authors/nfbvs/newsvendor/newsvendor.ipynb

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      "cell_type": "markdown",
      "id": "d91fbd83",
      "metadata": {
        "id": "7RjB1ipC7KbS"
      },
      "source": [
        "# Solving simple stochastic optimization problems with AMPL\n",
        "[![newsvendor.ipynb](https://img.shields.io/badge/github-%23121011.svg?logo=github)](https://github.com/ampl/colab.ampl.com/blob/master/authors/nfbvs/newsvendor/newsvendor.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ampl/colab.ampl.com/blob/master/authors/nfbvs/newsvendor/newsvendor.ipynb) [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/ampl/colab.ampl.com/blob/master/authors/nfbvs/newsvendor/newsvendor.ipynb) [![Gradient](https://assets.paperspace.io/img/gradient-badge.svg)](https://console.paperspace.com/github/ampl/colab.ampl.com/blob/master/authors/nfbvs/newsvendor/newsvendor.ipynb) [![Open In SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/ampl/colab.ampl.com/blob/master/authors/nfbvs/newsvendor/newsvendor.ipynb) [![Hits](https://h.ampl.com/https://github.com/ampl/colab.ampl.com/blob/master/authors/nfbvs/newsvendor/newsvendor.ipynb)](https://colab.ampl.com)\n",
        "\n",
        "Description: Examples of the Sample Average Approximation method and risk measures in AMPL\n",
        "\n",
        "Tags: AMPL, amplpy, Stochastic Optimization, Sample Average Approximation, Risk measures\n",
        "\n",
        "Notebook author: Nicolau Santos <<nfbvs@ampl.com>>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "ed055cf3",
      "metadata": {
        "id": "rQ50dBwL7KbX"
      },
      "outputs": [],
      "source": [
        "# Install dependencies\n",
        "%pip install -q amplpy pandas matplotlib numpy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "fb8b0c0e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101,
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        },
        "id": "s7DEaRp87KbY",
        "outputId": "7ccc087a-1af8-445e-c56d-31f3371b0c38"
      },
      "outputs": [],
      "source": [
        "# Google Colab & Kaggle integration\n",
        "from amplpy import AMPL, ampl_notebook\n",
        "\n",
        "ampl = ampl_notebook(\n",
        "    modules=[\"highs\"],  # modules to install\n",
        "    license_uuid=\"default\",  # license to use\n",
        ")  # instantiate AMPL object and register magics"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "83def22c",
      "metadata": {},
      "source": [
        "This notebook is based on the Gurobi webinar and materials available at\n",
        "https://www.gurobi.com/events/solving-simple-stochastic-optimization-problems-with-gurobi/\n",
        "and has examples of the Sample Average Approximation method and risk measures in AMPL.\n",
        "The original version featured the Gurobi solver. By default, this notebook uses the Highs solver, so that all users are able to change and solve the models in the notebook. It's possible to choose a different solver directly changing the *solver* variable. Note that the MIP in the examples is hard to solve and open source solvers will strugle to find a good solutions for it.\n",
        "\n",
        "This notebook features the newsvendor problem.\n",
        "The newsvendor problem has the following characteristics:\n",
        " - You have to buy today a stock of products to sell tomorrow.\n",
        " - The demand is unknown.\n",
        " - It is possible to salvage the remaining stock (sell for scrap).\n",
        " \n",
        "The presented solutions are:\n",
        " - Manual solution\n",
        " - Maximize expected returns\n",
        " - Maximize worst case\n",
        " - Maximize worst alpha percentile\n",
        " - Minimize Conditionnal Value at Risk (CVaR)\n",
        " - Maximize mixture of CVaR and Expected value"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "1eb84d70",
      "metadata": {},
      "outputs": [],
      "source": [
        "import random\n",
        "\n",
        "from amplpy import AMPL\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import colors\n",
        "from matplotlib import gridspec\n",
        "from matplotlib.ticker import PercentFormatter\n",
        "import numpy\n",
        "import pandas as pd\n",
        "\n",
        "random.seed(a=100)\n",
        "n_bins = 150\n",
        "dpi = 120\n",
        "solver = \"highs\"\n",
        "\n",
        "\n",
        "# Conditional value at risk (for losses) is the expected value of the worst alpha% tail of the realizations\n",
        "# in the random variable (represented in this case as a list of values)\n",
        "# alpha: a value in ]0,1[\n",
        "# data: a list of values\n",
        "def CVaR(data, alpha):\n",
        "    data.sort()\n",
        "    n = len(data)\n",
        "    m = int(alpha * n)\n",
        "    return sum(data[m:-1]) / len(data[m:-1])\n",
        "\n",
        "\n",
        "# Prints the double histogram of a random variable (represented in this case as a list of values)\n",
        "# First the empirical density distribution, and then the empirical accumulated probability\n",
        "def doubleHist(data, xlabel=None):\n",
        "    # plt.figure(figsize=(14.5,6),dpi=100)\n",
        "    plt.figure(figsize=(10, 6), dpi=dpi)\n",
        "    gs = gridspec.GridSpec(2, 1, height_ratios=[5, 1], wspace=0.25)\n",
        "    ax = plt.subplot(gs[0])\n",
        "    ax3 = plt.subplot(gs[1])\n",
        "    ax.grid(True)\n",
        "    ax.set_title(xlabel)\n",
        "    ax.set_ylabel(\"Likelihood of occurrence\", color=\"blue\")\n",
        "    ax2 = ax.twinx()\n",
        "    ax2.set_ylabel(\"Cumulative probability\", color=\"red\")\n",
        "    # plot the density and cumulative histogram\n",
        "    n, bins, patches = ax.hist(\n",
        "        data,\n",
        "        n_bins,\n",
        "        density=True,\n",
        "        alpha=0.65,\n",
        "        cumulative=False,\n",
        "        label=\"Density\",\n",
        "        color=\"blue\",\n",
        "    )\n",
        "    n, bins, patches = ax2.hist(\n",
        "        data,\n",
        "        n_bins,\n",
        "        density=True,\n",
        "        histtype=\"step\",\n",
        "        cumulative=True,\n",
        "        label=\"CDF\",\n",
        "        color=\"red\",\n",
        "    )\n",
        "    ax3.boxplot(data, vert=False, whis=[5, 95])\n",
        "    # ax3.axis(\"off\")\n",
        "    ax3.tick_params(\n",
        "        left=False,\n",
        "        right=False,\n",
        "        labelleft=False,\n",
        "        labelbottom=False,\n",
        "        bottom=False,\n",
        "        top=True,\n",
        "    )\n",
        "    # tidy up the figure\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "# Print basic info on a given solution\n",
