkzpGD/66KNr8ODB9fvf/77TZx8yZEi9+93v7vTZx4wZU1VV69ev71hrVdWsWbM6Hfe/6RHTAIlcZgbwOmttba2qqr///e8H/Z5JkybVihUrasOGDTVq1Kj6yU9+Us8880ynRxxv3ry5jj/++E6XXx2MJ554oh555JEDhsK2bduqqmrmzJm1cuXKOvfcc+ud73xnfeQjH6mLLrqoxo4d+6qO929bt26td73rXdWtW+f/VxsyZEjH9hcbMGDAAec64YQTOr3u3bt39ezZs4499tj9xv/2t791vH7iiSfqsccee8XPvnXr1urWrVsNHDiw0/bBgwcfcE0AHHpiBuB11traWscff3w9+uijB/2ec845p4477rhavnx5jRo1qpYvX159+vSps88++z9eT3t7e334wx+uuXPnvuT2k08+uaqq3v72t9fDDz9ca9eurbvvvrvuvvvuWrp0aU2aNGm/m/YPhcMPP/yA27p3735QY1XVcXar6l+f/ZRTTqlvfOMbL7lvv379XuUqAXg9iRmALnD++efXkiVL6qGHHqrTTz/9Fffv3r17XXLJJXXLLbfUggULavXq1TVt2rRO/2AfOHBgrV27tp599tlXdXZm4MCBtXPnzoMKo8MOO6wuuOCCuuCCC6q9vb1mzpxZixcvrquuuqoGDRp00Mesqurfv3898sgj1d7e3unszOOPP96x/VAbOHBg/frXv66zzjrrZS/F69+/f7W3t9fmzZs7nY353e9+d8jXCMCBuWcGoAvMnTu3evXqVVOnTq1nnnlmv+2bN2/e75HHEydOrOeee65mzJhRO3furEsvvbTT9gsvvLCapqmrr756v/lefDbi/7rooovqoYceqrVr1+63bfv27bV3796qqk6XZ1VVdevWrYYNG1ZVtd9jjA/GRz/60frLX/5SK1as6Bjbu3dv3XjjjXXkkUfW6NGjX/Wcr9ZFF11UTz/9dH3729/eb9vu3btr165dVVV17rnnVlXVDTfc0Gmffz/pDYCu4cwMQBcYOHBgffe73+14pPCkSZNq6NChtWfPnnrwwQc7HlH8Yu9973tr6NChHTetv+997+u0/cwzz6yJEyfWDTfcUE888USNHTu22tvba8OGDXXmmWfWZZdd9pJrueKKK+quu+6q888/vyZPnlynnXZa7dq1qzZu3Fh33HFHbdmypY499tiaOnVqPfvsszVmzJjq27dvbd26tW688cY69dRTO+5zeTWmT59eixcvrsmTJ9evfvWrOvHEE+uOO+6oBx54oBYuXHjQD0j4T0ycOLFWrlxZn/rUp2r9+vV1xhln1L59++rxxx+vlStX1tq1a2vEiBF16qmn1oQJE+qmm26q559/vkaOHFk//elP68knnzzkawTgwMQMQBcZN25cPfLII/W1r32t7rzzzvrmN79ZPXr0qGHDhtV1111X06ZN2+89kyZNqrlz53a68f/Fli5dWsOGDaubb765rrjiiurdu3eNGDHiZb/P5ogjjqj77ruvrrnmmlq1alXddttt1draWieffHJdffXV1bt376qquvTSS2vJkiV100031fbt26tPnz518cUX1/z58/e7if9gHH744XXvvffWlVdeWbfeemvt2LGjBg8eXEuXLt0v5A6Vbt261erVq+v666+v2267rX74wx/WEUccUSeddFLNnj27436hqqrvfOc71dbWVrfffnutXr26xowZU2vWrHFfDUAXamle7toDAN5QFi1aVHPmzKktW7bs9wQvAHizETMAIZqmqeHDh9fb3va2ju8/AYA3M5eZAbzB7dq1q+66665av359bdy4se68886uXhIAvCE4MwPwBrdly5YaMGBAHX300TVz5sz6yle+0tVLAoA3BDEDAABE8j0zAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABAJDEDAABEEjMAAEAkMQMAAEQSMwAAQCQxAwAARBIzAABApP8FN+mKD58VMtIAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Graph number 4 :\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Graph number 5 :\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Graph number 6 :\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Graph number 7 :\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzMAAAKCCAYAAADlSofSAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABIzUlEQVR4nO3de3hU9YH/8c+ZDLkCAUNISBBQlItAMMilRVs3KmhEgwh4W81aQVnd1uuqdX9ttc92u9pqBdzWSitY8VIRXUIRVMRoUSyoDQQqAQXBNiEXAgETcmEy5/cHmzEhF3KZmXPOnPfrefo8khkmX9pU5+N5z4xhmqYpAAAAAHAYj9UHAAAAAIDuYMwAAAAAcCTGDAAAAABHYswAAAAAcCTGDAAAAABHYswAAAAAcCTGDAAAAABHYswAAAAAcCTGDAAAAABHYswAgMsMGzZMN998c1i/Z3V1tebPn6/U1FQZhqG77747rN8/lAzD0COPPGL1MQDAlRgzAGChPXv2aMGCBTrzzDMVGxurvn376vzzz9eiRYtUW1tr9fGC5uc//7mee+453X777Vq+fLluuukmq48EAIgAXqsPAABu9cYbb2ju3LmKiYlRbm6uxo4dq4aGBn3wwQe6//779be//U1Lliyx+phB8e677+pb3/qWHn74YauPAgCIIIwZALDAl19+qeuuu05Dhw7Vu+++q0GDBgVu+7d/+zd98cUXeuONNyw8YXCVl5frnHPOCdrj+Xw++f1+RUdHB+0xAQDOQ2YGABb4xS9+oerqaj377LMthkyTs846S3fddZck6cILL9T48ePbfJyRI0fq0ksvDfza7/dr0aJFGjdunGJjY5WcnKzLLrtMn3zySYfnqaqq0t13363TTz9dMTExOuuss/TYY4/J7/e3uN8f//hHnXfeeerTp4/69u2rcePGadGiRe0+7nvvvSfDMPTll1/qjTfekGEYMgxD+/btk3Ri5MybN08pKSmKjY3V+PHj9Yc//KHFY+zbt0+GYejxxx/XwoULNXz4cMXExOizzz7TI488IsMwtHv3bt14441KTExUcnKyfvzjH8s0Tf3973/XzJkz1bdvX6WmpuqJJ55odcb6+no9/PDDOuussxQTE6PTTz9dDzzwgOrr61vd75577lFycrL69OmjnJwc/eMf/+jwv1cAQGhxZQYALPCnP/1JZ555pqZOnXrK+95000269dZbtWPHDo0dOzbw9Y8//li7d+/Wj370o8DX5s2bp+eee07Z2dmaP3++fD6fNm7cqL/85S+aOHFim49/7NgxXXjhhSouLtaCBQs0ZMgQbdq0SQ899JAOHDighQsXSpLWr1+v66+/XhdffLEee+wxSdLOnTv14YcfBobXyUaPHq3ly5frnnvu0eDBg3XfffdJkpKTk1VbW6t/+qd/0hdffKHvf//7OuOMM/Tqq6/q5ptvVlVVVavHXLZsmerq6nTbbbcpJiZGp512WuC2a6+9VqNHj9ajjz6qN954Qz/72c902mmn6ZlnntFFF12kxx57TC+++KL+/d//XZMmTdJ3v/tdSSfGX05Ojj744APddtttGj16tLZv364nn3xSu3fv1qpVqwLfY/78+XrhhRd0ww03aOrUqXr33Xc1Y8aMU/7vBwAIIRMAEFZHjhwxJZkzZ87s1P2rqqrM2NhY88EHH2zx9TvvvNNMSEgwq6urTdM0zXfffdeUZN55552tHsPv9wf+eujQoea//Mu/BH79n//5n2ZCQoK5e/fuFr/nhz/8oRkVFWV+9dVXpmma5l133WX27dvX9Pl8nTp3c0OHDjVnzJjR4msLFy40JZkvvPBC4GsNDQ3mt7/9bbN3797m0aNHTdM0zS+//NKUZPbt29csLy9v8RgPP/ywKcm87bbbAl/z+Xzm4MGDTcMwzEcffTTw9cOHD5txcXEt/uzLly83PR6PuXHjxhaP+9vf/taUZH744YemaZrm1q1bTUnmHXfc0eJ+N9xwgynJfPjhh7v83wkAoOfIzAAgzI4ePSpJ6tOnT6fun5iYqJkzZ+rll1+WaZqSpMbGRr3yyiu66qqrlJCQIEl67bXXZBhGmy+yNwyj3cd/9dVX9Z3vfEf9+/fXwYMHA/+55JJL1NjYqD//+c+SpH79+qmmpkbr16/v0p+3PWvXrlVqaqquv/76wNd69eqlO++8U9XV1Xr//fdb3H/27NlKTk5u87Hmz58f+OuoqChNnDhRpmlq3rx5ga/369dPI0eO1N69e1v82UePHq1Ro0a1+LNfdNFFkqT8/PzAWSXpzjvvbPF9I+ktpgHAicjMACDM+vbtK0n6+uuvO/17cnNz9corr2jjxo367ne/q3feeUdlZWUt3uJ4z549SktLa5Ffdcbnn3+uwsLCdodCeXm5JOmOO+7QihUrlJ2drfT0dE2fPl3XXHONLrvssi59vyb79+/X2WefLY+n5b9XGz16dOD25s4444x2H2vIkCEtfp2YmKjY2FgNGDCg1dcrKysDv/7888+1c+fOU/7Z9+/fL4/Ho+HDh7e4feTIke2eCQAQeowZAAizvn37Ki0tTTt27Oj077n00kuVkpKiF154Qd/97nf1wgsvKDU1VZdcckmPz+P3+zVt2jQ98MADbd4+YsQISdLAgQO1detWvfXWW1q3bp3WrVunZcuWKTc3t9WL9kMhLi6u3duioqI69TVJgatb0ok/+7hx4/SrX/2qzfuefvrpXTwlACCcGDMAYIErrrhCS5Ys0UcffaRvf/vbp7x/VFSUbrjhBj333HN67LHHtGrVKt16660tnrAPHz5cb731lg4dOtSlqzPDhw9XdXV1p4ZRdHS0rrzySl155ZXy+/2644479Mwzz+jHP/6xzjrrrE5/T0kaOnSoCgsL5ff7W1ydKSoqCtweasOHD9e2bdt08cUXd5jiDR06VH6/X3v27GlxNWbXrl0hPyMAoH28ZgYALPDAAw8oISFB8+fPV1lZWavb9+zZ0+otj2+66SYdPnxYCxYsUHV1tW688cYWt8+ePVumaeqnP/1pq8drfjXiZNdcc40++ugjvfXWW61uq6qqks/nk6QWeZYkeTweZWRkSFKrtzHujMsvv1ylpaV65ZVXAl/z+Xx66qmn1Lt3b1144YVdfsyuuuaaa1RcXKzf/e53rW6rra1VTU2NJCk7O1uStHjx4hb3aXqnNwCANbgyAwAWGD58uF566aXAWwrn5uZq7Nixamho0KZNmwJvUdxcZmamxo4dG3jR+oQJE1rcnpWVpZtuukmLFy/W559/rssuu0x+v18bN25UVlaWvv/977d5lvvvv1+rV6/WFVdcoZtvvlnnnXeeampqtH37dq1cuVL79u3TgAEDNH/+fB06dEgXXXSRBg8erP379+upp57SueeeG3idS1fcdttteuaZZ3TzzTfr008/1bBhw7Ry5Up9+OGHWrhwYaffIKEnbrrpJq1YsUL/+q//qvz8fJ1//vlqbGxUUVGRVqxYobfeeksTJ07Uueeeq+uvv16/+c1vdOTIEU2dOlUbNmzQF198EfIzAgDax5gBAIvk5OSosLBQv/zlL5WXl6enn35aMTExysjI0BNPPKFbb7211e/Jzc3VAw880OKF/80tW7ZMGRkZevbZZ3X//fcrMTFREydO7PDzbOLj4/X+++/r5z//uV599VU9//zz6tu3r0aMGKGf/vSnSkxMlCTdeOONWrJkiX7zm9+oqqpKqampuvbaa/XII4+0ehF/Z8TFxem9997TD3/4Q/3hD3/Q0aNHNXLkSC1btqzVkAsVj8ejVatW6cknn9Tzzz+v//3f/1V8fLzOPPNM3XXXXYHXC0nS0qVLlZycrBdffFGrVq3SRRddpDfeeIPX1QCAhQyzo/YAAGArixYt0j333KN9+/a1egcvAADchjEDAA5hmqbGjx+vpKSkwOefAADgZmRmAGBzNTU1Wr16tfLz87V9+3bl5eVZfSQAAGyBKzMAYHP79u3TGWecoX79+umOO+7Qf/3Xf1l9JAAAbIExAwAAAMCR+JwZAAAAAI7EmAEAAADgSIwZAAAAAI7EmAEAAADgSIwZAAAAAI7EmAEAAADgSIwZAAAAAI7EmAEAAADgSIwZAAAAAI7EmAEAAADgSIwZAAAAAI7EmAEAAADgSIwZAAAAAI7EmAEAAADgSIwZAAAAAI7EmAEAAADgSIwZAAAAAI7EmAEAAADgSIwZAAAAAI7EmAEAAADgSIwZAAAAAI7EmAEAAADgSIwZAAAAAI7EmAEAAADgSIwZAAAAAI7EmAEAAADgSIwZAAAAAI7EmAEAAADgSIwZAAAAAI7EmAEAAADgSIwZAAAAAI7EmAEAAADgSIwZAAAAAI7EmAEAhIxpmmpsbJRpmlYfBQAQgbxWHwAA4HymaaqsrEwlJSUqKSlRcXGxDh48KJ/PF7iP1+vVgAEDlJ6errS0NKWlpSklJUWGYVh4cgCAkxkm/7oMANBN9fX12rZtm7Zs2aLKykpJksfjkd/vb/f3NL89KSlJkydP1vjx4xUTExOWMwMAIgdjBgDQZbW1tcrPz1dBQUGLqy/d5fV6lZmZqaysLMXFxQXhhAAAN2DMAAC6ZPfu3crLy1NtbW1QXwtjGIbi4+OVk5OjESNGBO1xAQCRizEDAOiUuro6rV27Vtu3b5dhGCF5UX/T42ZkZCg7O1uxsbFB/x4AgMjBmAEAnFJ1dbWWL1+uioqKsLwzmWEYSk5OVm5urhISEkL+/QAAzsSYAQB0qLq6WkuXLlVVVVVY32LZMAz169dP8+bNY9AAANrE58wAANpVV1en5cuXh33ISCfe7rmqqkrPP/+86urqwvq9AQDOwJgBALRr7dq1YUvL2mKapioqKrRu3TpLvj8AwN4YMwCANu3atUvbt2+3bMg0MU1ThYWF2r17t6XnAADYD2MGANBKbW2tVq9eLcMwrD6KpBOvn1m9erVqa2utPgoAwEYYMwCAVvLz84P+OTI9YZqmjh07pvz8fKuPAgCwEcYMAKCF+vp6FRQU2GbINDFNUwUFBaqvr7f6KAAAm2DMAABa2LZtm3w+n9XHaJPP51NhYaHVxwAA2ARjBgAQYJqmtmzZYvUxOrR582bbXTUCAFjDa/UBAAD2UVZWpsrKyh4/Tn19vTZt2qQdO3boyJEjio2N1ahRo3TxxRcrLi6uR49dWVmp8vJypaSk9PicAABn48oMACCgpKQkKI/z8ssv6/3339ehQ4eUlJSkhoYGffLJJ1q+fLkaGxt7/PjFxcVBOCUAwOkYMwCAgJKSEnk8PftHQ3l5ufbt2ydJys7O1u23364FCxYEHv9vf/tbjx7f4/HowIEDPXoMAEBkYMwAAAKKi4vl9/t79BjNX8/S9Dk1zT+vZu/evT16fL/fz5UZAIAkXjMDAGjm4MGDPX6M5ORkDRw4UOXl5Vq7dq0++eQTVVVVBW7/+uuve/w9KioqevwYAADn48oMAEDSiSsqwXhLZo/Ho3/+53/WuHHjFB8fr8OHD2vIkCHq379/4Pae8vl8vKMZAIArMwCAE3qalzWXmJio2bNnB359/PhxPf7445KkAQMGBOV7+P1+RUVFBeWxAADOxJUZAICk4FwxaVJSUqL6+npJJ0bH+vXrA78eM2ZMUL5HMM8LAHAmrswAACSdeJG+1+sNSmpWUFCggoICnXbaaaqurtaxY8ckSd/61rc0ePDgHj++1+tt8aYCAAB3YswAAAIGDBig0tLSHj9Oenq69u3bp8OHD8s0TQ0aNEiTJk3ShAkTgnDKE28yAAAAYwYAEJCenq7y8vIev37m3HPP1bnnnhucQ53E4/EoPT09JI8NAHAWgmMAQEBaWlpQ3wggFPx+vwYNGmT1MQAANsCYAQAEpKWlWX2ETuHKDABAYswAAJpJSUlRUlKS1cfoUFJSkgYOHGj1MQAANsCYAQAEGIahyZMnW32MDk2ZMoV3MgMASGLMAABOMn78eHm99nx/GK/Xq4yMDKuPAQCwCcYMAKCFmJgYZWZm2u7qh2EYyszMVExMjNVHAQDYBGMGANBKVlaW4uLibDNoDMNQfHy8srKyrD4KAMBGGDMAgFYMw1D//v1lmqbVR5EkmaaprKwsxcbGWn0UAICNGKZd/kkFALCF4uJirVy5UnV1dUpJSdFXX31l6agxDENRUVHy+XyKjo7WoEGDlJqaqtTUVKWkpCg5Odm2r/EBAIQWYwYAIOnE1Y8tW7bo7bffVmpqqubMmaO4uDgtW7ZMFRUVlgwaj8ejAQMGaMyYMcrPz2/x9aYP92y6ipSWlqZzzz1Xw4cPD/s5AQDW4F9lAQBUV1envLw8FRUVacqUKZo2bZqioqIkSbm5uXr22WdVVVUV1kFjGIYSExOVm5uruLg4/fWvf9WRI0ckKTBkpBMj7NChQzp06JBiYmIYMwDgIrxmBgBcrri4WM8884z27duna6+9VpdddllgyEhSQkKC5s2bp+Tk5LC9IYBhGEpOTta8efOUkJAgj8fT4Yv/DcNQnz59dPHFF4flfAAAeyAzAwCXaisr69+/f7v3r6ur07p161RYWCjDMEJylabpcTMyMpSdnd3iBf9+v19PPfWUqqqq2vx9t9xyiwYPHhz0MwEA7IsxAwAu1FFWdiq7d+9WXl6eamtrgzpomt5+OScnRyNGjGjzPtu2bdOqVatafX3gwIH63ve+x7udAYDLMGYAwGWav1vZzJkzNWrUqC4/Rm1trfLz81VQUCCfz9fjM3m9XmVmZgY+36Y9fr9fixcvDrx2xjAMpaamqrKyUvHx8Zo7d67S0tJ6fB4AgDMwZgDAJbqalXVGfX29CgsLtXnzZlVWVkpq+U5jbWl+e1JSkqZMmaKMjAzFxMR06ns2XZ0xDEO9e/fW7bffrrq6Oq1cuVKlpaWaPn26Jk+ebJsP/AQAhA5jBgBcoCdZWWeYpqny8nIVFxfrwIEDKi4uVkVFRYurNl6vV8nJyUpPT9egQYOUnp6ugQMHdnl0NF2dOXr0aIvXyTQ2Nmr9+vXavHmzRo8erZycHLIzAIhwjBkAiHDByMq6yzRN+f1+eTyeoF4pKS0tVU1NTZtvw1xUVKRVq1YpLi6O7AwAIhxjBgAiVCiyMqc4fPgw2RkAuABjBgAiUKizMicgOwOAyMeYAYAIY2VWZkdkZwAQuRgzABAh3JyVnQrZGQBEJsYMAEQAsrJTIzsDgMjDmAEAhyMr6xqyMwCIHIwZAHAosrLuIzsDgMjAmAEAByIr6zmyMwBwPsYMADgMWVlwkZ0BgHMxZgDAIcjKQofsDACciTEDAA5AVhZ6ZGcA4DyMGQCwObKy8CI7AwDnYMwAgE2RlVmH7AwAnIExAwA2RFZmPbIzALA/xgwA2AxZmb2QnQGAfTFmAMAmyMrsi+wMAOyJMQMANkBWZn9kZwBgP4wZALAYWZmzkJ0BgH0wZgDAImRlzkV2BgD2wJgBAAuQlTkf2RkAWI8xAwBhRlYWWcjOAMA6jBkACBOysshFdgYA1mDMAEAYkJVFPrIzAAg/xgwAhBhZmbuQnQFA+DBmACBEyMrci+wMAMKDMQMAIUBWBrIzAAg9xgwABBlZGZojOwOA0GHMAECQkJWhPWRnABAajBkACAKyMpwK2RkABB9jBgB6iKwMXUF2BgDBw5gBgG4iK0N3kZ0BQHAwZgCgG8jK0FNkZwDQc4wZAOgisjIEE9kZAHQfYwYAOomsDKFCdgYA3cOYAYBOICtDqJGdAUDXMWYA4BTIyhBOZGcA0HmMGQBoB1kZrEJ2BgCdw5gBgDaQlcFqZGcAcGqMGQA4CVkZ7ITsDADax5gBgP9DVga7IjsDgLYxZgBAZGWwP7IzAGiNMQPA9cjK4CRkZwDwDcYMANciK4NTkZ0BwAmMGQCuRFYGpyM7AwDGDAAXIitDJCE7A+BmjBkArkFWhkhFdgbArRgzAFyBrAyRjuwMgBsxZgBEPLIyuAnZGQA3YcwAiFhkZXArsjMAbsGYARCRyMrgdmRnANyAMQMg4pCVAd8gOwMQyRgzACIGWRnQNrIzAJGKMQMgIpCVAR0jOwMQiRgzAByPrAzoPLIzAJGEMQPAscjKgO4hOwMQKRgzAByJrAzoGbIzAJGAMQPAccjKgOAhOwPgZIwZAI5BVgaEBtkZAKdizABwBLIyILTIzgA4EWMGgO2RlQHhQ3YGwEkYMwBsi6wMsAbZGQCnYMwAsCWyMsBaZGcAnIAxA8B2yMoA+yA7A2BnjBkAtkFWBtgT2RkAu2LMALAFsjLA3sjOANgRYwaA5cjKAOcoKipSXl6eYmNjyc4AWI4xA8AyZGWAM5GdAbALxgwAS5CVAc5GdgbADhgzAMKOrAyIHGRnAKzEmAEQNmRlQGQiOwNgFcYMgLAgKwMiG9kZACswZgCEHFkZ4B5kZwDCiTEDIGTIygB3IjsDEC6MGQAhQVYGuBvZGYBwYMwACDqyMgBNyM4AhBJjBkDQkJUBaAvZGYBQYcwACAqyMgAdITsDEAqMGQA9RlYGoLPIzgAEE2MGQLeRlQHoDrIzAMHCmAHQLWRlAHqC7AxAMDBmAHQZWRmAYCE7A9ATjBkAnUZWBiAUyM4AdBdjBkCnkJUBCCWyMwDdwZgBcEpkZQDChewMQFcwZgC0i6wMgBXIzgB0FmMGQJvIygBYiewMQGcwZgC0QlYGwC7IzgB0hDEDIICsDIAdkZ0BaA9jBoAksjIA9kZ2BqAtjBkAZGUAHIPsDEBzjBnAxcjKADgR2RmAJowZwKXIygA4WXeys6+++krR0dFKTU0N0ykBhBpjBnCpDRs26JNPPiErA+BoTdnZ2LFjNWPGjHbvd+DAAV111VUyTVM333yz7rjjjjCeEkCoMGaACFRdXa3evXvLNM020wvTNOX3+1VTU6O+fftacEIACJ4jR44oPj5eUVFR8ng87d7vz3/+s/bs2aMf/ehHuvzyy/W73/0ujKcEEAqMGSDC/OpXv9I777yjFStWqHfv3u3er72hAwBO1NHf0/x+f4uRs337dl1//fX67W9/qwsuuCBcRwQQAl6rDwAgeGbMmKE///nPeu211zocMpIYMgAiSkd/T/N4PIFB4/P5NHbsWNXX12vjxo2MGcDh2r8WC8Axdu3apbS0NB07dkx79uzR9OnT5fP5rD4WANhG05UZr9erTz/9VHv27NH48eMtPhWAnuLKDOBw9fX1evTRR1VfX68nnnhCAwcO1IsvvqgPP/xQx48f13e+8x1dd911io6OtvqoABA2TdlZaWmpSktL9eWXX2rnzp3atGmTNm7cqPnz5+v8888P3P/LL79USUmJBg8erKFDh1p4cgBdwWtmgAiwadMm/eIXv5DH41FjY6OKioo0efJkVVRU6O2339YDDzyge+65RykpKVYfFQDC4rbbbtMnn3yihoYGVVVVyev16pxzzlFGRoYyMjJ0ww03SJJ8Pp9uvvlmffjhh/J4PDpy5Ih+9KMf6e6777b2DwCgU7gyAzhQaWmpJAU+K2Hq1KmaPXu2Hn30UQ0cOFCvv/66RowYoV69eun3v/+97r//fn3nO9/RjBkzeOE/gIhXVVWl119/XYcOHdL777+vSZMmye/3Kz4+vsX99u7dqwcffFDvvPOOli5dqhEjRmj//v2aP3++zjrrLF1xxRUW/QkAdBavmQEcZsmSJUpLS9MzzzyjmpqawNdvuukm3X333frJT36iMWPGqFevXpKk+fPn64wzztDrr78uiRf+A4h8/fr108GDBzVr1izNnj1b77zzTmDI+P1+SSfeznnJkiVas2aNNmzYoFmzZmnMmDHKysrSsGHDtGvXLiv/CAA6iTEDOMjSpUu1ePFiTZ8+XU888YQ2bNgQ+AezJOXm5iorKyvwa7/fr8OHD8vj8ejss8+24sgAYJnXXntNDz30kGbOnKl77rlH0jdvBPDXv/5Vixcv1sKFCzVhwgQdP35ckhQXF6evv/5aBw4csOzcADqPMQM4hM/n0/Hjx3X11VfrzTff1LRp03Tfffdpx44dgfuc/CJ/j8ejDRs2qK6uTt/+9rfDfWQAsNw999yjLVu2aPfu3frrX/8a+PrPfvYzXXDBBVqwYIGkb65av/zyy/rHP/6ha6+9VtKJNxIAYF+8AQDgIJWVlZKkpKQkSdKQIUM0ceJEPf300y1e3H/06FGtWbNGn376qX7961/rqaee0q233mrJmQHALo4fP65evXpp7969uuSSS/TEE09o1qxZqq+vV0xMjPbs2aMbbrhBY8aM0X//93/zpimAA/AGAICDNI2YhoYGRUdHKy8vT+edd54mTJige++9N9CER0VFad26dfriiy+0ZcsWZWRkWHlsALCFptcSpqenKzExUWVlZZKkmJgYSQr8fXTu3LkMGcAhGDOAA0VHR6uxsVGZmZn61a9+pQcffFAZGRnKzs7W2rVrFRMTo6efflq9e/e2+qgAYCtNQcq4ceP0/PPPa9KkSaqsrNSTTz6p/fv366GHHlJ2drbFpwTQWWRmgEM1f4vlOXPmqLCwUKNHj9aaNWv0yiuvaM6cORafEADs7eqrr9ann36qXr16qU+fPlqyZInGjx/PhwwDDsKYARzM7/fL4/Fo69atmjBhgkaNGqWXXnpJ5557rtVHAwBH2LFjhwYOHKjevXu3+hwaAPbHmAFszDRN1dTUKC4uTlFRUW3eZ+3atbriiis0Z84crVixIswnBIDI0NYHCvv9fh05ckT9+vXjM7oAm+KtmQGbqqur04oVK/S73/1Ofr+/3bcH7dOnj37/+98zZACgB04eK6ZpqqGhQUuWLNGrr76quro6i04GoCNcmQFsqLi4WCtXrlRdXZ1mzpypUaNGWX0kAHCloqIi5eXlKTY2VnPnzlVaWprVRwLQDGMGsBHTNLVlyxa9/fbbSk1N1Zw5c9S/f3+rjwUArnb48GGtXLlSpaWlmj59uiZPnkx2BtgEYwawibq6OuXl5amoqEhTpkzRtGnT2n2dDAAgvBobG7V+/Xpt3rxZo0ePVk5OjmJjY60+FuB6jBnABsjKAMAZyM4Ae2HMABYiKwMA5yE7A+yDMQNYhKwMAJyL7AywB8YMYAGyMgCIDGRngLUYM0AYkZUBQOQhOwOsw5gBwoSsDAAiF9kZYA3GDBAGZGUA4A5kZ0B4MWaAECIrAwD3ITsDwocxA4QIWRkAuBfZGRAejBkgBMjKAAAS2RkQaowZIIjIygAAJyM7A0KHMQMECVkZAKA9ZGdAaDBmgCAgKwMAdAbZGRBcjBmgB8jKAABdRXYGBA9jBugmsjIAQHeRnQHBwZgBuoGsDAAQDGRnQM8wZoAuICsDAAQb2RnQfYwZoJPIygAAoUJ2BnQPYwboBLIyAEA4NM/O5syZo/T0dKuPBNgaYwboAFkZACDcyM6AzmPMAO0gKwMAWIXsDOgcxgzQBrIyAIAdkJ0BHWPMAM2QlQEA7IbsDGgfYwb4P2RlAAC7IjsD2saYAURWBgBwBrIzoCXGDFyNrAwA4DRkZ8A3GDNwLbIyAIBTkZ0BJzBm4EpkZQCASFBUVKRVq1YpLi6O7AyuxJiBq5CVAQAiDdkZ3IwxA9cgKwMARCqyM7gVYwauQFYGAHADsjO4DWMGEY2sDADgNmRncBPGDCIWWRkAwK3IzuAWjBlEJLIyAADIzhD5GDOIKGRlAAC0RHaGSMaYQcQgKwMAoG1kZ4hUjBlEBLIyAABOjewMkYYxA0cjKwMAoGvIzhBJGDNwLLIyAAC6h+wMkYIxA0ciKwMAoOfIzuB0jBk4ClkZAADBRXYGJ2PMwDHIygAACA2yMzgVYwaOQFYGAEDokZ3BaRgzsDWyMgAAwovsDE7CmIFtkZUBAGCN5tnZqFGjNHPmTLIz2BJjBrZEVgYAgPXIzmB3jBnYClkZAAD2QnYGO2PMwDbIygAAsCeyM9gVYwa2QFYGAID9kZ3BbhgzsBRZGQAAzkJ2BjthzMAyZGUAADgT2RnsgjEDS5CVAQDgfGRnsBpjBmFFVgYAQGQhO4OVGDMIG7IyAAAiE9kZrMKYQViQlQEAEPnIzhBujBmEFFkZAADuQnaGcGLMIGTIygAAcCeyM4QLYwYhQVYGAADIzhBqjBkEFVkZAABojuwMocSYQdCQlQEAgLaQnSFUGDMICrIyAABwKmRnCDbGDHqErAwAAHQF2RmCiTGDbiMrAwAA3UF2hmBhzKBbyMoAAEBPkZ2hpxgz6BKyMgAAEExkZ+gJxgw6jawMAACEAtkZuosxg04hKwMAAKFGdoauYsygQ2RlAAAgnMjO0BWMGbSLrAwAAFiB7AydxZhBm8jKAACA1cjOcCqMGbRAVgYAAOyE7AwdYcwggKwMAADYEdkZ2sOYgSSyMgAAYH9kZzgZY8blyMoAAICTkJ2hOcaMi5GVAQAAJyI7QxPGjEuRlQEAAKcjOwNjxmXIygAAQCQhO3M3xoyLkJUBAIBIRHbmXowZlyArAwAAkY7szH0YMxGOrAwAALgJ2Zm7MGYiGFkZAABwI7Iz92DMRCiyMgAA4HZkZ5GPMRNhyMoAAAC+QXYW2RgzEYSsDAAAoDWys8jFmIkQZGUAAAAdIzuLPIwZhyMrAwAA6Dyys8jCmHEwsjIAAICuIzuLHIwZhyIrAwAA6BmyM+djzDgMWRkAAEDwkJ05G2PGZhobG7Vr1y6NHDmyVTJGVgYAABB8ZGfOxZjpJtM05ff75fF4grreP/74Y61du1ZTpkzRZZddFvg6WRkAAEBoWZWdhep5pRswZk7BNE2VlZWppKREJSUlKi4u1sGDB+Xz+QL38Xq9GjBggNLT05WWlqa0tDSlpKR0+YfR5/Np4cKFqqmpkSRde+21GjlyJFkZAABAmIQyOwvn80q3YMy0o76+Xtu2bdOWLVtUWVkpSfJ4PPL7/e3+nua3JyUlafLkyRo/frxiYmI69T2brso0iY6O1uDBg7V3716yMgAAgDAJdnZmxfNKt2DMnKS2tlb5+fkqKChosZK7y+v1KjMzU1lZWYqLi2v3fidflWliGIZmz56tMWPG9PgsAAAA6LyeZmdWPa90E8ZMM7t371ZeXp5qa2sVzP9aDMNQfHy8cnJyNGLEiDbvc/JVmeZOfv0MAAAAwqO72ZmVzyvdhDGjE+8StnbtWm3fvl2GYQT1B65J0+NmZGQoOzu7xaXK9q7KNHfttdfyon8AAAALdCU7s/p5pdu4fsxUV1dr+fLlqqioCMkP28kMw1BycrJyc3OVkJAgqeOrMk2io6N13333KTo6OuRnBAAAQGunys7s8LzSbTxWH8BK1dXVWrp0adh+4KQT72JRUVGhZ599VjU1NTp+/LjeeeedFvfxeL75nyUqKkqpqanKzMxs8XUAAACE16hRo7RgwQLFx8dr6dKl2rx5c+A5pB2eV7qRa6/M1NXVadmyZWH9gWuuaUlnZGQExkzv3r01aNAgpaamKiUlRSkpKTrttNMYMQAAADZycnZ22WWX6aWXXrL8eeX3vvc91yVnrh0zr7/+unbs2GHJD1wTwzA0duxYjRkzRkOHDnXdDx8AAICTNWVn/fv3V1lZmeXPK8eNG6dZs2ZZdgYruPJf+e/atUvbt2+39AdOOnFpsOnFYQwZAAAAZxk1apSuvPJKlZaW2uJ5ZWFhoXbv3m3pOcLNdWOmtrZWq1evts2nqBqGodWrV6u2ttbqowAAAKALamtrtXbtWp5XWsh1YyY/Pz/o7/fdE6Zp6tixY8rPz7f6KAAAAOgCnldaz1Vjpr6+XgUFBbb5gWtimqYKCgpUX19v9VEAAADQCTyvtAdXjZlt27bJ5/NZfYw2+Xw+FRYWWn0MAAAAdALPK+3BNWPGNE1t2bLF6mN0qPl7lQMAAMCeeF5pH16rDxAuZWVlqqys7PHjNDQ06L333lNRUZGOHj2qqKgoJSYmavz48Zo6dWqPXgBWWVmp8vJypaSk9PicAAAACI1gPK88fPiwFi1a1O7tF154obKysrr9+G55XumaMVNSUhKUx3njjTe0bds2SVJycrLq6+tVXl6u9evXy+v1asqUKT16/OLi4oj/oQMAAHCyYDyv9Hq9Sk9Pb/G1urq6wEjq06dPj7+HG55XumrMeDwe+f3+Hj3OV199JUk666yzdOONN+r48eN67LHH5PP5VFVV1aPH9ng8OnDgQI8eAwAAAKEVjOeVffr00a233tria2+88YYqKysVGxurcePG9eiMbnle6ZoxU1xc3OMhI0lDhw7V4cOH9cUXX+jXv/616uvr5fP5NGTIEE2dOrVHj+33+1VcXNzjMwIAACB0gvW8srljx45p69atkqRJkyYpJiamR4/nlueVrhkzBw8eDMrjXHHFFTJNU9u2bVNFRYUkKSoqSikpKYqNje3x4zc9JgAAAOwpWM8rm/v44491/PhxRUVFafLkyUF5TDc8r3TFmDFNM2hvnffRRx9p27ZtOv3003XdddeppqZGy5Yt08cffyyPx6Ps7OwePb7P55Npmrb5JFkAAAB8I5jPK5v4fL7Au6NlZGQE5fUyTY8b6c8rXfHWzMG6DNjQ0BD4RNVzzjlHCQkJGjhwoIYMGSJJ2rt3b1C+T7AvWwIAACA4QvE8bdu2baqpqZGkHr9s4WSR/rzSFWPG4wnOH/P48eOBH4imd7E4fvy4ysvLJUnR0dFB+T7BOi8AAACCK9jP00zT1KZNmyRJI0aMUHJyclAfP9KfV7oiMzMMQ16vt8eXBBMSEjR06FDt379f27dvV3Fxserr6wNLevz48T0+q9frjehLgQAAAE4WrOeVTXbt2hV4O+ZgX5Vxw/PKyJ5qzQwYMCAoj3Pdddfp/PPPV1JSkr7++ms1NjYqPT1dV199dVBerBXsNQ4AAIDgCtbzSkmBqzLp6ekaNmxY0B5XcsfzSldcmZFO/ICUl5f3uBuMi4vTtGnTNG3atCCd7Bsej6fVhycBAADAXoL1vFKSbrnlliCcqDW3PK90zZWZtLQ0278Ayu/3a9CgQVYfAwAAAB3geaV9uGrMOIEbFjQAAICT8bzSPlwzZlJSUpSUlGT1MTqUlJSkgQMHWn0MAAAAdIDnlfbhmjFjGEbQPk01VKZMmRLx7zgBAADgdDyvtA/XjBnpxFsne732fM8Dr9erjIwMq48BAACATuB5pT24aszExMQoMzPTdivVMAxlZmYqJibG6qMAAACgE3heaQ+uGjOSlJWVpbi4ONv84BmGofj4eGVlZVl9FAAAAHQBzyut57oxExcXp5ycHJmmafVRJEmmaWrChAmKi4uz+igAAADopNraWn311Ve68sorbfW8Micnx1XPK+0Z+oXYyJEjNW7cOO3YscPSHz7DMNSnTx9t3LhRDQ0NmjZtmqKioiw7DwAAAFry+/06dOiQysrKVFpaqrKyMpWUlKimpkaSlJ2dbZvnlePGjdOIESMsO4MVXDlmJOnyyy9XWVmZKioqLPnB83g8GjBggG6++WYVFhbq7bff1t///nfNmTNH/fv3D/t5AAAAcILP59P69eu1f/9+HTx4UI2NjZJOPH9r/mGZ0dHRyszMVEZGhi2eV2ZnZ4f9e1vNdZlZk9jYWOXm5qpfv35h7xwNw1BiYqJyc3MVFxenKVOm6JZbbtGxY8e0ZMkSFRUVhfU8AAAA+EZjY6O2bt2qsrKywJCR1GLISNIll1yiXr162eZ5ZWxsbFi/tx24dsxIUkJCgubNm6fk5OSw/eAZhqHk5GTNmzdPCQkJga+np6drwYIFGjZsmF555RW9+eabLf7PAwAAgPCIiYnRVVdd1eF9evfurczMzMCv7fS80k0M0y6vWLJQXV2d1q1bp8LCQhmGEZLLg02Pm5GRoezs7HaXs2ma2rJli95++22lpqaSnQEAAFjkzTff1ObNm9u87fLLL9ekSZNafd1OzyvdgDHTzO7du5WXl6fa2tqg/uA1vU1eTk5Op1+UVVxcrJUrV6qurk4zZ87UqFGjgnYeAAAAnNqOHTv0+uuvt3pe2Lt3b911110dfmimnZ5XRjLGzElqa2uVn5+vgoIC+Xy+Hj+e1+tVZmZm4H3Iu6Kurk55eXkqKirSlClTeLczAACAMGhsbNT69eu1efNmDR8+XF999ZWOHz8euL29qzIns9PzykjFmGlHfX29CgsLtXnzZlVWVkpq/Q4WJ2t+e1JSkqZMmaKMjIwefQIr2RkAAED4HD58WCtXrlRpaakuvfRSTZo0SUVFRVqxYoWkzl2VOZldnldGIsbMKZimqfLychUXF+vAgQMqLi5WRUVFi3Xt9XqVnJys9PR0DRo0SOnp6Ro4cGBQX/xFdgYAABBaO3fuVF5enuLi4jR37lylpaUFbmt6/Uxnr8q0xS7PKyMJY6abTNOU3++Xx+MJ2w8X2RkAAEDwNc/KRo8erZycnFYvqm9sbNTu3bs1YsSIoD//suJ5ZaRgzDgM2RkAAEDwtJWVMSicgzHjUGRnAAAAPdNRVgZnYMw4GNkZAABA13UmK4MzMGYcjuwMAACg88jKIgtjJkKQnQEAAHSMrCzyMGYiCNkZAABAa2RlkYsxE2HIzgAAAL5BVhbZGDMRiuwMAAC4HVlZ5GPMRDCyMwAA4EZkZe7BmIlwZGcAAMBNyMrchTHjEmRnAAAg0pGVuQ9jxkXIzgAAQCQiK3MvxozLkJ0BAIBIQlbmbowZlyI7AwAATkdWBsaMi5GdAQAAJyIrQxPGjMuRnQEAACchK0NzjBlIIjsDAAD2R1aGkzFmEEB2BgAA7IisDO1hzKAFsjMAAGAnZGXoCGMGbSI7AwAAViMrw6kwZtAusjMAAGAFsjJ0FmMGHSI7AwAA4URWhq5gzKBTyM4AAECokZWhqxgz6DSyMwAAEApkZeguxgy6hOwMAAAEE1kZeoIxg24hOwMAAD1FVoaeYsyg28jOAABAd5CVIVgYM+gRsjMAANAVZGUIJsYMgoLsDAAAnApZGYKNMYOgITsDAABtIStDqDBmEFRkZwAAoDmyMoQSYwYhQXYGAADIyhBqjBmEDNkZAADuRFaGcGHMIKTIzgAAcBeyMoQTYwZhQXYGAEDkIytDuDFmEDZkZwAARCayMliFMYOwIjsDACCykJXBSowZWILsDAAA5yMrg9UYM7AM2RkAAM5EVga7YMzAUmRnAAA4C1kZ7IQxA1sgOwMAwP7IymA3jBnYBtkZAAD2RFYGu2LMwFbIzgAAsBeyMtgZYwa2RHYGAID1yMpgd4wZ2BbZGQAA1iArg1MwZmBrZGcAAIQXWRmchDEDRyA7AwAg9MjK4DSMGThGXV2dVq9erZ07d5KdAQAQRGRlcCrGDByF7AwAgOAiK4OTMWbgSGRnAAD0HFkZnI4xA8ciOwMAoHvIyhApGDNwNLIzAAC6hqwMkYQxg4hAdgYAwKmRlSHSMGYQMcjOAABoG1kZIhVjBhGF7AwAgJbIyhDJGDOISGRnAACQlSHyMWYQscjOAABuRVYGt2DMIKKRnQEA3IasDG7CmIErkJ0BANyArAxuw5iBa5CdAQAiFVkZ3IoxA1chOwMARBqyMrgZYwauRHYGAIgEZGVwO8YMXIvsDADgVGRlwAmMGbjaydnZ3Llz1a9fP6uPBQBAu8jKgG8wZgCRnQEAnIGsDGiJMQP8H7IzAIBdkZUBbWPMAM2QnQEA7KYpKysrK9P06dPJyoBmGDNAG8jOAAB2QFYGdIwxA7SD7AwAYBWyMqBzGDNAB8jOAADhRlYGdB5jBugEsjMAQDiQlQFdw5gBOonsDAAQKmRlQPcwZoAuIDsDAAQbWRnQfYwZoBvIzgAAwUBWBvQMYwboJrIzAEB3kZUBwcGYAXqA7AwA0FVkZUDwMGaAICA7AwB0BlkZEFyMGSBIyM4AAO0hKwNCgzEDBBHZGQDgZGRlQOgwZoAQIDsDAEhkZUCoMWaAECE7AwD3IisDwoMxA4QQ2RkAuA9ZGRA+jBkgDMjOAMAdyMqA8GLMAGFCdgYAkYusDLAGYwYII7IzAIg8ZGWAdRgzgAXIzgAgMpCVAdZizAAWITsDAOciKwPsgTEDWIjsDACch6wMsA/GDGADZGcA4AxkZYC9MGYAmyA7AwD7IisD7IkxA9gI2RkA2A9ZGWBfjBnAhrqTnX399ddqbGxk/ABAEJGVAfbmsfoAAFpLT0/XggULdMYZZ2jDhg1qbGxUR//eoaGhQS+88IJyc3P1/PPPh/GkABCZTNNUY2Oj3n33XZ155plasGABQwawIa7MADZmmqbq6uoUHR19ytfPrFmzRjt27NB//Md/6N5779Xjjz8eplMCQGRqbGxUQ0ODYmNjycoAm/JafQAA7TMMQ3FxcZ267xVXXKGkpCQZhqHevXurpqZGCQkJIT4hADjPn/70J5WUlGjYsGGaOHGikpKS2rxfVFRUp/8eDMAaXJkBHK6xsTFw1Wbo0KGaNGmS/ud//kepqakWnwwA7GfWrFn6+OOPNWzYMP3tb3/TD37wA/2///f/FBMTY/XRAHQDr5kBHMw0zcCQmTVrlqKjo/WTn/yk1ZBpbGy04ngAYAtNye73vvc9HT16VJs2bdIHH3ygBQsWaNWqVSRkgIORmQEOZpqmDMPQwoULtXbtWr366qsaO3Zs4Pa9e/fq0UcfVUlJibxerx555BGde+651h0YACxgGIYaGxu1fft23XbbbRoyZIgk6dJLL9XBgwdVWlqq1NRURUdHW3xSAF3FlRnAgQ4dOqTDhw/L4/GooKBA9957r3784x/rkksukcfzzf+t//3f/127du3SqFGjdPbZZ2vq1KlatmyZhScHAGuUlJSotrZWFRUVOnr0qHbs2KFrrrlGmzZt0tixY3XDDTdo7dq1ktThu0cCsBfGDOAwpmlq8eLFOv/887V161bNnj1bV111lebPn6/4+PgW9zUMQxdccIEef/xx/fKXv9TixYu1dOlS/f3vf7fo9AAQHqWlpSotLQ38+uyzz9aVV16p559/XldffbUyMjJ0ww03aN26dfrjH/+o2NhYLV68WIcPHyY7AxyEMQM4jGEYeuSRRzR06FBNmDBBlZWVeuSRR1q8TubTTz/VqlWr1KdPH3399deBr48cOVJ/+ctfdPDgQSuODgBhsWTJEqWlpemZZ55RTU1N4OuPPvqo3nnnHV177bW64IIL9Mtf/lJDhw7V5ZdfrilTpmjXrl06evSohScH0FWMGcCh1q1bp+eee07V1dV68skndeTIEUlSfn6+pk6dqkWLFunYsWN69tlnNWPGDOXn5+uHP/yhLrjgAvXu3dvi0wNAaCxdulSLFy/W9OnT9cQTT2jDhg3y+/2STlzZPv3003XkyBGdfvrpLV4j06dPHw0aNEiJiYlWHR1ANzBmAAfLzc3Vp59+qq1bt+r111+XJN1555268cYb9c4772jFihXavHmzioqK9Oyzz+rss8/WT37yE5199tkWnxwAgs/n8+n48eO6+uqr9eabb2ratGm67777tGPHDkkK5GN9+vTRihUr9NZbb2nXrl1as2aNHnroIV166aXq16+fhX8CAF3F58wAEaKqqkoJCQk677zzNGvWLP30pz8NvNvZvHnzFBUVpSVLlgTu33QbAESSyspKSQp8EOaQIUM0ceJEPf3000pJSQncb/bs2dqwYYPS0tJ09OhRPfDAA7rzzjstOTOA7uOtmYEIkZiYKL/fr0mTJmnXrl2BsdLY2CiPxxMYLk1fZ8gAiERNI6ahoUHR0dHKy8vTeeedpwkTJujee+8NvFHKa6+9ps2bN6uyslLnnHOOhg0bZuGpAXQXV2aACLNnzx5dfvnlSklJ0S233KI//elP2rdvn+bMmaOHHnrI6uMBQNg0NjYqKipKCxcu1IMPPqhXX31V2dnZWrNmjaQTHzYMwNkYM0CEWrBggb788ks1NDRo9uzZ+sEPfmD1kQAgrJrntHPmzFFhYaFGjx6tNWvWaMWKFZo9e7bFJwTQU4wZIIIdOnRIp512WuAf6M3/wc5rZgBEkvb+nub3++XxeLR161ZNmDBBo0aN0osvvqjMzEwLTgkg2Hg3MyCCnXbaaS1+ffI/6Jt/Bg0AOFVHfy/zeDxau3atJkyYoDlz5uizzz5jyAARhDEDuMDJI8Y0TX300Uf6zW9+o6KiIotOBQA9t3PnTv3617/WRx99pPZikz59+uj3v/+9VqxYEebTAQg1MjPAperq6rR69Wrt3LlTU6ZM0bRp0xQVFWX1sQCgUxobG7V+/Xpt3rxZo0ePVk5OjmJjY60+FoAwY8wALmaaprZs2aK3335bqampmjt3Lh8YB8D2Dh8+rJUrV6qsrEzTp0/XpEmTeA0g4FKMGQAqLi7WypUrVVdXp5kzZ2rUqFFWHwkA2rRz507l5eUpLi5Oc+fOVVpamtVHAmAhxgwASWRnAOyNrAxAWxgzAALIzgDYEVkZgPYwZgC0QnYGwC7IygB0hDEDoE1kZwCsRFYGoDMYMwDaRXYGwApkZQA6izED4JTIzgCEC1kZgK5gzADoFLIzAKFEVgagOxgzADqN7AxAKJCVAeguxgyALiM7AxAsZGUAeoIxA6BbyM4A9ARZGYBgYMwA6DayMwDdQVYGIFgYMwB6jOwMQGeRlQEIJsYMgKAgOwPQEbIyAKHAmAEQNGRnANpCVgYgVBgzAIKO7AxAE7IyAKHEmAEQEmRngLuRlQEIB8YMgJAhOwPciawMQLgwZgCEHNkZ4B5kZQDCiTEDICzIzoDIRlYGwAqMGQBhQ3YGRCayMgBWYcwACDuyMyBykJUBsBJjBoAlyM4AZyMrA2AHjBkAliE7A5yJrAyAXTBmAFiO7AxwDrIyAHbCmAFgC2RngL2RlQGwI8YMANs4OTubM2eO+vfvb/WxANdryspKS0t16aWXkpUBsA3GDADbITsD7IOsDICdMWYA2BLZGWAtsjIATsCYAWBbZGeANcjKADgFYwaA7ZGdAeFDVgbASRgzAByB7AwILbIyAE7EmAHgGGRnQGiQlQFwKsYMAMchOwOCh6wMgJMxZgA4EtkZ0DNkZQAiAWMGgGORnQHdQ1YGIFIwZgA4HtkZ0HlkZQAiCWMGQEQgOwM6RlYGIBIxZgBEDLIzoG1kZQAiFWMGQMQhOwO+QVYGIJIxZgBEJLIzuB1ZGQA3YMwAiFhkZ3ArsjIAbsGYARDxyM7gJmRlANyEMQPAFcjOEOnIygC4EWMGgGuQnSFSkZUBcCvGDADXITtDJCErA+BmjBkArkR2BqcjKwMAxgwAFyM7g1ORlQHACYwZAK5HdgYnISsDgG8wZgBAZGewP7IyAGiNMQMA/4fsDHZFVgYAbWPMAMBJyM5gJ2RlANA+xgwAtIHsDFYjKwOAU2PMAEA7yM5gFbIyAOgcxgwAnALZGcKJrAwAOo8xAwCdQHaGUCMrA4CuY8wAQCeRnSFUyMoAoHsYMwDQRWRnCCayMgDoPsYMAHQD2Rl6iqwMAHqOMQMA3UR2hu4iKwOA4GDMAEAPkZ2hK8jKACB4GDMAEARkZzgVsjIACD7GDAAECdkZ2kNWBgChwZgBgCAjO0NzZGUAEDqMGQAIAbIzkJUBQOgxZgAgRMjO3IusDADCgzEDACFGduYuZGUAED6MGQAIA7KzyEdWBgDhx5gBgDAhO4tcZGUAYA3GDACEGdlZZCErAwDrMGYAwAJkZ85HVgYA1mPMAIBFyM6ci6wMAOyBMQMAFiM7cxayMgCwD8YMANgA2Zn9kZUBgP0wZgDAJsjO7IusDADsiTEDADZDdmYvZGUAYF+MGQCwIbIz65GVAYD9MWYAwKbIzqxDVgYAzsCYAQCbIzsLL7IyAHAOxgwAOADZWeiRlQGA8zBmAMAhyM5Ch6wMAJyJMQMADkN2FlxkZQDgXIwZAHAgsrOeIysDAOdjzACAQ5GddR9ZGQBEBsYMADgc2VnXkJUBQORgzABABCA7OzWyMgCIPIwZAIgQZGftIysDgMjEmAGACEN21hJZGQBELsYMAEQgsjOyMgBwA8YMAEQoN2dnZGUA4A6MGQCIcJ3Jzvbs2aOEhASlpqZacMLgIisDAPdgzACAC3SUnf3jH//Q0qVLlZiYqB/84AfyeDxB+76macrv98vj8YT8yghZGQC4D2MGAFyirewsNjZWTz/9tKqrq2Wapq666iqNHz++W49dVlamkpISlZSUqLi4WAcPHpTP5wvcx+v1asCAAUpPT1daWprS0tKUkpISlJFDVgYA7sSYAQCXacrOamtrddppp6m0tFRN/yjo169fl67O1NfXa9u2bdqyZYsqKyslSR6PR36/v93f0/z2pKQkTZ48WePHj1dMTEy3/jxkZQDgXowZAHChuro6LVu2TOXl5a1u68zVmdraWuXn56ugoKDF1Zfu8nq9yszMVFZWluLi4jr1e8jKAABeqw8AAAi/gwcPqqKios3b3nvvPY0bN67dqzO7d+9WXl6eamtrFax/H+bz+fTJJ5/os88+U05OjkaMGNHh/ZtnZdnZ2WRlAOBSXJkBAJepra1t8TqZtrR1daaurk5r167V9u3bZRhG0IZMc02Pm5GRoezs7DavtJCVAQCacGUGAFxmw4YN+vrrrzu8z8lXZ6qrq7V8+fLA1ZxQ/Xuwpsfdvn27SktLlZubq4SEBElkZQCA1rgyAwAus2fPHm3dulUHDhzQoUOHAgPi5BfuT5s2TVOnTlV1dbWWLl2qqqqqkI2YthiGoX79+mnevHlqaGjg3coAAK0wZgDAxXw+nyoqKlRWVqaysjIdOHBABw4cUENDg/r27avbb79dy5YtU0VFRViHTBPDMNS3b1/V1tYqPj6erAwA0AJjBgDQgmmaqqqqUq9evfT2229rx44dlgyZ5pqGFVkZAKC54H3MMwAgIhiGof79+6u4uFjbt2+3fMhI0tGjR/XVV19ZfQwAgM0wZgAArdTW1mr16tW2eV2KYRhavXq1amtrrT4KAMBGGDMAgFby8/OD+jkyPWWapo4dO6b8/HyrjwIAsBHGDACghfr6ehUUFNhmyDQxTVMFBQWqr6+3+igAAJtgzAAAWti2bZt8Pp/Vx2iTz+dTYWGh1ccAANgEYwYAEGCaprZs2WL1MTq0efNm2101AgBYw2v1AQAA9lFWVqbKysou/Z59+/bpgw8+UElJiY4dOyZJmjFjhiZNmhS4T2NjozZu3KitW7fq6NGjSkhI0JgxY5SVlaWYmJgufb/KykqVl5crJSWlS78PABB5uDIDAAgoKSnp8u85cOCA9u7dq7i4uHbvk5eXp/fee09HjhxR//79VVNTo7/85S966aWX5Pf7u/w9i4uLu/x7AACRhyszAICAkpISeTyeLg2M8ePHa+LEiaqurtaiRYvafMym17lcdtllmjJlinbt2qWXX35Z+/fvV1FRkc4555xOfz+Px6MDBw50+v4AgMjFlRkAQEBxcXGXr5TEx8erV69e7d7+xRdfBP66abScffbZ8nq9rW7vDL/fz5UZAIAkxgwAoJmDBw8G/TGPHDkS+OuEhARJJ66uxMfHt7q9syoqKoJzOACAozFmAACSTryTWTjfkrkn70jm8/l4RzMAAGMGAHBCd16I3xmJiYmBv66pqQl8r9ra2la3d0WozgsAcA7GDABA0on0KxTOOuuswF9/9tlnkqTPP/88cBWo+e1dEarzAgCcg3czAwBIkgzDkNfr7XJq9tlnn2n9+vUtrpTk5+dr06ZNGjx4sGbPnq2xY8dqx44devPNN/Xxxx/r0KFDkqQhQ4Zo1KhRXT6r1+uVYRhd/n0AgMjCmAEABAwYMEClpaVd+j319fU6fPhwi68dO3ZMx44dU9++fSVJs2bNUlJSkrZt26ZDhw4pISFB55xzji666KJuXWFJTk7u8u8BAEQew+QVlACA/7NmzRoVFBTY+vUoHo9HEyZM0IwZM6w+CgDAYgTHAICAtLQ0Ww8Z6cQL/wcNGmT1MQAANsCYAQAEpKWlWX2ETklPT7f6CAAAG2DMAAACUlJSlJSUZPUxOpSUlKSBAwdafQwAgA0wZgAAAYZhaPLkyVYfo0NTpkzhncwAAJIYMwCAk4wfP15erz3f7NLr9SojI8PqYwAAbIIxAwBoISYmRpmZmba7+mEYhjIzMxUTE2P1UQAANsGYAQC0kpWVpbi4ONsMGsMwFB8fr6ysLKuPAgCwEcYMAKCVuLg45eTkyC4fRWaapnJychQXF2f1UQAANsKYAQC0aeTIkRo3bpzlV2cMw1BGRoZGjBhh6TkAAPbDmAEAtOvyyy9XcnKyZYPG4/EoOTlZ2dnZlnx/AIC9MWYAAO2KjY1Vbm6u+vXrF/ZBYxiGEhMTlZubq9jY2LB+bwCAMximXYJoAIBt1dTU6Pnnn1dFRUVYXkdjGIaSk5OVm5urhISEkH8/AIAzMWYAAJ1SV1endevWqbCwUIZhhGTUND1uRkaGsrOzuSIDAOgQYwYA0CW7d+9WXl6eamtrgzpomt5+OScnhxf7AwA6hTEDAOiy2tpa5efnq6CgQD6fr8eP5/V6lZmZGfh8GwAAOoMxAwDotvr6ehUWFmrz5s2qrKyUdOIdyPx+f7u/p/ntSUlJmjJlijIyMhQTExOWMwMAIgdjBgDQY6Zpqry8XMXFxTpw4ICKi4tVUVHR4qqN1+tVcnKy0tPTNWjQIKWnp2vgwIGWf44NAMC5GDMAgJAxTVN+v18ej4fRAgAIOsYMAAAAAEfiQzMBAAAAOBJjBgAAAIAjMWYAAAAAOBJjBgAAAIAjMWYAAAAAOBJjBgAAAIAjMWYAAAAAOBJjBgAAAIAjMWYAAAAAOBJjBgAAAIAjMWYAAAAAOBJjBgAAAIAjMWYAAAAAOBJjBgAAAIAjMWYAAAAAOBJjBgAAAIAjMWYAAAAAOBJjBgAAAIAjMWYAAAAAOBJjBgAAAIAjMWYAAAAAOBJjBgAAAIAjMWYAAAAAOBJjBgAAAIAjMWYAAAAAOBJjBgAAAIAjMWYAAAAAOBJjBgAAAIAjMWYAAAAAOBJjBgAAAIAjMWYAAAAAOBJjBgAAAIAjMWYAAAAAOBJjBgAAAIAjMWYAAAAAOBJjBgAAAIAjMWYAAAAAOBJjBgAAAIAj/X+wSDHeQ0a3iQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Graph number 8 :\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Graph number 9 :\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Graph number 10 :\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Comparison Of All The Approaches:**"
      ],
      "metadata": {
        "id": "BlbyvQ5knJ2G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.DataFrame({'Approach':['Edge formulation','Cycle formulation','Reduced Edge Assignment Formulation','Extended Edge Formulation','Reduced Extended Edge Formulation'],'Number of Variables':[100,5760,201,1000,1001],'Number of Constraints':[30260,10,1185,120,835],'Time Taken(seconds)':[0.4687,0.0691,0.04609,0.0285,0.02366]})\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "vimdyiH6hxNd",
        "outputId": "30b862f4-aef2-445c-f770-70caa99263c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                              Approach  Number of Variables  \\\n",
              "0                     Edge formulation                  100   \n",
              "1                    Cycle formulation                 5760   \n",
              "2  Reduced Edge Assignment Formulation                  201   \n",
              "3            Extended Edge Formulation                 1000   \n",
              "4    Reduced Extended Edge Formulation                 1001   \n",
              "\n",
              "   Number of Constraints  Time Taken(seconds)  \n",
              "0                  30260              0.46870  \n",
              "1                     10              0.06910  \n",
              "2                   1185              0.04609  \n",
              "3                    120              0.02850  \n",
              "4                    835              0.02366  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-23d8b84a-3e7b-47b0-8e21-05fdc9fbf66b\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Approach</th>\n",
              "      <th>Number of Variables</th>\n",
              "      <th>Number of Constraints</th>\n",
              "      <th>Time Taken(seconds)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Edge formulation</td>\n",
              "      <td>100</td>\n",
              "      <td>30260</td>\n",
              "      <td>0.46870</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Cycle formulation</td>\n",
              "      <td>5760</td>\n",
              "      <td>10</td>\n",
              "      <td>0.06910</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Reduced Edge Assignment Formulation</td>\n",
              "      <td>201</td>\n",
              "      <td>1185</td>\n",
              "      <td>0.04609</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Extended Edge Formulation</td>\n",
              "      <td>1000</td>\n",
              "      <td>120</td>\n",
              "      <td>0.02850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Reduced Extended Edge Formulation</td>\n",
              "      <td>1001</td>\n",
              "      <td>835</td>\n",
              "      <td>0.02366</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-23d8b84a-3e7b-47b0-8e21-05fdc9fbf66b')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-23d8b84a-3e7b-47b0-8e21-05fdc9fbf66b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-23d8b84a-3e7b-47b0-8e21-05fdc9fbf66b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-1002898d-8f16-4ccc-a53b-004e4c3503e0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1002898d-8f16-4ccc-a53b-004e4c3503e0')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1002898d-8f16-4ccc-a53b-004e4c3503e0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_e6472f0d-a099-4d00-a140-0ee98e33372f\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_e6472f0d-a099-4d00-a140-0ee98e33372f button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Approach\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Cycle formulation\",\n          \"Reduced Extended Edge Formulation\",\n          \"Reduced Edge Assignment Formulation\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Number of Variables\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2357,\n        \"min\": 100,\n        \"max\": 5760,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          5760,\n          1001,\n          201\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Number of Constraints\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13301,\n        \"min\": 10,\n        \"max\": 30260,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          10,\n          835,\n          1185\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Time Taken(seconds)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19172826682573438,\n        \"min\": 0.02366,\n        \"max\": 0.4687,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.0691,\n          0.02366,\n          0.04609\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(['Edge formulation','Cycle formulation','Reduced Edge Assignment Formulation','Extended Edge Formulation','Reduced Extended Edge Formulation'],[0.4687,0.0691,0.04609,0.0285,0.02366], marker='o',color='r')\n",
        "plt.ylabel('Time taken in seconds')\n",
        "plt.title('Execution Time For Different Approaches')\n",
        "plt.xticks(rotation=45)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 745
        },
        "id": "ArZMDXFQpS0_",
        "outputId": "a71bb75d-bdd3-49bc-fd30-a34a4baa4cb1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "([0, 1, 2, 3, 4],\n",
              " [Text(0, 0, 'Edge formulation'),\n",
              "  Text(1, 0, 'Cycle formulation'),\n",
              "  Text(2, 0, 'Reduced Edge Assignment Formulation'),\n",
              "  Text(3, 0, 'Extended Edge Formulation'),\n",
              "  Text(4, 0, 'Reduced Extended Edge Formulation')])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}