        "def solutionStats(model, losses):\n",
        "    global cost\n",
        "    losses.sort()\n",
        "    samples = len(losses)\n",
        "    order = model.var[\"order\"].value()\n",
        "    print(\n",
        "        \"Model Value\",\n",
        "        model.get_objective(\"obj\").value(),\n",
        "        \"order Qty\",\n",
        "        order,\n",
        "        \"investment\",\n",
        "        order * cost,\n",
        "        \"#samples\",\n",
        "        samples,\n",
        "    )\n",
        "    print(\"Losses stats:\")\n",
        "    print(\n",
        "        \"CVaR(95%,90%,80%,70%)\",\n",
        "        CVaR(losses, 0.95),\n",
        "        CVaR(losses, 0.90),\n",
        "        CVaR(losses, 0.80),\n",
        "        CVaR(losses, 0.70),\n",
        "    )\n",
        "    print(\n",
        "        \"min\",\n",
        "        min(losses),\n",
        "        \"max\",\n",
        "        max(losses),\n",
        "        \"average\",\n",
        "        sum(losses) / samples,\n",
        "        \"VaR_75%\",\n",
        "        losses[int(0.75 * samples)],\n",
        "    )\n",
        "\n",
        "\n",
        "# Build a dataframe with expected values and CVaR values specified in the cvars list\n",
        "def getTable(data, colnames, cvars):\n",
        "    info = []\n",
        "    temp = [\"Expected Value\"]\n",
        "    for sol in data:\n",
        "        temp.append(sum(sol) / len(sol))\n",
        "    info.append(temp)\n",
        "    for c in cvars:\n",
        "        temp = [\"CVaR_\" + str(c) + \"%\"]\n",
        "        for sol in data:\n",
        "            temp.append(CVaR(sol, c / 100))\n",
        "        info.append(temp)\n",
        "    return pd.DataFrame(info, columns=[\"Metric\"] + colnames)\n",
        "\n",
        "\n",
        "# Get the list of losses from the AMPL instance\n",
        "def getLosses(ampl):\n",
        "    return [-v[1] for v in ampl.var[\"profit\"].getValues()]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "ee3c49ff",
      "metadata": {},
      "outputs": [],
      "source": [
        "# Define constants\n",
        "# Basic economic parameters\n",
        "# cost: is the procurement cost of each unit\n",
        "# retail: selling price\n",
        "# recover: scrap price (if negative is a cost, MUST be smaller than cost, otherwise problem is unbounded!)\n",
        "cost = 2\n",
        "retail = 15\n",
        "recover = -3\n",
        "# By sampling, we approximate integrals....\n",
        "# The larger the sample, the more accurate.... but the harder the problem\n",
        "samples = 10000\n",
        "# we have a (positive truncated) normal distribution for demand (can't have a negative demand... unless short selling....)\n",
        "sigma = 100\n",
        "mu = 400\n",
        "demand = [max(random.normalvariate(mu, sigma), 0) for i in range(samples)]\n",
        "# Maximum and minimum revenue. Will be used to bound variables\n",
        "maxrev = max(demand) * (retail - cost)\n",
        "minrev = max(demand) * (recover - cost) + min(demand) * retail"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "63cf78c0",
      "metadata": {},
      "source": [
        "# Manual solution"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "4a448cbb",
      "metadata": {},
      "outputs": [],
      "source": [
        "order = 600\n",
        "losses = [\n",
        "    -retail * min(order, demand[i]) + cost * order - recover * max(0, order - demand[i])\n",
        "    for i in range(samples)\n",
        "]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ff0f0219",
      "metadata": {},
      "source": [
        "### Explore solution"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "54b15b23",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Solution Value 4218.884025261554 order Qty 600 investment 1200 #samples 10000\n",
            "Losses stats:\n",
            "CVaR(95%,90%,80%,70%) -548.3178927360779 -1098.2236361473692 -1725.803157600196 -2157.3548113755487\n",
            "min -7800 max 2301.589271028297 average -4218.884025261554 VaR_75% -3029.6614816227075\n"
          ]
        },
        {
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HkTIyMuTn56dhw4ZZyoKCgjR06FB99tlnOnz4cJltu3XrZgmWSFLbtm11++236+2337aUZWZm6sSJE0pJSbFqP2LECBUUFGjdunVO9+lI8+bNJUmnT5+2+/zcuXMqKSm5aj8AAABep18/qX1769flO16ufK1ZU9UzBgCgSnhtEGbnzp1q3bq1wsPDrcpjY2MlSbt27bLbzmQy6auvvrK7syQ2NlYHDhzQuXPnLGNIsqnbpUsX+fr6Wp4702epCxcu6Oeff9bhw4f17rvv6oUXXtC1116rllf+b5Ck+Ph4hYeHKyQkRP369dO+ffvsfmwAAABeiZuNAAAoF689jpSbm6vo6Gib8tKyo0eP2m138uRJFRcXX7VtmzZtlJubKz8/PzVq1MiqXkBAgBo0aGAZw5k+S61evVoPPPCA5f2uXbvqtdde0zXXXPqUh4SEaMiQIZYgzBdffKHZs2frlltu0Y4dOxQTE2P/k3NRXl6ejh8/blWWk5MjSSosLNTZs2fLbF9RBQUFVm8r6or4moWHpo1awl3rE3A31ia8lTNrM/j+++X7ww825b4HD8rUooUKPvvMthHf2OECvnbCW1XG2gw1mYwxqsHX0cLCwqqeQrXhtUGYoqIiBQYG2pQHBQVZnjtqJ6lcbYuKihQQEGC3n6CgIKt65e2zVHx8vDZs2KDTp0/r448/1pdffmnzD3TgwIEaOHCg5f177rlHvXv3Vo8ePTRjxgy98sordudWat68eZo6dardZ7t371ZISEiZ7V2VlZXlUvs+feyXZ2a61C0gyfX1CXgKaxPeqjxrM/7rrxWam6uCK/9jKipKBWFhyuKbODyEr53wVp5cm/EXf3/MrAZfW3fv3l3VU6g2vDYIExwcrOLiYpvy8+fPW547aiepXG2Dg4N14cIFu/2cP3/eql55+ywVFRWlqKgoSVJiYqJmzpypO+64Q/v27bNJzHu57t27Ky4uTh999JHDOqVSUlKUlJRkVbZx40aNHDlSHTt2VHx8/FX7qIiCggJlZWUpNjZWoaGhFe4nNdV++fPPV7hLwG3rE3A31ia8lTNrMzQ01DhmtG2b7TNJnvnJA7UZXzvhrSpjbZb266nf69yJnTDl57VBmOjoaB05csSmPDc3V5LUpEkTu+0iIiIUGBhoqVdW2+joaJWUlCgvL8/qSNKFCxd04sQJSz1n+nQkMTFREydO1HvvvadHH320zLoxMTHas2dPmXUkqVGjRjZHqfZfPI8dEhJik0/H3UJDQ10aw9GuOg9PG7WEq+sT8BTWJryV1dp0dL30wYNSy5asYVQ6vnbCW3l0bfoaKVyrw9r39CmMmsRrE/N27txZe/futclrsu3i/7x07tzZbjtfX1917NhR2dnZNs+2bdumFi1aKCwszKqPK+tmZ2fLZDJZnjvTpyOlx5XOnDlTZj1JOnjwoCIjI69aDwAAwCPsJdqVSLYLAICLvDYIk5iYqJKSEi1cuNBSVlxcrCVLliguLs6StDYnJ0fff/+9Tdvt27dbBU327NmjjRs3Wh3f6dWrlyIiIjR//nyr9vPnz1dISIgSEhKc7vPnn3+W2Wy2+XheffVVSdY3MV2ZVFeS1q9fry+++EJ33nmng88MAABAJeB6aQAA3M5rjyPFxcUpKSlJ48ePV15enlq2bKmlS5fq0KFDWrx4saXe4MGDtXnzZqvAR0pKihYtWqSEhASNGTNG/v7+mj17tqKiojR69GhLveDgYE2bNk0jRoxQUlKSevfurS1btmjFihWaMWOGIiIinO5zxYoVeuWVV3TPPfeoRYsWOnfunD744ANt2LBBd999t3r16mWpe8stt+jGG29U165dVbduXe3YsUOvvfaaYmJiNGHCBE99agEAACyC77/fSLgbGmrZ+q79+40gDAAAcCuvDcJI0rJlyzRp0iQtX75cp06dUqdOnbR27Vr16NGjzHZhYWHatGmTRo0apenTp8tkMqlnz56aM2eOzTGflJQU+fv7a9asWVqzZo1iYmI0Z84cPfHEExXqs3v37tq6daveeOMNHTt2TNdcc43atGmj2bNn6/HHH7fq87777tO6dev04YcfqrCwUNHR0UpOTtbkyZMtSX0BAAA8yfeHHxSam2sddOHYEQAAHuHVQZigoCClpaUpLS3NYZ1NmzbZLW/atKlWrVpVrnGSk5OVnJx81Xrl6bNr1656++23yzXu9OnTNX369HLVBQAAcImDZLu+Bw/qXHS0tG1btUj+CABAdea1OWEAAADgRg6S7ZpatFBB48ZVMCEAAGofr94JAwAAADcqTbZ7mYKzZ5WVman4KpoSAAC1CUEYAACAmsTBsSOS7QIAUPU4jgQAAFCTODh2RLJdAACqHjthAAAAaho7x44AAEDVIwgDAABQXdk7esSxIwAAvBbHkQAAAKore0ePOHYEAIDXYicMAABAdcbRIwAAqg12wgAAAAAAAFQCdsIAAAB4O66dBgCgRmAnDAAAgLfj2mkAAGoEdsIAAABUB+R+AQCg2mMnDAAAAAAAQCVgJwwAAIA3sZf/hdwvAADUCOyEAQAA8Cb28r+Q+wUAgBqBnTAAAADehvwvAADUSARhAAAAqgLXTgMAUOtwHAkAAKAqcO00AAC1DjthAAAAqgrHjgAAqFUIwgAAAHgSx44AAMBFHEcCAADwJI4dAQCAi9gJAwAA4GkcOwIAAGInDAAAAAAAQKVgJwwAAIC72Mv/Qu4XAABwETthAAAA3MVe/hdyvwAAgIvYCQMAAOBO5H8BAAAOsBMGAAAAAACgErATBgAAwFn2cr9I5H8BAABlYicMAACAs+zlfpHI/wIAAMrEThgAAICKIPcLAABwEkEYAAAARzh2BAAA3IjjSAAAAI5w7AgAALgRQRgAAICylB47uvK1Zk1VzwwAgGqvuLhY48aNU5MmTRQcHKy4uDht2LChXG0/+ugjxcfHq2HDhqpXr55iY2O1fPlyD8/YNQRhAAAAAABAlRgyZIhmz56thx56SC+++KL8/PzUt29fffrpp2W2W7Nmjf7whz/owoULmjJlimbMmKHg4GANHjxYc+bMqaTZO4+cMAAAAAAAoNJlZWXpzTffVFpamsaMGSNJGjx4sDp06KDU1FRt3brVYduXX35Z0dHR2rhxowIDAyVJjz76qNq2bav09HSNGjWqUj4GZ7ETBgAAQDKS8LZvb/2ylw8GAAC4RUZGhvz8/DRs2DBLWVBQkIYOHarPPvtMhw8fdtj27Nmzql+/viUAI0nXXHONGjZsqODgYI/O2xUEYQAAACT7SXhJwAsAgMfs3LlTrVu3Vnh4uFV5bGysJGnXrl0O2/bs2VPffPONJk2apP379+vAgQOaNm2asrOzlZqa6slpu4TjSAAAAKVKk/ACAACn5eTk6Jsrvo9GRkaqUaNGduvn5uYqOjrapry07OjRow7HmjRpkn744QfNmDFD06dPlySFhITonXfeUf/+/Sv6IXicV++EcSVL8pEjRzRw4EDVq1dP4eHh6t+/vw4ePGi37uLFi9WuXTsFBQWpVatWeumllyrcZ1FRkYYOHaoOHTqobt26qlOnjn7729/qxRdf1C+//GLT5+nTpzVs2DBFRkYqNDRU8fHx2rFjR7k+RgAAUAH2jh1x9AgAAJeNHDlSHTp0sHrNmzfPYf2ioiKr40SlgoKCLM8dCQwMVOvWrZWYmKg33nhDK1asUNeuXTVo0CB9/vnnrn8wHuLVO2GGDBmijIwMPfnkk2rVqpXS09PVt29fZWZmqnv37g7b5efnKz4+XmfOnNGECRPk7++vOXPm6LbbbtOuXbvUoEEDS90FCxZo+PDhGjBggJ566ilt2bJFI0eOVGFhocaNG+d0n0VFRfrmm2/Ut29fNW/eXL6+vtq6datGjRqlbdu2aeXKlZY+TSaTEhIS9OWXX2rs2LFq2LCh5s2bp549e+qLL75Qq1atPPBZBQCglis9dtSypXU5R48AAHDJ3Llz1atXL6uyyMhIh/WDg4NVXFxsU37+/HnLc0cee+wxff7559qxY4d8fY39JQMHDlT79u31xBNPaNu2bRX5EDzOa4MwrmRJnjdvnvbt26esrCx169ZNktSnTx916NBBs2bN0syZMyUZAZOJEycqISFBGRkZkqTk5GSZTCZNmzZNw4YNU/369Z3qMyIiwibqNnz4cNWtW1cvv/yyZs+ercaNG0sykhBt3bpVq1atUmJioiRj0bRu3VqTJ0+2CtgAAAA34tgRAABu16xZM7Vv377c9aOjo3XkyBGb8tzcXElSkyZN7La7cOGCFi9erNTUVEsARpL8/f3Vp08fvfzyy7pw4YICAgKc/Ag8z2uPI7mSJTkjI0PdunWzBEskqW3btrr99tv19ttvW8oyMzN14sQJpaSkWLUfMWKECgoKtG7dOqf7dKR58+aSjONHl/cZFRWle++911IWGRmpgQMH6r333rMbEQQAAAAAoCbo3Lmz9u7dq7Nnz1qVl+5i6dy5s912J06c0K+//qqSkhKbZ7/88otMJpPdZ97Aa4MwFc2SbDKZ9NVXX6lr1642z2JjY3XgwAGdO3fOMoYkm7pdunSRr6+v5bkzfZa6cOGCfv75Zx0+fFjvvvuuXnjhBV177bVqednW5507d+qmm26yityV9llYWKi9e/fa/RgBAAAAAKjuEhMTVVJSooULF1rKiouLtWTJEsXFxSkmJkaSkfD3+++/t9Rp1KiR6tWrp3fffVcXLlywlOfn5+v9999X27Ztvfaaaq89jlTRLMknT55UcXHxVdu2adNGubm58vPzs8nUHBAQoAYNGljGcKbPUqtXr9YDDzxgeb9r16567bXXdM01lz7lubm56tGjR5l9duzY0e7HKUl5eXk6fvy4VVlOTo4kqbCw0Caa6C4FBQVWbyvqiviahYemjVrCXesTcDfWZuULvv9++f7wg02578GDMrVooQK+4UhibcK7sT7hrSpjbYaaTMYY1eD7VWFhYYXaxcXFKSkpSePHj1deXp5atmyppUuX6tChQ1q8eLGl3uDBg7V582aZzWZJkp+fn8aMGaO//e1v+t3vfqfBgwerpKREixcv1o8//qgVK1a45ePyBK8NwlQ0S3JpeXnaFhUVOTwjFhQUZFWvvH2Wio+P14YNG3T69Gl9/PHH+vLLL23+gbqSCVoy8tRMnTrV7rPdu3crJCSkzPauysrKcql9nz72yzMzXeoWkOT6+gQ8hbVZeeK//lqhubkquPI/UaKiVBAWpiy+4VhhbcKbsT7hrTy5NuMv/v6YWQ2+X+3evbvCbZctW6ZJkyZp+fLlOnXqlDp16qS1a9fa3bBwuYkTJ+q6667Tiy++qKlTp6q4uFidOnVSRkaGBgwYUOH5eJrXBmEqmiW5tLw8bYODg622Ll1Z9/J65e2zVFRUlKKioiQZW6xmzpypO+64Q/v27bMk5nUlE7QkpaSkKCkpyaps48aNGjlypDp27Kj4+Pgy21dUQUGBsrKyFBsbq9DQ0Ar3k5pqv/z55yvcJeC29Qm4G2uz8oWGhhoJeO3cjhAqyTPfJasf1ia8GesT3qoy1mZpv576vc6dKroTRjI2IaSlpSktLc1hnU2bNtktf/DBB/Xggw9WeOyq4LVBmIpmSY6IiFBgYKClXllto6OjVVJSory8PKsjSRcuXNCJEycs9Zzp05HExERNnDhR7733nh599FHL+K702ahRI5ujVPv375ckhYSE2OTTcbfQ0FCXxnC0q87D00Yt4er6BDyFtVmJLuZc4/NdPqxNeDPWJ7yVR9dmNfo+5ulTGDWJ1ybmrWiWZF9fX3Xs2FHZ2dk2z7Zt26YWLVooLCzMqo8r62ZnZ8tkMlmeO9OnI6VHi86cOWP1Me7YsUOmi2f9Lu8zJCRErVu3LrNPAABwUb9+Uvv21q+L/zEBAADgLdwShDlyRHrjDenFF6UffzTKSkqkkyeNtxVR0SzJpW23b99uFTTZs2ePNm7caHV8p1evXoqIiND8+fOt2s+fP18hISFKSEhwus+ff/7Zkizocq+++qok65uYEhMTdezYMa1evdqq/apVq3T33XfbzRcDAADsOHDANujSsqV0/fVVMx8AAAA7XDqOZDZLo0dLL78s/fqr5OMjdewoNW0q5edLzZtLzz4rPfmk831XNEuyZORKWbRokRISEjRmzBj5+/tr9uzZioqK0ujRoy31goODNW3aNI0YMUJJSUnq3bu3tmzZohUrVmjGjBmKiIhwus8VK1bolVde0T333KMWLVro3Llz+uCDD7Rhwwbdfffd6tWrl6VuYmKifve73+nPf/6zvv32WzVs2FDz5s1TSUmJw4S7AADAgZYtpW++qepZAAAAOORSECYtzdj9Mm6cdPvt0h13XHpWt650773SO+9ULAgjVTxLclhYmDZt2qRRo0Zp+vTpMplM6tmzp+bMmaPIyEiruikpKfL399esWbO0Zs0axcTEaM6cOXriiScq1Gf37t21detWvfHGGzp27JiuueYatWnTRrNnz9bjjz9u1aefn5/Wr1+vsWPHau7cuSoqKlK3bt2Unp5udd01AAAAAACo/lwKwixaJA0eLM2cKZ04Yfu8UyfpP/+peP+uZElu2rSpVq1aVa5xkpOTlZycfNV65emza9euevvtt8s1riTVr19fr776quW4EgAAKEO/fsbRoyvt32/shAEAAPBiLuWEOXxYuuUWx89DQx3fgAMAAOA0e7lfJPK/AACAasGlnTCNGhmBGEe++EJq1syVEQAAAK5A7hcAAFBNubQT5t57pVdekQ4evFTm42O8/fBDKT1duuziIAAAAAAAgFrLpSDM1KlSdLTUubORG8bHR3ruOal7d6lPHyMnzIQJbpopAAAAAACAu7VrZyS7/d//PD6US0GYunWlzz+XUlOlI0ekoCBp82bp9Glp8mRpyxYpJMRNMwUAALVLv35S+/bWL3v5YAAAAFwRE2MEMa6/XurRQ3r1VenMGY8M5VIQRpKCg6W//U3atUsqKJCKiqSvv5aeecZ4BgAAUCH2kvCSgBcAALjbhx9KP/4opaUZQY1hw6TGjaXEROm996RffnHbUC4l5v31V6mwUAoPt//87FljJ8w1Lo0CAABqLZLwAgCAyhAVJY0aZbz27JFWrJDeeEN6912pXj3pvvukQYPKviK6HFzaCTNyZNnj//730ujRrowAAAAAAABQidq0kaZNkz791NgNc+qUcSvRrbdKrVpJ//qXZDJVqGuXgjD//a8xH0cSE6X1610ZAQAAAAAAoJIUFBi7YO68U2rWzNgJc9dd0ttvG39u08bYkfLXv1aoe5cOCh09Kv3mN46fN2liJOwFAABwqF8/I//LlfbvN44jAQAAeFJJifTBB0bwZc0aI+9Kly7SrFnSAw9IDRteqtuvn3EN9L/+JS1Y4PRQLgVhGjQwjko58t13jvPFAAAASLqUgPfKgAtJeAEAQGVo3Fg6edLYZfL449Lgwca11Y506iSdO1ehoVwKwtx5pxH4eegh6cYbrZ/t2CEtXCglJbkyAgAAqBVIwAsAAKpKQoL08MNSr16Sj8/V699/v/GqAJeCMNOmGXlhYmONHTnt2xvlX38tvf++1KiRUQcAAAAAAMArPfKIsfPFUQDm55+lb7+VevRweSiXEvM2aSJlZ0sPPih9/LE0fbrx2rjR2B2zfbvUtKnLcwQAAAAAAPCM+HhpwwbHzz/+2KjjBi7thJGk6Ghp6VLJbJaOHzfKIiPLt4MHAADUIiTgBQAA3shsLvt5cbHk5+eWoVwOwpTy8TGOHwEAANhFAl4AAOAtcnKkQ4cuvf/999Inn9jWO33aSIZ77bVuGdblIMypU9Ibb0gHDxp/vjKA5OMjLV7s6igAAKBGIAEvAADwBkuWSFOnGkELHx9pxgzjdSWz2dgFU4HrqO1xKQjzwQdSYqJUUGBcRV2/vm0djiUBAAAAAACvMnCg1KGDEWQZOFAaOVK69VbrOj4+Umio1LmzFBXllmFdCsKMHm1cp716tdSxo1vmAwAAAAAA4Fnt2hkvydgV06OHdN11Hh/WpSDM/v1SWhoBGAAAcAV7SXhJwAsAALzRn/5UaUO5FIRp1Uo6d85dUwEAADWGvSS8JOAFAADe4JFHjKNGCxca+V4eeeTqbdyU8NalIMz06dKIEdKDD0rNm7s8FwAAUJOQhBcAAHijjRslX1/JZDKCMBs3Xj2hrZsS3roUhPn4Yyky0jhGdccdUkyM7dXZPj7Siy+6MgoAAAAAAICbXH41tb33PcilIMzLL1/689q19usQhAEAoAazl/tFIv8LAACAHb6uNDaZrv4qKXHXVAEAgNcpzf1yJfK/AAAA2HBpJwwAAAC5XwAAQLXi6+t8jhcfH+nXX10e2i1BmM8/lzIzpbw8KSXFuDWpsFD6/nupdWupTh13jAIAAAAAAOCiZ55xW6JdZ7kUhLlwQbr/fum99ySz2fgY7r7bCML4+kp/+IM0apQ0caK7pgsAAAAAAOCCKVOqbGiXcsJMmmQk5J0/X9qzxwjElAoKkpKSjAANAAAAAABAbefSTpg33pD++ldp2DDpxAnb5+3aSatWuTICAADwCtyCBAAAaoply4y3Dz9sHOkpff9qBg92eWiXgjB5eVLHjo6f+/kZuWEAAEA1V3oL0pUBF25BAgAA1c2QIUbw5f77pYAA4/2r8fGp+iBMTIyRfNeR//s//nMMAIAag1uQAABATfDDD8bbgADr9yuBS0GYBx+UZs+WBgwwbkGSLiUYXrRIevtt6R//cHWKAAAAAAAAbnLttWW/70EuBWEmTjSup+7Rw8j/4uNj3IZ08qT0449S377G+wAAAAAAAF6tpET64gvp0CHj/ebNpS5djFwrbuJSECYgQPrvf6XXX5cyMoz5FhdLnTpJ06dfynEDAAAAAADgtdLTpfHjjeS3pVc/+/hIkZHSzJnSI4+4ZZgKB2GKioydMPHx0qBBxgsAANQA9m5C4hYkAABQUy1YYFz93LmzNGXKpXwre/YYz5KTpQsXpOHDXR6qwkGY4GBjLjfc4PIcAADwiAcftC1bubLy51Ht2LsJiVuQAABATfXcc9Ktt0offST5+18qj4+Xhg6VevWSnn++aoMwknE06uuvXZ4DAADwNtyEBAAAaouffpJGj7YOwJTy9zeusk5NdctQvq40/uc/pTfflF59Vfr1V7fMBwAAAAAAoPLceKO0d6/j53v3GkeV3MClIMyQIZKvr/Too1J4uNSqlZGU9/LXb39b8f6Li4s1btw4NWnSRMHBwYqLi9OGDRvK1fbIkSMaOHCg6tWrp/DwcPXv318HDx60W3fx4sVq166dgoKC1KpVK7300ksV7vPw4cOaOnWqYmNjVb9+fTVs2FA9e/bURx99ZNNfenq6fHx87L5++umncn2cAAAAAADABS+9JL39tvTii0YC3FJFRdKcOcazl192y1AuHUeKiJAaNJDatHHLXGwMGTJEGRkZevLJJ9WqVSulp6erb9++yszMVPfu3R22y8/PV3x8vM6cOaMJEybI399fc+bM0W233aZdu3apQYMGlroLFizQ8OHDNWDAAD311FPasmWLRo4cqcLCQo0bN87pPt977z0999xzuueee/SnP/1Jv/76q5YtW6Y77rhDr732mv785z/bzPfZZ5/VddddZ1VWr149Fz97AABchb0EvBJJeAEAQM3WqZNtmZ+f9NRTxrGjJk2MsqNHjWM/0dHGLpQvv3R5aJeCMJs2uTy+Q1lZWXrzzTeVlpamMWPGSJIGDx6sDh06KDU1VVu3bnXYdt68edq3b5+ysrLUrVs3SVKfPn3UoUMHzZo1SzNnzpQkFRUVaeLEiUpISFBGRoYkKTk5WSaTSdOmTdOwYcNUv359p/qMj49XTk6OGjZsaJnP8OHD1blzZz3zzDN2gzB9+vRR165dXf2UAQDgHHsJeCWS8AIAgJotIsK4fvpyDRoYx3su17y524eucBCmsFCKiTGu0b4YI3GrjIwM+fn5adiwYZayoKAgDR06VBMmTNDhw4cVExPjsG23bt0swRJJatu2rW6//Xa9/fbbloBJZmamTpw4oZSUFKv2I0aM0Ouvv65169Zp0MW7t8vbZ/v27W3mExgYqL59+2r27Nk6d+6cwsLCbOqcO3dOISEh8vPzK++nCAAA15GAFwAA1Dae3FFyFRXOCRMSIl1zjfHWE3bu3KnWrVsrPDzcqjw2NlaStGvXLrvtTCaTvvrqK7s7S2JjY3XgwAGdO3fOMoYkm7pdunSRr6+v5bkzfTry008/KSQkRCF2PmHx8fEKDw9XSEiI+vXrp3379pXZFwAAAAAAqH5cOo40YICUkSH99a+2O3lclZubq+joaJvy0rKjR4/abXfy5EkVFxdftW2bNm2Um5srPz8/NWrUyKpeQECAGjRoYBnDmT7t2b9/v1avXq2kpCSrnS4hISEaMmSIJQjzxRdfaPbs2brlllu0Y8cOhzt9SuXl5en48eNWZTk5OZKkwsJCnT17tsz2FVVQUGD1tqKuiK9ZeGjaqCXctT5RM9j7OlNVX2O8cW2GmkySpAK+8NZq3rg2gVKsT3irylib1en7dGFhYVVPwT1++UX6/nvpzBnp4uffSo8eLg/hUhDm/vullBQpPl5KTjaOSwUH29a76Sbn+y4qKlJgYKBNeVBQkOW5o3aSytW2qKhIAQEBdvsJCgqyqlfePq9UWFiopKQkBQcH6x//+IfVs4EDB2rgwIGW9++55x717t1bPXr00IwZM/TKK6/Y7bPUvHnzNHXqVLvPdu/ebXfXjTtlZWW51L5PH/vlmZkudQtIcn19omaw93Wmqr/GeNPajL/4g2NmVX9S4BW8aW0CV2J9wlt5cm1Wp+/Tu3fvruopuMZkMnKtzJtn5F5xpKTE5aFcCsL07Hnpz1u22D43m40dMhWZZ3BwsIqLi23Kz58/b3nuqJ2kcrUNDg7WhQsX7PZz/vx5q3rl7fNyJSUluv/++/Xtt9/qP//5j5qUZlguQ/fu3RUXF2f3SusrpaSkKCkpyaps48aNGjlypDp27Kj4+Pir9lERBQUFysrKUmxsrEJDQyvcT2qq/fLnn69wl4Db1idqBntfZ6rqa4w3rs3SeXjq+wWqB29cm0Ap1ie8VWWszer0fbra74SZOVNKS5MefVTq3l16+GHpueekevWMwIyPj9t+iHQpCLNkiVvmYFd0dLSOHDliU56bmytJDgMaERERCgwMtNQrq210dLRKSkqUl5dndSTpwoULOnHihKWeM31eLjk5WWvXrtXrr7+uXr16lfnxXi4mJkZ79uy5ar1GjRrZHKXav3+/JOOo05X5dNwtNDTUpTEc7arz8LRRS7i6PlEz2Ps6U9XLokrWpqOrqA8elFq25N8KJPF1E96N9Qlv5dG16WukcK0Oa9/TpzA8Lj1dGjhQmj9fOnHCKOvSRerVS/rTn6Sbb5Y2bpT+3/9zeSiXgjB/+pPL4zvUuXNnZWZm6uzZs1aLbtu2bZbn9vj6+qpjx47Kzs62ebZt2za1aNHCcjtRaR/Z2dnq27evpV52drZMJpPluTN9lho7dqyWLFmif/7zn3rggQfK/XFL0sGDBxUZGelUGwAAHOIqagAAAMd+/PHSFurSNCQXT70oIEAaNEiaPdvYMeOiCt+O5GmJiYkqKSnRwoULLWXFxcVasmSJ4uLiLElrc3Jy9P3339u03b59u1XQZM+ePdq4caPV8Z1evXopIiJC8+fPt2o/f/58hYSEKCEhwek+JSktLU0vvPCCJkyYoCeeeMLhx3hlUl1JWr9+vb744gvdeeedDtsBAOC00quor3ytWVPVMwMAAKhaDRpI+fnGn+vUMbZOHzxoXefUKbcM5dJOmEceuXodHx9p8WLn+46Li1NSUpLGjx+vvLw8tWzZUkuXLtWhQ4e0+LIOBw8erM2bN8tsNlvKUlJStGjRIiUkJGjMmDHy9/fX7NmzFRUVpdGjR1vqBQcHa9q0aRoxYoSSkpLUu3dvbdmyRStWrNCMGTMUERHhdJ/vvvuuUlNT1apVK7Vr104rVqyw+rjuuOMORUVFSZJuueUW3Xjjjeratavq1q2rHTt26LXXXlNMTIwmTJjg/CcNAAAAAAA458Ybpe3bL70fHy/9859GuckkzZ0r/fa3bhnKpSDMxo22V1OXlEi5ucbbyEjJlRxFy5Yt06RJk7R8+XKdOnVKnTp10tq1a9XjKtdChYWFadOmTRo1apSmT58uk8mknj17as6cOTbHfFJSUuTv769Zs2ZpzZo1iomJ0Zw5c2x2sJS3zy+//FKStG/fPj388MM2c8vMzLQEYe677z6tW7dOH374oQoLCxUdHa3k5GRNnjzZUgcAAAAAAHjQsGFGXpjiYuM40owZxnXUPXoYNw7Vry+98YZbhnIpCHPokP3yX36RFiwwAkcbNlS8/6CgIKWlpSktLc1hnU2bNtktb9q0qVatWlWucZKTk5WcnHzVeuXpc8qUKZoyZUq5xp0+fbqmT59erroAAJSLvSS89vLBAAAAwNCvn/EqdcMNxs9TmzZJfn7SLbdIl52UcYVLQRhH/P2lxx6Tvv3WeLtunSdGAQAANuwl4SUBLwAAgHPq1pX693d7tx4JwpT67W+l5cs9OQIAoLZ58EHbspUrK38eXq00CS8AAADKb+1aaf36S8d+mjeX+vaV7rrLbUN4NAizYYNU3a8LBwAAAAAANdjp09If/yh98olx/Cg62ij/6CMj18qtt0r//rdUr57LQ7kUhHn2Wfvlp08bc9+xQ3r6aVdGAAAAAAAA8KAnnpC2bJGee076618v3TBUUCDNmyeNH2/UWbrU5aFcCsI4yj9bv75x9PyVV6Ry5LsFAAAAAACoGv/+t5SSIo0ZY10eGiqNHSvl5EjLlrllKJeCMCaTW+YAAACcZe8WJImbkAAAAJzl7y+1aeP4edu2Rh038HVLLwAAoHKV3oJ0JW5CAgAAcM6AAdKqVVJJie2zX3+V3n5bSkpyy1AuBWE2bJAmTHD8fOJEaeNGV0YAAAAOld6CdOVrzZqqnhkAAEC5FBcXa9y4cWrSpImCg4MVFxenDRs2lLv9W2+9pZtvvlmhoaGqV6+ebrnlFm28WiBixw7r16BB0qlT0i23SIsXS5s3G69XXzXKzpyRHnrIxY/U4NJxpOnTpWbNHD8/csSo06uXK6MAAABPsXflt8S13wAAoHIMGTJEGRkZevLJJ9WqVSulp6erb9++yszMVPfu3ctsO2XKFD377LNKTEzUkCFD9Msvv+jrr7/WkSNHyh60a1fJx8e6zGw23m7ffulZaZkk3Xab/Z0yTnIpCLN7d9k7crp1M67ZBgBUH/xSDgAAgMqQlZWlN998U2lpaRpzMSnu4MGD1aFDB6Wmpmrr1q0O237++ed69tlnNWvWLI0aNcq5gZcscWXaLnEpCFNcLF24UPbzwkJXRgAAAAAAADVRRkaG/Pz8NGzYMEtZUFCQhg4dqgkTJujw4cOKiYmx2/af//ynGjdurCeeeEJms1kFBQWqU6dO+Qb+05/cMf0KcSknTIcO0rvv2n9mNkurV0s33ODKCAAAQP36Se3bW7/sJeUFAACoRnbu3KnWrVsrPDzcqjw2NlaStGvXLodtP/74Y3Xr1k1z585VZGSkwsLCFB0drZdfftm1SeXnS999Z7zy813ryw6XgjCPPy793/8ZR5J27zaSBv/6q/TVV0bZZ58ZdQAAgAvs3YTELUgAAMDL5OTk6JtvvrF65eXlOayfm5ur6Ohom/LSsqNHj9ptd+rUKf3888/6v//7P02aNElPP/203nrrLXXu3FmPP/64FixY4Pzkt2+X4uOl+vWNHScdOhh/7tVLys52vj8HXDqONGiQ8XPhtGnGrhffiyEdk8nIY/O3v1XpLh8AAGqO0puQAAAAvNTIkSNtyiZPnqwpU6bYrV9UVKTAwECb8qCgIMtze/Iv7lA5ceKE3nzzTd13332SpMTERHXs2FHTp0/Xo48+Wv6Jb9sm9ewpBQRIf/mL1K6dUf7dd9Ibb0g9ekibNkkXd+i4wqUgjCRNnmwEY959Vzp40Ci7/nrpnnv4DzoAAAAAAGqLuXPnqtcV1yNHRkY6rB8cHKzi4mKb8vPnz1ueO2onSf7+/kpMTLSU+/r66r777tPkyZOVk5OjZmVd53y5iROl3/xG+vRTqXFj62dTpki//71Rx4mrsx1xOQgjGcGWi4mMAQCAC+zdTvX8j1LTppU/lytxcxYAAChLs2bN1L59+3LXj46OtnuddG5uriSpSZMmdttFREQoKChI9erVk5+fn9WzRo0aSTKOLJU7CLNtm/TMM7YBGEmKipKGDTOOALmBS0GYHTukzz+XUlLsP583T7rlFqlzZ1dGAQCglujXT89vPmBTHJW/X1LLyp8PAACAB3Xu3FmZmZk6e/asVXLebdu2WZ7b4+vrq86dO2v79u26cOGCAgICLM9K88iUtQPHTodGgltHSkou5V9xkUu9TJwoffSR4+cbNxp5YQAAqA0efNDxKzXVqJOa6nhHiQ4cuBhwsXasDkl4AQBAzZOYmKiSkhItXLjQUlZcXKwlS5YoLi7Ocj11Tk6Ovv/+e6u29913n0pKSrR06VJL2fnz5/X666/rhhtucLiLxq5bbpH+9S/pf/+zfZaTY+ww+f3vnfvgHHBpJ8wXX0jjxzt+fuut0t//7soIAADULsfqtFRqgm0CXntHfjgeBAAAqrO4uDglJSVp/PjxysvLU8uWLbV06VIdOnRIixcvttQbPHiwNm/eLLPZbCl79NFH9eqrr2rEiBHau3evmjVrpuXLl+t///uf3n//fecmMnOmEcBo21b64x+l1q2N8j17pPfek665xm3BDZeCMOfOGXNxxNdXOnPGlREAAAAAAEBNtWzZMk2aNEnLly/XqVOn1KlTJ61du1Y9evQos11wcLA2btyo1NRUvfbaayooKFDnzp21bt069e7d27lJ3HijlJVlHPdZs0YqLDTKQ0KkO++Upk+Xbrihgh+hNZeCMK1aSR9+KD3+uP3n//2v1KKFKyMAAAAAAICaKigoSGlpaUpLS3NYZ9OmTXbLGzVqpPT0dNcmUFwsffCB1Ly5ce2zySQdP248i4x0Wy6YUi71NnSotG6d9NRT0unTl8pPn5ZGjTKCMEOHujZBAAAAAAAAjwgIkJKSpK1bjfd9fY0bkaKi3B6AkVzcCTNypLRrl/TPf0pz50qleW+OHjWCRw8/bARjAADAJaM/6Se1t70FSfv3S0HcggQAAFBpfHyMYz4//1wpw7kUhPHxkZYskQYPlt55Rzp40Cjv318aMEDq2dMNMwQAVDs1KWGsvY/F1Y8j6twB/ZK737j16HJBLXUsjFuQAAAAKtWECcYRn6QkqU0bjw7lUhCmVHy88QIAAOXj6BYkAAAAVLLPP5caNJA6dDB2kzRvLgUHW9fx8ZFefNHlodwShCkokDZvvnSldvPmUo8eUmioO3oHAAAAAADwkJdfvvTnjz+2X8dbgjAvvST97W9Sfr502ZXdCguTZsyQHnvM1REAACibo+NPAAAAwFWZTJU2lEupfpctk554wtixs3KlkaR31y7pjTekjh2NZ8uXu2eiAAAAAAAA1ZlLO2FmzzaOHX38seTnd6m8UycpMVG6/XZp1izjliQAAGqj0Z/0U9S5A/L1M6nO+gJ1yw9VZP5B26S8V8FuHwAAAA/7+mtp/Xrp0CHj/ebNpT59jF0mbuJSEGbPHumFF6wDMKX8/IzEwmPGuDICAADVW9S5A4rK36/jdVtYyo7V4RYkAAAAr1FcLD36qHGUx2yWfC8eGjKZpPHjpYcekl59VQoIcHkol4IwdeteChDZc+iQFB7uyggAAFR/x+q01PSBn6lPn0z95z/xOnuWb44AAABeY9w4I99KSor0+OPS9dcbiXj375fmzpXmz5ciIqR//tPloVwKwiQkGIl5u3SR7r/f+tlbbxkJhh96yJURAAA1naNjNitXVu48AAAAUEutWGHkUbn8liRJatNG+te/pLNnjTpuCMK4lJj3H/+QWrQwAi2/+Y1xnXbPnsafH3zQePaPf7g8RwAAAAAAAM/45Rfpd79z/PyWW6Rff3XLUC4FYSIjpR07jAS9HTtKx44Zr44dpTlzpC++kBo2dMs8AQAAAAAA3K93b+mDDxw//+9/pT/8wS1DuXQcSZKCgoyrqJ94wh3TAQCgarh8+1C/fnp+8wGb4qj8/U7fhOQJ3K4EAADgwLRp0sCB0r33SiNGSC0v/uy2b59xHOl//zNyrpw8ad0uIsLpoVwOwgAAAEkHDtgNuHATEgAAgJdr1854u3u39N571s/MZuPtDTfYtispcXoogjAAALjJsTotlZrwjd1n4TpbybMBAABAuTzzjHEbUiUgCAMAAFzCDVcAAKBamzKl0oZyKTGvpxUXF2vcuHFq0qSJgoODFRcXpw0bNpSr7ZEjRzRw4EDVq1dP4eHh6t+/vw4ePGi37uLFi9WuXTsFBQWpVatWeumllyrc5+HDhzV16lTFxsaqfv36atiwoXr27KmPPvrIbp+nT5/WsGHDFBkZqdDQUMXHx2vHjh3l+hgBAAAAAED14dROmDVrpK5dpSZNPDUda0OGDFFGRoaefPJJtWrVSunp6erbt68yMzPVvXt3h+3y8/MVHx+vM2fOaMKECfL399ecOXN02223adeuXWrQoIGl7oIFCzR8+HANGDBATz31lLZs2aKRI0eqsLBQ48aNc7rP9957T88995zuuece/elPf9Kvv/6qZcuW6Y477tBrr72mP//5z5Y+TSaTEhIS9OWXX2rs2LFq2LCh5s2bp549e+qLL75Qq1atPPBZBVCTsSPh6khQCwAAgKriVBDmj3+Uli+/9ANsixbSP/8p9evn/ollZWXpzTffVFpamsaMGSNJGjx4sDp06KDU1FRt3brVYdt58+Zp3759ysrKUrdu3SRJffr0UYcOHTRr1izNnDlTklRUVKSJEycqISFBGRkZkqTk5GSZTCZNmzZNw4YNU/369Z3qMz4+Xjk5OWp42d3cw4cPV+fOnfXMM89YBWEyMjK0detWrVq1SomJiZKkgQMHqnXr1po8ebJW8lsTAAAAAAA1hlPHkcLCpNOnL71/6JCUn+/eCZXKyMiQn5+fhg0bZikLCgrS0KFD9dlnn+nw4cNltu3WrZslWCJJbdu21e233663337bUpaZmakTJ04oJSXFqv2IESNUUFCgdevWOd1n+/btrQIwkhQYGKi+ffvqxx9/1Llz56z6jIqK0r333mspi4yM1MCBA/Xee++puLi4zM8RAKDyjf6kn9S+ve1r//6qnhoAAAC8nFM7YWJjpRkzpGPHpLp1jbL166WffnLcxsdHGjXK+Ynt3LlTrVu3Vnh4+BVziJUk7dq1SzExMTbtTCaTvvrqKz3yyCN25h+rDz/8UOfOnVNYWJh27twpSeratatVvS5dusjX11c7d+7UoEGDnOrTkZ9++kkhISEKCQmx+hhvuukm+fpax8JiY2O1cOFC7d27Vx07dnTYJwBUtdp4tCfq3AHp+H6ppfVV1GrZUsfOcBU1AAAAHHMqCDNvnjR4sDRtmvG+j4+RZ6CsUzMVDcLk5uYqOjrapry07OjRo3bbnTx5UsXFxVdt26ZNG+Xm5srPz0+NGjWyqhcQEKAGDRpYxnCmT3v279+v1atXKykpSX5+flYfY48ePcrss6wgTF5eno4fP25VlpOTI0kqLCzU2bOeuQ61oKDA6m1FXRFfs/DQtFFLuGt9elpqqm3Z88+73q87/l0504ejuo4404ejOTs7prv5+plU0qKFCj77zObZolTHV1HXqVNg9daT3PF3NXy4633w9bx6qC5fN1E7sT7hrSpjbYaaTMYY1eAbamFhYVVPodpwKgjTsqW0dat0/ryUlyc1b27khOnf3/0TKyoqUmBgoE15UFCQ5bmjdpLK1baoqEgBAQF2+wkKCrKqV94+r1RYWKikpCQFBwfrH//4h81cK9JnqXnz5mnq1Kl2n+3evdtq140nZGVludS+Tx/75ZmZLnULSHJ9fXqavfXvjrXvjn9XzvThqK4jzvThaM7OjuluddYXqKDAONJ6pfLM7dZbPb823fF35al5wHt5+9dN1G6sT3grT67N+IsBHns/c3ib3bt3V/UUXHf2rLHzJDPTCHgsWGAcBzp5UkpPN5LhXrkTugKcCsKUCgqSmjWTJk+WevWSrr3W5XnYCA4OtpsT5fz585bnjtpJKlfb4OBgXbhwwW4/58+ft6pX3j4vV1JSovvvv1/ffvut/vOf/6jJFddKVfRjLJWSkqKkpCSrso0bN2rkyJHq2LGj4uPjy2xfUQUFBcrKylJsbKxCQ0Mr3I+9nQCSe3YDoPZy1/r0NE/thHHHvytn+nBU1xFn+nA0Z2fHdLdu+aGKipLdr7Flza1OnQLdemuWtmyJVX6+Z9emO/6uPDUPeJ/q8nUTtRPrE96qMtZmab+e+r3Onar9Tpgff5Ruu006fFhq1Ur6/vtLCXAjIoyAzP/+J734ostDVSgIU2ry5Et/zs835itJMTFSnTqu9GwcyTly5IhNeW5uriTZBDRKRUREKDAw0FKvrLbR0dEqKSlRXl6e1ZGkCxcu6MSJE5Z6zvR5ueTkZK1du1avv/66evXqZfdjdLbPyzVq1MjmKNX+i4khQ0JCbPLpuFtoaKhLY3jrUQPUDK6uT09zx3GR8vbrbN/O9OHs7lhn+vCGI4ujP+ln5IC5TGT+QflFt7S7vsozt/z8UJ0969m16Y6/K0/Ng2vUvZe3f91E7cb6hLfy6Nq8mDu0Oqx9T5/C8LixY6Vz56Rdu6RGjYzX5e65R1q71i1DOXU7kj3bt0vx8VL9+lKHDsarfn1jh0x2dsX77dy5s/bu3WuT12Tbtm2W5/b4+vqqY8eOyrYz+LZt29SiRQtLAt3SPq6sm52dLZPJZHnuTJ+lxo4dqyVLlmjOnDl64IEHHH6MO3bskOniWb/L+wwJCVHr1q3ttgMAVI6ocwcUlW9969GxOi2l60nACwAAUGN8+KE0cqR0ww1GYtsrtWhxadeJi1zaCbNtm9SzpxQQIP3lL1K7dkb5d99Jb7wh9eghbdpkHKNyVmJiol544QUtXLhQY8aMkWQcB1qyZIni4uIsNyPl5OSosLBQbdu2tWr79NNPKzs723Lz0Z49e7Rx40ZLX5LUq1cvRUREaP78+erbt6+lfP78+QoJCVFCQoLTfUpSWlqaXnjhBU2YMEFPPPFEmR9jRkaGVq9ercTEREnSzz//rFWrVunuu++2my8GACrC3u4Ddh6Uz7E6LZWa8I3tg1p4MxQAAECNVFQkRUY6fn7unNuGcikIM3Gi9JvfSJ9+KjVubP1syhTp97836mzY4HzfcXFxSkpK0vjx45WXl6eWLVtq6dKlOnTokBYvXmypN3jwYG3evFlms9lSlpKSokWLFikhIUFjxoyRv7+/Zs+eraioKI0ePdpSLzg4WNOmTdOIESOUlJSk3r17a8uWLVqxYoVmzJihiIgIp/t89913lZqaqlatWqldu3ZasWKF1cd1xx13KCoqSpIRhPnd736nP//5z/r222/VsGFDzZs3TyUlJQ4T7gIAUBNxTAkAAFSZG26QPvlEevRR+8///W/pxhvdMpTLO2GeecY2ACNJUVHSsGGXrrOuiGXLlmnSpElavny5Tp06pU6dOmnt2rV2r3W+XFhYmDZt2qRRo0Zp+vTpMplM6tmzp+bMmaPIK6JbKSkp8vf316xZs7RmzRrFxMRozpw5NjtYytvnl19+KUnat2+fHn74YZu5ZWZmWoIwfn5+Wr9+vcaOHau5c+eqqKhI3bp1U3p6usPrrgEAAAAAgBs9+aT0pz9JnTpJpZffmEzS/v3S1KnSZ59J77zjlqFcCsL4+kq//ur4eUmJJZdQhQQFBSktLU1paWkO62zatMluedOmTbVq1apyjZOcnKzk5OSr1itPn1OmTNGUKVPKNa4k1a9fX6+++qpeffXVcrcBAKAsjnaVAAAAwI5Bg4zbj/72N+M4jyTdeadkNhtBjZkzjeS8buBSEOaWW6R//cv4Ye/Ka6pzcowrtn//e1dGAADA8+zdgiRJUfn7jUS8AAAAqNkmTpQeftjY8bJ/v7ET5vrrpXvvNRLzuolLQZiZM43ku23bSn/8o1R6mc+ePdJ770nXXCP9/e/umCYAoCbw1h0apbcgXRlwOVanpY6FcRMSAABArdCsmTRqlEeHcCkIc+ONRl6YiROlNWukwkKjPCTE2LkzfbqR3wYAAGdVdsDG4S1IAAAAqNliY6UHHjDywTRt6tGhXArCSEaQ5d13jZ06x48bZZGRruWCAQAAAAAAqBR+ftLo0dLYsdLvfifdf7+UmGj/FiIXuS1U4utr3IgUFUUABgAAAAAAVBOffSYdOmTkUykulkaOlGJipF69pIULpZ9/dttQLu+EAQCguiABLwAAAOxq1szYCTN2rHTwoPTWW9Lbb0vDh0uPPWYEZP77X5eHYc8KAKDWKE3AeyUS8AIAAMCiRQtp/Hhpxw5pwQIpOFjasMEtXbMTBgBQq5CAFwAAAGX6/HNjF8yqVdLRo1KdOm67NYIgDAAAAAAAqN2++OLSEaTDh43dL3fdJd13n9S3rxQY6JZhCMIAAAAAAIDa6/rrjcS8AQFSnz7Sc89Jd98thYS4fSingjCffFKxQXr0qFg7AID3cNMOTAAAAMC73HCDNHWq1L+/FBbm0aGcCsL07Cn5+Fx632y2ft+RkhInZwUAgIvs3YTELUgAAACw8f77lTaUU0GYzEzr94uLpdRUqbBQGjZMatPGKP/+e2nRIik0VHr+eXdNFQCA8iu9CenyoAu3IAEAAEA5OcbbZs2s37+a0voucCoIc9tt1u8/9ZRxZOrzz6WgoEvld98tjRhh1P/vf6U77nB5ngDg1Rwd1Vm5snLnAWvchAQAAAAbzZsbx3qKioygRun7V+OGYz4uJeZ9/XXpb3+zDsCUCgmRHn5YmjFDmjXLlVEAAAAAAADc5LXXjKCLv7/1+5XApSBMQYGUm+v4eW6ucVQJAOB+JMpFbWBvnbPDDAAAuGTIkLLf9yCXgjD/7/9JL74ode0q3Xuv9bN33jGe9e7tyggAAADuxxFCAABg8cgj0qOPSnFx9p9nZUmvvGLsmHGRryuN//UvqWlTKSnJeNuzp/GKiZEGDjTKXnrJ5TkCAAAAAAB4Rnq6dOCA4+c//CAtXeqWoVzaCfOb30hffiktWCD95z/S//5nlLdvL40dKyUnS8HB7pgmAAD22buKWuI6agAAALjJ0aNuC264FISRjKS8TzxhvAAA7kful7LZu4pa4jpqAAAAlOG994xXqYULpY8+sq13+rRR3q2bW4Z1OQhT6ttvL+2EufZa6YYb3NUzAABl4ypqAAAAOOXbb6VVq4w/+/hI27ZJX3xhXcfHRwoNlXr0kGbPdsuwLgdh3ntPeuop6dAh432z2ZjnddcZc+zXz9URAAAAAAAA3Gj8eOMlSb6+0uLFlbIF3aUgzPr10oABxs6XmTOldu2M8u++M3by3HuvtHatdOed7pgqAFQ/3nyUyJvnBgAAAFQak6nShnIpCDNtmtSpk7Rli7FDp1S/ftJjj0ndu0tTpxKEAQAAAAAAcCkI89VXxg6YywMwpUJDpSFDpAkTXBkBAIBL7N2ExC1IAAAAcNl//mPkVNmxQzpzxsi1cqWSEpeH8XWlcVCQdPKk4+cnTxp1AABwh9KbkC7HLUgAAABwyTvvSHfdJR07Jt1/v3E86YEHjD8HBxtHgJ55xi1DubQTplcv6cUXjeNGN99s/WzbNmnuXOkPf3BlBAAArHETEgAAANzq73+XYmOlTz+VTp2S5s+XHnnECHocOiT97nfG7UNu4FIQ5vnnjeBL9+7GfNu0Mcr37JGysqRGjaTnnnPHNAEAAKqGoyTWK1dW7jwAAICHfPutEYjx85OuuRgm+eUX423z5lJKihHcGDzY5aFcOo503XVGXpiRI41g0VtvGa9Tp6QnnpC+/NKYLwAAAAAAgFcKCZECAow/16snBQZKubmXnkdFST/84JahXNoJIxm7XebMMV4AgIrjymgAAACgCrRpY+yGKdW5s7R8uTRokPTrr8b212bN3DKUy0GYUvn50uHDxp9jYqQ6ddzVMwBUDY4gVB17tyBJ3IQEAAAAD/jjH42kti+8YOyCmThR6t/f2BXj4yMVFEivveaWoVw6jiRJ27dL8fFS/fpShw7Gq359I39NdrY7pggAqG3s3YIkcRMSAAAAPGDMGCknxwjASMZNSZs2ScnJ0qOPSh9/LA0Z4pahXNoJs22b1LOncXTqL3+R2rUzyr/7TnrjDalHD2PesbGuTxQAULtwCxIAAACqzK23Gi83cykIM3Gi9JvfGLc4NW5s/WzKFOn3vzfqbNjgyigAAABXZ+8IIccHAQCAN3F5J8wzz9gGYCQjefCwYdK0aa6MAAAAAAAA4EbXXWfkenGGj490wDZnobNcCsL4+hqJgh0pKTHqAABgDwl4URHcJAYAQM1RXFysZ555RsuXL9epU6fUqVMnTZ8+XXfccYdT/dxxxx366KOPNGLECL388stlV77tNueDMG7iUhDmllukf/3L+GHo2mutn+XkSPPmGUeSAACwpzQB75UBFxLwwl0I2AAA4N2GDBmijIwMPfnkk2rVqpXS09PVt29fZWZmqnv37uXqY/Xq1frss8/KP2h6esUm6wYuBWFmzjSS77Zta9zo1Lq1Ub5nj/Tee9I110h//7s7pgkAqKlIwAsAAFA7ZWVl6c0331RaWprGjBkjSRo8eLA6dOig1NRUbd269ap9nD9/XqNHj9a4ceP0zDPPeHrKLnPpsNCNNxp5Ye68U1qzRnr2WeP1/vtG2eefS7/9bcX7Ly4u1rhx49SkSRMFBwcrLi5OG8qZ5ffIkSMaOHCg6tWrp/DwcPXv318HDx60W3fx4sVq166dgoKC1KpVK7300ksu9Tl//nwlJSWpWbNm8vHx0RAHV1mlp6fLx8fH7uunn34q18cJAIC3evBB2xcAAECpjIwM+fn5adiwYZayoKAgDR06VJ999pkOHz581T6ef/55mUwmSxCnQj75pHwvN3BpJ4wk3XCD9O67kskkHT9ulEVGuicXTEW3JeXn5ys+Pl5nzpzRhAkT5O/vrzlz5ui2227Trl271KBBA0vdBQsWaPjw4RowYICeeuopbdmyRSNHjlRhYaHGjRtXoT6fe+45nTt3TrGxscrNzb3qx/nss8/quuuusyqrV6+eE58pAOXF7SkAAACAd9i5c6dat26t8PBwq/LY2FhJ0q5duxQTE+OwfU5Ojv7xj3/otddeU3BwcMUn0rNn+XLElJRUfIyLXA7ClPL1NW5EchdXtiXNmzdP+/btU1ZWlrp16yZJ6tOnjzp06KBZs2Zp5syZkqSioiJNnDhRCQkJysjIkCQlJyfLZDJp2rRpGjZsmOrXr+9Un5K0efNmyy6YOnXqXPVj7dOnj7p27VqBzxIAAAAAAN4hJydH33xjfcw8MjJSjRo1sls/NzdX0dHRNuWlZUePHi1zvNGjR+vGG2/U/fffX8EZX5SZaVtWUiIdOiQtXGjsOvnHP1wb4yKXgzCnTklvvCEdPGj82Wy2fu7jIy1e7Hy/ZW1LmjBhgg4fPuwwIpaRkaFu3bpZgiWS1LZtW91+++16++23LQGTzMxMnThxQikpKVbtR4wYoddff13r1q3ToEGDnOpTkq69MktxOZw7d04hISHy8/Nzui1Q212+uyU8XOrTp+rmAsfs3YTELUgAAAA1x8iRI23KJk+erClTptitX1RUpMDAQJvyoKAgy3NHMjMz9c4772jbtm0Vm+zlbrvN8bMhQ6Rbb5U2bZJ69XJ5KJcODX3wgdSsmfTYY9Krr0obNxoBpCtfFVGebUn2mEwmffXVV3Z3lsTGxurAgQM6d+6cZQxJNnW7dOkiX19fy3Nn+qyI+Ph4hYeHKyQkRP369dO+ffsq3BcAeKvSm5Auxy1IAAAANcfcuXP19ddfW72u3PRwueDgYBUXF9uUnz9/3vLcnl9//VUjR47Uww8/bLVRwiN8faX77zeCHm7g0k6Y0aOlxo2l1auljh3dMh+Lim5LOnnypIqLi6/atk2bNsrNzZWfn5/N1qiAgAA1aNDAMoYzfTojJCREQ4YMsQRhvvjiC82ePVu33HKLduzYUebZN0nKy8vT8dJEPBfl5ORIkgoLC3X27Fmn5lNeBQUFVm8r6or4moWHpo0a7PK1VKdO2evT3rpztOacWaOO6uISXz+TjtdtoekDba8PDFfN/4dfujZL38I7OfPvu6Z8v3LX93XAE1if8FaVsTZDTSZjjGrwDaewsFCS1KxZM7Vv377c7aKjo3XkyBGb8tLcqk2aNLHbbtmyZdqzZ48WLFigQ4cOWT07d+6cDh06pEaNGikkJKTccynTyZPS6dNu6cqlIMz+/VJamvsDMFLFtyWVlpenbVFRkQICAuz2ExQUZFWvvH06Y+DAgRo4cKDl/XvuuUe9e/dWjx49NGPGDL3yyitltp83b56mTp1q99nu3bvdt+AcyMrKcqm9oyMjFd09hdrL3lpytD7t1XW05pxZoxyBuro6640fUvr0qd3/yG+91bWvnfAsZ/5917TvV65+Xwc8ifUJb+XJtRl/McCTWQ2+4ezevbtC7Tp37qzMzEydPXvW6hRM6RGjzp07222Xk5OjX375Rb///e9tni1btkzLli3Tu+++q3vuuad8E7m4mcHG6dPGrUhpacaRJDdwKQjTqpXkwimcMlV0W1JpeXnaBgcH68KFC3b7OX/+vFW98vbpqu7duysuLk4fffTRVeumpKQoKSnJqmzjxo0aOXKkOnbsqPj4eLfM6UoFBQXKyspSbGysQkNDK9xPaqr98uefr3CXqKUuX0t16hTo1luztGVLrPLzy7c+Ha05Z9aoo7q4pNvFv4///MczX5u8XUXWJiqfM/++a8r3K3d9Xwc8gfUJb1UZa7O0X0/9XudOpTthnJWYmKgXXnhBCxcutFzIU1xcrCVLliguLs5yOiQnJ0eFhYVq27atJOn++++3G6D54x//qL59+yo5OVlxcXHln0jz5o5vRzKbpd/9TlqwwJkPzSGXgjDTp0sjRhhJMZs3d8t8LCq6LSkiIkKBgYF2r4a+sm10dLRKSkqUl5dndSTpwoULOnHihKWeM326Q0xMjPbs2XPVeo0aNbI5SrV/v5FvISQkxCafjruFhoa6NIazR0BQe9i7RlpyfJW0vbWUnx+qs2fLt5icPWrgzJGm2sheAl5Jisw/qGN1Wpb776WmcmZtovK548hideXq93XAk1if8FYeXZu+RgrX6rD2K3oKIy4uTklJSRo/frzy8vLUsmVLLV26VIcOHdLiy274GTx4sDZv3izzxZuA2rZtawnIXOm6664r/w6YUq+9ZhuE8fGR6teXrr9euuEG5/org1NBGDuJjhUZKbVrJ91xhxQTI115uY+Pj/Tii85PrKLbknx9fdWxY0dlZ2fbPNu2bZtatGihsLAwqz6ys7PVt29fS73s7GyZTCbLc2f6dIeDBw8qMjLSbf0BcC9HQSIYShPwXnnrEUl4AQAAcKVly5Zp0qRJWr58uU6dOqVOnTpp7dq16tGjR+VNYsiQShvKqSDMyy87frZ2rf3yigZhKrotqbTt008/rezsbMuNRnv27NHGjRstfUlSr169FBERofnz51sFYebPn6+QkBAlJCQ43aczjh8/bhNsWb9+vb744gu7V3sBQHVxrE5LpSZ8U9XTAAAAgJcLCgpSWlqa0tLSHNbZtGlTufoq3SnjzZwKwlxMzlwpKrotSTJypSxatEgJCQkaM2aM/P39NXv2bEVFRWn06NGWesHBwZo2bZpGjBihpKQk9e7dW1u2bNGKFSs0Y8YMRUREON2nJL3//vv68ssvJUm//PKLvvrqK02fPl2S1K9fP3Xq1EmSdMstt+jGG29U165dVbduXe3YsUOvvfaaYmJiNGHCBPd/UgEAAAAAgK1PPzWOJR08KJ06ZeSCuZyPj3Tx93xXuJQTxtMqui0pLCxMmzZt0qhRozR9+nSZTCb17NlTc+bMsdl5kpKSIn9/f82aNUtr1qxRTEyM5syZoyeeeKLCfb7zzjtaunSp5f2dO3dq586dkqSmTZtagjD33Xef1q1bpw8//FCFhYWKjo5WcnKyJk+erKioqAp/3gAAAAAAQDnNni2NHSsFBUlt2kiXbchwN68OwriyLalp06ZatWpVucZJTk5WcnLyVeuVt8/09HSlp6dftd706dMtO2QAAAAAAEAVSEuTfv976f33pbp1PTqUU0EYX1/jVVgoBQQYf3Z0i1MpHx/p119dmSIAwBs5ugXJXlJeoDaxl7zb0e1urvbrrr4BAKjVCgulhx7yeABGcjII88wzRlDlmmus3wcA1D7cggQAAIAaIT5e2r27UoZyKggzZUrZ7wMAahduQQIAAEC199JL0h/+IL3wgvTII7U3JwwAAEBNwFEiAAC8WEyM9Oij0pgx0rhxRoJePz/rOj4+0pkzLg/lVBBm2bKKDTJ4cMXaAQAAAAAAeNQzz0gzZki/+Y3UtatHc8M4FYQZMsT5AXx8CMIAAAAAAAAv9corUkKC9O9/GzcQeZBTQZgffvDUNAAA3szeTUjcgoTaztERIwAAUM1cuGAEYTwcgJGcDMJce62npgEA8Gb2bkLiFiQAAADUCHfdJW3ZYuSF8TC3JOYtLpZ27JDy8qTf/15q2NAdvQIAvAk3IQHeyd6OHBL+AgDghMmTpfvuk1JSpKFDpWbNbBPzSm65NcnlIMzcucZV1aVJgjdskHr1kn7+WWrbVnr+eeOGJwDwVhwpAAAAAGqxNm2Mt7t2SQsWOK5XUuLyUC4FYZYskZ58Urr/fuNK7cuDLQ0bGsGYN98kCAMA1YW93C8S+V8AAABQgz3zjHGrUCVwKQgza5bUv7+x5fXECdvnXboYO2UAANWDvdwvEvlfAAAAUINNmVJpQ7kUhNm/Xxo50vHziAj7wRkAgPci9wsAAADgGS4FYerVM3K/OPLtt1Ljxq6MAAAAAAAA4EHPPnv1Oj4+0qRJLg/lUhCmb19p4UIjgfCVvvlGWrSIfDAAAAAAAMCLlXUcycdHMpvdFoTxdaXx9OlGcuAOHaS//c2Y09Kl0qBBUteuUqNGRn4bAAAAAAAAr2Qy2b5+/VU6cEAaNcoIcOTluWUol4IwTZpIX3wh3Xmn9NZbRnBo+XLp/felBx6QPv/cuCUJAAAAAACg2vD1la67TnrhBalVK+nxx93SrUvHkQoLjd0ur75qvI4fNwJGkZHGfCXp4EGpRQt3TBVAbffgg1U9g5qDq6gB7+Do69rKlZU7DwAAUIYePaRx49zSlUs7Ye66SyouvvR+ZKQUFXUpAPPNN9Ktt7oyAgDAE0qvor4SV1EDAAAAV8jOvhTocJFLO2G+/lq65x5pzRrJ39/6WXa2cUwpOtqVEQAAnsJV1AAAAICkZcvsl58+LX3yibR6tfSXv7hlKJeCMB9+KN1+u5SYKL3zjnTNxd4++cTYJdOmjfTBB+6YJgBvZG8bPVvoAQAAAFQrQ4Y4ftawofT00267dcilIEznztJ//yvdcYeRiPett6T//EdKSpK6dZPWrpXCwtwyTwBABdnL/0LuF8A+ck8BAFAL/fCDbZmPj1S/vtuDGi4FYSQj2LJunXH0KD5e2rZN6tXL2K0TFOSOKQIAXFGa/+XyoAu5XwAAAICLrr220oZyOQgjSb//vXEtdUKC1L+/9Prrl44mAQCqHvlfAAAAgMucPy89+aTUvn3Z10/PnSt9953x9spkuBXgVKgkLMzYkePIhQvS+vVSRMSlMh8f6cyZik4PAAAAAADAzRYulNLTpW+/LbteQoKUmip16iT99a8uD+tUEGbAgLKDMAAAAAAAAF7v7beNIEeLFmXXu/56I/HtG29UfhAmPd3l8QAAAAAAAKrW7t3SQw+Vr+4ttxg5WNyAzC0AUEPYuwVJ4iYkAAAAwMaFC1JAQPnqBgRIxcVuGdapIMyyZcbbhx82jiWVvn81gwc7Oy0ANY2ja19XrqzcedRk9m5BkrgJCQAAALDRpIn09dflq/v110Z9N3AqCDNkiBF8uf9+IxA0ZMjV2/j4EIQBgMrCLUgAJALfAABc1f/7f8bOkvHjpUaNHNfLyzPqJSW5ZVingjA//GC8Ld2xU/o+AAAAAABAtTFunLRihdSrl7R4sRQXZ1tn2zbpL38xrrMeO9YtwzoVhLn22rLfv1JBAddTAwAAOMvRThYAAOAmLVoYNyQ98ICReLdFC6ljRyksTDp3zjiCdOCAFBIivfmmcUuSG3g0Me8//yk984xUUuLJUQCg9rGXhJcEvAAAAIATEhKkr76SnntOWrtW+ve/Lz1r0kRKTpZSU69+jbUTuB0JAKohe0l4ScALAAAAOKl5c2n+fON17px09qwUHm7siPEAgjAAUE2RhBcAAABwo7AwjwVfSvl6tHcAAAAAAABIIggDAAAAAABQKZw+jrRjR/nrHj3qbO8Aqjtu9HAvewl4JZLwAqgYe1+jw8OlPn0qfy4AANRGTu+E6dpV6tatfK/5812bXHFxscaNG6cmTZooODhYcXFx2rBhQ7naHjlyRAMHDlS9evUUHh6u/v376+DBg3brLl68WO3atVNQUJBatWqll156yaU+58+fr6SkJDVr1kw+Pj4aMmSIw3mePn1aw4YNU2RkpEJDQxUfH68dzkS6ANRopQl4r0QSXgAAAKD6cXonzJIlnpiGfUOGDFFGRoaefPJJtWrVSunp6erbt68yMzPVvXt3h+3y8/MVHx+vM2fOaMKECfL399ecOXN02223adeuXWrQoIGl7oIFCzR8+HANGDBATz31lLZs2aKRI0eqsLBQ48aNq1Cfzz33nM6dO6fY2Fjl5uY6nKfJZFJCQoK+/PJLjR07Vg0bNtS8efPUs2dPffHFF2rVqpWLn0HAMXv/G7pyZeXPA1dHAl4AAACgZnA6CPOnP3liGraysrL05ptvKi0tTWPGjJEkDR48WB06dFBqaqq2bt3qsO28efO0b98+ZWVlqVu3bpKkPn36qEOHDpo1a5ZmzpwpSSoqKtLEiROVkJCgjIwMSVJycrJMJpOmTZumYcOGqX79+k71KUmbN2+27IKpU6eOw3lmZGRo69atWrVqlRITEyVJAwcOVOvWrTV58mSt5Ddi1AIcXwKA6sXR121+bAEA4Oq8NjFvRkaG/Pz8NGzYMEtZUFCQhg4dqs8++0yHDx8us223bt0swRJJatu2rW6//Xa9/fbblrLMzEydOHFCKSkpVu1HjBihgoICrVu3zuk+Jenaa6+Vj49PuT7GqKgo3XvvvZayyMhIDRw4UO+9956Ki4uv2gcAAAAAAKgenN4JU1l27typ1q1bKzw83Ko8NjZWkrRr1y7FxMTYtDOZTPrqq6/0yCOP2DyLjY3Vhx9+qHPnziksLEw7d+6UJHXt2tWqXpcuXeTr66udO3dq0KBBTvXp7Md40003ydfXOhYWGxurhQsXau/everYsaNTfQKewG4VzyMBLwAAAFDzeW0QJjc3V9HR0TblpWVHHVy9dPLkSRUXF1+1bZs2bZSbmys/Pz81atTIql5AQIAaNGhgGcOZPp2Rm5urHj16lNlnWUGYvLw8HT9+3KosJydHklRYWKizZ886NZ/yKigosHpbUVfE1yw8NG1cwd7n39Hn3tHflTeqU6fA6m11EV24T5H5B3W8bgur8uN1W+hE+LUKD+cfRnVXXdcmar7SNVne7+t8/0ZlctfPnYC7VcbaDDWZjDGqwRfYwsLCqp5CteG1QZiioiIFBgbalAcFBVmeO2onqVxti4qKFBAQYLefoKAgq3rl7dMZFf0YS82bN09Tp061+2z37t0KCQlxek7OyMrKcqm9o+swMzNd6hblZO/z7+hzXx2vLr31VtfWZ2Wrs75ARXWitP2lf9h93kf8w6gpqtvaRO1R3u/rfP9GVXD1507AUzy5NuMvBngyq8EX2N27d1f1FKoNrw3CBAcH282Jcv78ectzR+0klattcHCwLly4YLef8+fPW9Urb5/OqOjHWColJUVJSUlWZRs3btTIkSPVsWNHxcfHOz2n8igoKFBWVpZiY2MVGhpa4X5SU+2XP/98hbuEE+x9/h197h39XXmjOnUKdOutWdqyJVb5+RVfn5Wt28W5/uc/nvl3i6pXXdcmar7StVne7+vOfE/gezpc5a6fOwF3q4y1Wdqvp36vcyd2wpSf1wZhoqOjdeTIEZvy0iufmzRpYrddRESEAgMD7V4NfWXb6OholZSUKC8vz+pI0oULF3TixAlLPWf6dEZ0dLRLfTZq1MjmKNX+/fslSSEhITb5dNwtNDTUpTFqwtGX6sze578mbTHPzw/V2bPeuZjs5X+JzD+oY3Vaeu2c4T7evDZRu5X3+7oz3xP4ng53cfXnTsBTPLo2L+YOrQ5r39OnMGoSr70dqXPnztq7d69NXpNt27ZZntvj6+urjh07Kjs72+bZtm3b1KJFC0sC3dI+rqybnZ0tk8lkee5Mn87o3LmzduzYIdPFs36X9xkSEqLWrVs73ScA7xd17oCi8vdblR2r01LHwq6vohkBAAAAqAxeG4RJTExUSUmJFi5caCkrLi7WkiVLFBcXZ7kZKScnR99//71N2+3bt1sFTfbs2aONGzdaHd/p1auXIiIiNH/+fKv28+fPV0hIiBISEpzu09mP8dixY1q9erWl7Oeff9aqVat09913280XA6BmOFanpVITvrF6zeqxpqqnBaAWS001bsMrfQEAAPfz2uNIcXFxSkpK0vjx45WXl6eWLVtq6dKlOnTokBYvXmypN3jwYG3evFlms9lSlpKSokWLFikhIUFjxoyRv7+/Zs+eraioKI0ePdpSLzg4WNOmTdOIESOUlJSk3r17a8uWLVqxYoVmzJihiIgIp/uUpPfff19ffvmlJOmXX37RV199penTp0uS+vXrp06dOkkygjC/+93v9Oc//1nffvutGjZsqHnz5qmkpMRhwl0AAAAAAFA9eW0QRpKWLVumSZMmafny5Tp16pQ6deqktWvX2r3W+XJhYWHatGmTRo0apenTp8tkMqlnz56aM2eOIiMjreqmpKTI399fs2bN0po1axQTE6M5c+boiSeeqHCf77zzjpYuXWp5f+fOndq5c6ckqWnTppYgjJ+fn9avX6+xY8dq7ty5KioqUrdu3ZSenu70ddcAAAAAAMC7eXUQJigoSGlpaUpLS3NYZ9OmTXbLmzZtqlWrVpVrnOTkZCUnJ1+1Xnn7TE9PV3p6ernGrl+/vl599VW9+uqr5aoPoPqwl4BXkqLy9+tYnZZVMCMAAAAAVclrc8IAQHVnLwGvRBJeAAAAoLby6p0wAFDdlSbgBQAAAACCMIAXc3Q7xcqVlTsPAAAAAIDrOI4EAAAAAABQCdgJAwAuIgEvAAAAgPIgCAMALipNwHtlwIUEvACqM0dHYgEAQMURhAEANyABLwAAAICrIScMAAAAAABAJWAnDFCD2Ns6zk1KAAAAAOAdCMIAgBPsJeElAS8AAACA8iAIA1QyR4kO2bFSPdhLwksCXgBwDt8LAQC1FUEYAHASSXgBAAAA9yguLtYzzzyj5cuX69SpU+rUqZOmT5+uO+64o8x2q1ev1ltvvaXt27frp59+UkxMjO666y5NmjRJ9erVq5zJVwCJeQEAAAAAQJUYMmSIZs+erYceekgvvvii/Pz81LdvX3366adlths2bJi+++47DRo0SHPnztWdd96pl19+WTfffLOKiooqafbOYycMANhhL/eLRP4XAAAAwF2ysrL05ptvKi0tTWPGjJEkDR48WB06dFBqaqq2bt3qsG1GRoZ69uxpVdalSxf96U9/0uuvv66//OUvnpx6hbETBgDsKM39ciXyvwAAAADukZGRIT8/Pw0bNsxSFhQUpKFDh+qzzz7T4cOHHba9MgAjSX/84x8lSd99953b5+ou7IQBAAfI/QIAAAB4zs6dO9W6dWuFh4dblcfGxkqSdu3apZiYmHL399NPP0mSGjZs6L5JuhlBGKCGc3QDBQAA3sbe9yxuTAKA6iMnJ0fffGP9n5iRkZFq1KiR3fq5ubmKjo62KS8tO3r0qFPjP/fcc/Lz81NiYqJT7SoTQRgAtRq5XwAAAAD3GDlypE3Z5MmTNWXKFLv1i4qKFBgYaFMeFBRkeV5eK1eu1OLFi5WamqpWrVqVu11lIwgDoFYrzf1yZcCF3C8AAACAc+bOnatevXpZlUVGRjqsHxwcrOLiYpvy8+fPW56Xx5YtWzR06FD17t1bM2bMcGLGlY8gDIBaj9wvAAAAgOuaNWum9u3bl7t+dHS0jhw5YlOem5srSWrSpMlV+/jyyy/Vr18/dejQQRkZGbrmGu8Oc3A7EgAAAAAAqHSdO3fW3r17dfbsWavybdu2WZ6X5cCBA7rzzjvVqFEjrV+/XnXq1PHUVN3Gu0NEAOBG9vK/kPsFAAAAqBqJiYl64YUXtHDhQo0ZM0aSVFxcrCVLliguLs5yM1JOTo4KCwvVtm1bS9uffvpJf/jDH+Tr66sPPvigzGNP3oQgDOAhnryVyFN91/SblOzlfyH3CwAAAFA14uLilJSUpPHjxysvL08tW7bU0qVLdejQIS1evNhSb/Dgwdq8ebPMZrOl7M4779TBgweVmpqqTz/9VJ9++qnlWVRUlO64445K/VjKiyAMgFqF/C8AAACA91i2bJkmTZqk5cuX69SpU+rUqZPWrl2rHj16lNnuyy+/lCQ9//zzNs9uu+02gjAAAAAAAACXCwoKUlpamtLS0hzW2bRpk03Z5btiqhOCMICXqOlHgSpT7IwZ6rb/aZlKrHOPk/8FAAAAQFXidiQANU7oTz8p8sxBm3LyvwAAAACoSuyEAVAjHa/bQql3kvsFAAAAgPcgCAMAAACPqOybAleu9Nx4AAC4A0EYANXa6E/6KercAcv7vn4mhZ7LVX4YuV8AAAAAeBdywgCo1qLOHVBU/n6rsoLoaB0Pv66KZgQAAAAA9rETBkC1d6xOS6UmGPlfwsPPqk+fTP3nP/HS2SqeGAAAAABchp0wAAAAAAAAlYCdMACqhStzv5SKyt+vY3XI/wIANZUnk/sCAFDZ2AkDoFqwl/tFMo4iHQu7vgpmBAAAAADOYScMgGrj8twvAAAAAFDdsBMGAAAAAACgErATBoBXIfcLAAAAgJrKq3fCFBcXa9y4cWrSpImCg4MVFxenDRs2lKvtkSNHNHDgQNWrV0/h4eHq37+/Dh48aLfu4sWL1a5dOwUFBalVq1Z66aWXKqXPKVOmyMfHx+YVFBRUro8Rle/BB+2/4D7kfgEAeCN+BgAAuINX74QZMmSIMjIy9OSTT6pVq1ZKT09X3759lZmZqe7duztsl5+fr/j4eJ05c0YTJkyQv7+/5syZo9tuu027du1SgwYNLHUXLFig4cOHa8CAAXrqqae0ZcsWjRw5UoWFhRo3bpxH+yw1f/581alTx/K+n5+fq586oFoj9wsAAACAmshrgzBZWVl68803lZaWpjFjxkiSBg8erA4dOig1NVVbt2512HbevHnat2+fsrKy1K1bN0lSnz591KFDB82aNUszZ86UJBUVFWnixIlKSEhQRkaGJCk5OVkmk0nTpk3TsGHDVL9+fY/1WSoxMVENGzZ016cOAAAAAAB4Ia89jpSRkSE/Pz8NGzbMUhYUFKShQ4fqs88+0+HDh8ts261bN0uwRJLatm2r22+/XW+//balLDMzUydOnFBKSopV+xEjRqigoEDr1q3zaJ+lzGazzp49K7PZXNanBKhxRn/ST8+va2/1sncUCQAAAABqAq8NwuzcuVOtW7dWeHi4VXlsbKwkadeuXXbbmUwmffXVV+ratavNs9jYWB04cEDnzp2zjCHJpm6XLl3k6+tree6JPi/XokUL1a1bV2FhYRo0aJCOHTtm92MDahp7+V/I/QIAcDfyuQAAvIXXHkfKzc1VdHS0TXlp2dGjR+22O3nypIqLi6/atk2bNsrNzZWfn58aNWpkVS8gIEANGjSwjOGJPiWpfv36euyxx3TzzTcrMDBQW7Zs0b/+9S9lZWUpOzvbJgB1pby8PB0/ftyqLCcnR5JUWFios2fPltm+ogoKCqzeVpSjD89D03YLZ+Z8lb++WuWv/71fkWd/sCmPzD+o43VbaPrAz2yehatiC6FOnQKrt4C3YG3CW9WktenoZwh3/MxRHX9uqQnc9XMn4G6VsTZDTSZjjGrwhaawsLCqp1BteG0QpqioSIGBgTblpTcHFRUVOWwnqVxti4qKFBAQYLefoKAgq3ru7lOSnnjiCavnAwYMUGxsrB566CHNmzdPTz/9tN1+Ss2bN09Tp061+2z37t0KCQkps72rsrKyXGrfp4/98sxMl7r1KGfm7KhubXTd+q8Vei5XBVcEMovqRMmncZj69HH/X/qtt7q2PgFPYW3CW9WEtenoZwh3/MxRHX9uqUlc/bkT8BRPrs34iwGezGrwhWb37t1VPYVqw2uDMMHBwSouLrYpP3/+vOW5o3aSytU2ODhYFy5csNvP+fPnreq5u09HHnzwQY0ePVofffTRVYMwKSkpSkpKsirbuHGjRo4cqY4dOyo+Pr7M9hVVUFCgrKwsxcbGKjQ0tML9pKbaL3/++Qp36XHOzNlR3dqoW36o8sNa6tm+2+xX+I/7xqpTp0C33pqlLVtilZ9f8fUJuBtrE96qJq1NRz9DVPb3ZGd/lrE3P2/+eagyuevnTsDdKmNtlvbrqd/r3ImdMOXntUGY6OhoHTlyxKY8NzdXktSkSRO77SIiIhQYGGipV1bb6OholZSUKC8vz+r40IULF3TixAlLPU/0WZaYmBidPHnyqvUaNWpkc+xp/34jv0ZISMhVjzO5KjQ01KUxnN0y7A2cmXM12DVYaUwlRvqps2cr7y83Pz+0UscDyou1CW9VE9amtxwZcvZnGY41X52rP3cCnuLRtelr/AxdHda+p09h1CReG4Tp3LmzMjMzdfbsWatFt23bNstze3x9fdWxY0dlZ2fbPNu2bZtatGihsLAwqz6ys7PVt29fS73s7GyZTCbLc0/06YjZbNahQ4d04403llmvJrOXKG/lysqfhzNI7mcY/Uk/RZ07YFMelb9fx+q0rIIZAQBqE74fAwC8ndfejpSYmKiSkhItXLjQUlZcXKwlS5YoLi5OMTExkoxEtN9//71N2+3bt1sFTfbs2aONGzdaHd/p1auXIiIiNH/+fKv28+fPV0hIiBISEjza55VJdUvrHT9+XHfeeWfZnyDAC9m77UjixiMAAAAAkLx4J0xcXJySkpI0fvx45eXlqWXLllq6dKkOHTqkxYsXW+oNHjxYmzdvltlstpSlpKRo0aJFSkhI0JgxY+Tv76/Zs2crKipKo0ePttQLDg7WtGnTNGLECCUlJal3797asmWLVqxYoRkzZigiIsKjfV577bW677771LFjRwUFBenTTz/Vm2++qc6dO+vRRx/11Ke2RnH0P16u7pzhf9Kuzt6ul9IdL6kJ31TRrAAA8F78fAEA8NogjCQtW7ZMkyZN0vLly3Xq1Cl16tRJa9euVY8ePcpsFxYWpk2bNmnUqFGaPn26TCaTevbsqTlz5igyMtKqbkpKivz9/TVr1iytWbNGMTExmjNnjs3NRZ7o86GHHtLWrVv1zjvv6Pz587r22muVmpqqiRMncqYOXq9018vlx4zY8QIAAAAAjnl1ECYoKEhpaWlKS0tzWGfTpk12y5s2bapVq1aVa5zk5GQlJydftZ67+1y0aFG5+gK8FbteAAAAAKD8vDYnDAAAAAAAQE3i1TthgIqqjjcseStuPAIAAAAA92AnDIAyceMRAAAAALgHO2EASLr6jhdyvwAAUD7efgsSO4YBoOoQhEGt4anrrGsKe7cdSex4AQAAAAB3IQgD1EL2dr2w4wUAAAAAPIsgDFAL2dv1wo4XAAC8H0eJAKB6IwgD1FLsegEAwHt5e14ZAEDFEIQBajCulwYAAAAA70EQBrVeTf6fJpLtAgAAAID3IAgD1BAk2wUAAAAA7+Zb1RMA4B6lu14ux44XAAAAAPAe7IQBahB2vQAAAACA9yIIA1QzJNsFAAAAgOqJ40hANWPv2JHE0SMAAAAA8HbshAG81NV2vHDsCAAAAACqF3bCAF6KHS8AAAAAULOwEwbwAlwvDQAAvNGDD9ovX7mycucBADUFQRhUC45+AKgpSne9XJ5Ylx0vAAAAAFCzEIQBKhF5XgAAAACg9iIIA1QiezteJHa9AAAA96vpO4kBoDoiCAN4ADteAABAdULABgAqB7cjAR7AzUYAAAAAgCuxEwZwETcbAQCAqsQuFgCoPgjCAOXk6IhR07PfSpJ+DL/BUsaOFwAAAADAlQjCAOXkKKnuj+E36FjY9ZrVY00VzQwAAAAAUB0QhAGuQFJdAACAstk7ArVyZfnrllUfAGoygjCo1ewFXOwdL5I4YgQAAAAAcA1BGNQKzuRz4XgRAABA1bhy10x4uNSnT9XMBQA8gSAMahxndrcQcAEAAAAAVBaCMKi22N0CAAAAAKhOCMLA6zkTbCl9n4ALAAAAAMDbEISBV+EoEQAAQPXk6BYkd0hNlc6etS5zx+1K1fHmpuo4ZwCXEIRB1ejXT89v5igRAAAAAKD2IAiDqnHggKLy9+tYnZZWxQRcAAAA4Ame3KkDAOVFEAZV5lidlkpN+KaqpwEAAAAAQKXwreoJAAAAAAAA1AbshAEAAABQ6Tx1PKiyjx05Ox4JdIHajSAMAAAAAFQSe0EbTwZmKns8AGXz6uNIxcXFGjdunJo0aaLg4GDFxcVpw4YN5Wp75MgRDRw4UPXq1VN4eLj69++vgwcP2q27ePFitWvXTkFBQWrVqpVeeuklr+wTAAAAAICapLJ+7/cWXr0TZsiQIcrIyNCTTz6pVq1aKT09XX379lVmZqa6d+/usF1+fr7i4+N15swZTZgwQf7+/pozZ45uu+027dq1Sw0aNLDUXbBggYYPH64BAwboqaee0pYtWzRy5EgVFhZq3LhxXtMnAAAAgKpVW29YYjcNPKkyfu/3Jl4bhMnKytKbb76ptLQ0jRkzRpI0ePBgdejQQampqdq6davDtvPmzdO+ffuUlZWlbt26SZL69OmjDh06aNasWZo5c6YkqaioSBMnTlRCQoIyMjIkScnJyTKZTJo2bZqGDRum+vXrV3mfAAAAAKx5MiDi7XllPDWeM4EVb8+FQ+CoeqiM3/u9jdceR8rIyJCfn5+GDRtmKQsKCtLQoUP12Wef6fDhw2W27datm+UvQpLatm2r22+/XW+//balLDMzUydOnFBKSopV+xEjRqigoEDr1q3zij4BAAAAAKhpKuP3fm/jtTthdu7cqdatWys8PNyqPDY2VpK0a9cuxcTE2LQzmUz66quv9Mgjj9g8i42N1Ycffqhz584pLCxMO3fulCR17drVql6XLl3k6+urnTt3atCgQVXeJwAAAACUxR27abx5R05t3L3z/I9S06aVO4fKVhm/93sbrw3C5ObmKjo62qa8tOzo0aN22508eVLFxcVXbdumTRvl5ubKz89PjRo1sqoXEBCgBg0aWMao6j4dycvL0/Hjx63Kvv/+e0nSJ598osLCQodtXXH+/Hnt27dPJ0+eVFBQUIX6aHXmjE6XSCdOvOHm2aG2Kyg4r23b9ik396TOn6/Y+gQ8gbUJb8XahDdjfdYcb9j5sf/ECc/ULau+Mxz1Lbnnd6IrXTnn70vO6Kcz0r6yJuIlsrOzJRm/j7Zs2dLqWWRkpM3vx6Uq4/d+b+O1QZiioiIFBgbalJcu8KKiIoftJJWrbVFRkQICAuz2ExQUZFWvKvt0ZN68eZo6dardZ7Nnzy6zrdf4sJZmN4NHrVlT1TMA7GNtwluxNuHNWJ81w4cfVn1dZ3my73KNL0kFqlYZoZ9++mk9/fTTVmWTJ0/WlClT7NavjN/7vY3XBmGCg4NVXFxsU37+/HnLc0ftJJWrbXBwsC5cuGC3n/Pnz1vVq8o+HUlJSVFSUpJV2ZEjR7Ru3TrFxsaqTp06ZbavqJycHI0cOVJz585Vs2bNPDIGUFGsT3gr1ia8FWsT3oz1CW/F2rSWn5+vrKwsJSQk6De/+Y3Vs8jISIftKuP3fm/jtUGY6OhoHTlyxKY8NzdXktSkSRO77SIiIhQYGGipV1bb6OholZSUKC8vz2p71IULF3TixAlLvaru05FGjRrZbOtq3769/vCHP5TZzlXffPONJKlXr15q3769R8cCnMX6hLdibcJbsTbhzVif8FasTVsPPfSQ020q4/d+b+O1tyN17txZe/fu1dmzZ63Kt23bZnluj6+vrzp27Gg5k3Zl2xYtWliS85T2cWXd7OxsmUwmy/Oq7hMAAAAAgJqmMn7v9zZeG4RJTExUSUmJFi5caCkrLi7WkiVLFBcXZ8mQnJOTY0lGe3nb7du3W/2F7NmzRxs3brQ6vtOrVy9FRERo/vz5Vu3nz5+vkJAQJSQkeEWfAAAAAADUNJXxe7+38drjSHFxcUpKStL48eOVl5enli1baunSpTp06JAWL15sqTd48GBt3rxZZrPZUpaSkqJFixYpISFBY8aMkb+/v2bPnq2oqCiNHj3aUi84OFjTpk3TiBEjlJSUpN69e2vLli1asWKFZsyYoYiICK/oEwAAAACAmqYyfu/3OmYvVlRUZB4zZoy5cePG5sDAQHO3bt3M//3vf63q3HbbbWZ7H8bhw4fNiYmJ5vDwcHOdOnXMd911l3nfvn12x1m4cKG5TZs25oCAAPP1119vnjNnjtlkMnldn97i2LFj5smTJ5uPHTtW1VMBbLA+4a1Ym/BWrE14M9YnvBVr030q6/d+b+FjNl8WSgIAAAAAAIBHeG1OGAAAAAAAgJqEIAwAAAAAAEAlIAgDAAAAAABQCQjCAAAAAAAAVAKCMJAkffHFF7rrrrvUuHFj1alTR506ddLcuXNVUlJiU3fNmjW66aabFBQUpGbNmmny5Mn69ddfbeqdPn1aw4YNU2RkpEJDQxUfH68dO3bYHb+8faJ2+uijj9SrVy/VrVtXYWFh6tKli9566y2beqxNVKXk5GT5+Pjorrvusvuc9YnK8vHHH+uRRx5R69atFRISohYtWugvf/mLcnNz7dbfunWrunfvrpCQEDVu3FgjR45Ufn6+Tb3i4mKNGzdOTZo0UXBwsOLi4rRhwwaX+gQccWa9AeW1fft2PfbYY2rfvr1CQ0PVrFkzDRw4UHv37rWp+9133+nOO+9UnTp1FBERoYcffljHjx+3qWcymfT888/ruuuuU1BQkDp16qQ33njD7vjl7RM1XFVfz4Sql52dbQ4ICDC3b9/ePHv2bPMrr7xi7t+/v1mSeeTIkVZ1169fb/bx8THHx8ebFy5caH788cfNvr6+5uHDh1vVKykpMd9yyy3m0NBQ85QpU8wvv/yy+YYbbjCHhYWZ9+7dW6E+UTu99tprZh8fH/Mf/vAH88svv2yeP3+++cknnzSnpaVZ1WNtoipt377dfM0115iDgoLMCQkJNs9Zn6hMXbp0MV933XXm1NRU86JFi8zjx483h4WFmaOiosy5ublWdXfu3GkOCgoy33jjjeb58+ebJ06caA4MDDTfeeedNv3ef//95muuucY8ZswY84IFC8w333yz+ZprrjFv2bKlwn0CjpR3vQHOGDBggLlx48bmxx9/3Lxo0SLztGnTzFFRUebQ0FDz7t27LfUOHz5sbtiwofn66683v/jii+YZM2aY69evb/7tb39rLi4uturz6aefNksyJycnmxcuXGhOSEgwSzK/8cYbVvWc6RM1G0EYmJOTk80BAQHmEydOWJX36NHDHB4eblV2ww03mH/729+af/nlF0vZxIkTzT4+PubvvvvOUvbWW2+ZJZlXrVplKcvLyzPXq1fP/MADD1SoT9Q+P/zwgzk4ONgmGGgPaxNVxWQymW+++WbzI488Yr722mvtBmFYn6hMmzdvNpeUlNiUSTJPnDjRqrxPnz7m6Oho85kzZyxlixYtMksyf/DBB5aybdu2mSVZBcCLiorM119/vfnmm2+uUJ+AI86sN8AZ//d//2cT8Ni7d685MDDQ/NBDD1nK/vrXv5qDg4PN//vf/yxlGzZsMEsyL1iwwFL2448/mv39/c0jRoywlJlMJvOtt95qbtq0qfnXX391uk/UfARhYL7vvvvM4eHhNj+w3XfffeaoqCjL+998841Zkvlf//qXVb0jR46YJZmnTZtmKUtKSjJHRUXZ9Dls2DBzSEiI+fz58073idpn3Lhx5oCAAPPp06fNZrPZfO7cObPJZLKpx9pEVVq6dKk5LCzMnJubazcIw/qEt4iIiDDfe++9lvfPnDljvuaaa8xjx461qldcXGyuU6eOeejQoZaysWPHmv38/KwCK2az2Txz5kyzJHNOTo7TfQKOlHe9Ae5y0003mW+66SbL+40aNTInJSXZ1GvdurX59ttvt7z/r3/9yyzJ/M0331jVW7lypVmS1c6t8vaJmo+cMFDPnj119uxZPfroo/ruu+/0v//9T6+88opWr16t8ePHW+rt3LlTktS1a1er9k2aNFHTpk0tz0vr3nTTTfL1tV5isbGxKiwstJy7dKZP1D4fffSR2rZtq/Xr16tp06YKCwtTgwYNNGnSJJlMJks91iaqyrlz5zRu3DhNmDBBjRs3tluH9QlvkJ+fr/z8fDVs2NBStnv3bv3666826yggIECdO3e2WZutW7dWeHi4Vd3Y2FhJ0q5du5zuE3CkvOsNcAez2axjx45Zvj4eOXJEeXl5Nl/HJGMNXvm1MTQ0VO3atbOpV/rc2T5R8xGEgZKTk/XYY49p6dKluuGGG9S8eXM99thjmjt3rp544glLvdKEftHR0TZ9REdH6+j/b+/eg6Mq7z+Of3azuyE3CEsCCZCASaQpIQERbQ3S0KC0DFWxDKKtoy0CKrTadqrOqLWKjqNTO62Ol0Lp8Ic6lqk2td7ob7wUI7REnDojKkpidTdcTCTAguSyyT6/P/jt+WWzu2Fz4eyavF8zO8DZ5zznezbPLrufPOfZAwci2sZrJ8lqO5A+Mfrs27dPfr9fP/7xj7Vq1So9++yzWrJkie677z7dcccdVjvGJpJlw4YNysjI0M9//vO4bRifSAW///3v1dXVpZUrV1rbGJtIVYmON2A4PP3009q/f7/1+ni617G2tjZ1dnZabSdNmiSHwxHVTkr8tbF3nxj5XMkuAMMrFAqpq6srobbp6elyOBxKS0tTaWmpvvOd72jFihUaM2aMnnnmGf30pz9VQUGBli1bJklqb2+39utrzJgxCgQC1r/b29vjtuvd10D6xFfbYMbmiRMnFAqF9MADD+i2226TJC1fvlxtbW16+OGHdfvttysnJ4exiSEbzPj8+OOP9fDDD+uZZ56JOU7CGJ8YisGMzb7efPNN3XPPPbriiitUW1trbT/dOArfH247HGOzd59APImON2Co9u7dq/Xr1+uCCy7QtddeK+n0r2PhNunp6cP22ti7T4x8zIQZYd58801lZGQkdPvoo48kSQ888IAefPBBPfPMM7rmmmt0xRVXqK6uThdeeKHWr19vfd1pRkaGJMVMaTs6Oqz7w23jtevd10D6xFfbYMZm+Od/1VVXRfR11VVXqb293Zq6ydjEUA1mfN58882qrq7W8uXL++2b8YmhGMzY7G3v3r26/PLLNWvWLG3evDniPsYmUlWi4w0YikOHDmnp0qUaN26cnn32WaWlpUk6/etY7zbD9drYuw1GPmbCjDDl5eXasmVLQm3D0+Eef/xx1dbWKjs7O+L+Sy+9VL/4xS/06aefqqyszGp/8OBBFRUVRbQ9ePCgde1juO/wtLu+7aRT6xb0riGRPvHVNpixOXnyZO3bt0+TJk2KuH/ixImSpCNHjkS0Z2xisAY6Pl9//XVt27ZNf/3rX/Xpp59a93V3d6u9vV2ffvqpvF6vxo4dy/jEkAzmtTPM7/dr8eLFGjdunF5++WXl5OTEbB9vzIXHW7jt/v37Y7aTYo/N0/UJxJPoeAMG69ixY1qyZImOHj2q+vr6qNc7Kf7rmNfrtWasFBYW6o033pAxJmIm4kBfG3v3iZGPEGaEKSgo0I9+9KMB7fP555+rp6cnanswGJQkaybMnDlzJEm7d++OeIN/4MABNTc3a+3atda2OXPmqL6+XqFQKGKByV27dikzM1MzZswYcJ/4ahvM2Dz33HO1b98+7d+/XyUlJdb28PW1+fn5khibGLqBjk+fzydJ+v73vx913/79+3XWWWfpd7/7nX72s58xPjEkg3ntlKTDhw9r8eLF6uzs1GuvvRZzHYJZs2bJ5XJp9+7duuKKK6ztXV1devfddyO2zZkzR2+88YYCgUDEYqm7du2y7h9on0A8iY43YDA6Ojp0ySWX6OOPP9arr76qmTNnRtw/ZcoU5efna/fu3VH7NjQ0RIy/OXPmaPPmzfrwww8j+uk7VgfSJ0aBZH89E5Jv1qxZxuv1mi+++MLa1t3dbc4991yTk5Njurq6rO3l5eVm9uzZEd95f+eddxqHw2E++OADa9uf//xnI8n85S9/sba1traa3Nxcs3LlyojjJ9onRp+6ujojydx+++3Wtp6eHnPhhRcar9drfV2vMYxN2Ouzzz4zdXV1Ubf8/Hwzb948U1dXZxobG632jE/Y6cSJE+b88883OTk5Zvfu3f22/e53v2sKCwtNIBCwtm3evNlIMq+88oq17d///reRZH7zm99Y2zo6OkxZWZn5xje+Mag+gXgGMt6Ageju7jaXXnqpcblc5qWXXorb7oYbbjAZGRkRX4f+6quvGknmiSeesLb5/X7jdrvN+vXrrW2hUMgsWLDATJkyJeL/6ET7xMhHCAPz1FNPGUmmtLTUPPjgg+aRRx4xF1xwgZFk7rvvvoi2L7zwgnE4HKa2ttZs2rTJ3HTTTcbpdJo1a9ZEtOvu7jbf/OY3TXZ2trnnnnvMY489ZioqKkxOTo7Zu3fvoPrE6BMKhcyiRYuMw+Ewa9euNY899pi5+OKLjSSzcePGiLaMTaSCadOmmaVLl0ZtZ3zCTpdddpmRZFatWmWefPLJiFtdXV1E23feecekp6ebc845xzzxxBPmjjvuMGPGjDGLFy+O6nfFihXG5XKZW265xWzcuNFUV1cbl8tltm/fPug+gXgSHW/AQNx8881GkrnkkkuiXh+ffPJJq53P5zMTJkwwpaWl5pFHHjH333+/GT9+vKmsrIz4JaAxxtxyyy1Gklm7dq354x//aJYuXWokmaeffjqi3UD6xMhGCANjjDHbtm0zNTU1Ji8vz3g8HlNZWWn+8Ic/xGxbV1dn5syZY9LT083UqVPNnXfeGTFbJqytrc1cd911ZsKECSYzM9PU1NSYt99+e0h9YvQ5fvy4ufnmm01BQYE1Np966qmYbRmbSLZ4IYwxjE/YZ9q0aUZSzNu0adOi2tfX15vq6mozZswYk5+fb9avXx8xiyWsvb3d/PKXvzQFBQUmPT3dnHfeeWbbtm0xa0i0TyCegYw3IFE1NTVxXx/7XiSyZ88es3jxYpOZmWlyc3PND3/4Q3Po0KGoPnt6esz9999vpk2bZjwej6moqIj7XjXRPjGyOYwxxo7LngAAAAAAAEYzvqIaAAAAAADABoQwAAAAAAAANiCEAQAAAAAAsAEhDAAAAAAAgA0IYQAAAAAAAGxACAMAAAAAAGADQhgAAAAAAAAbEMIAAAAAAADYgBAGAAAAAADABoQwAAAAAAAANiCEAQAAAAAAsAEhDAAAAAAAgA0IYQAAAAAAAGxACAMAAAAAAGADQhgAAAAAAAAbEMIAAAAAAADYgBAGAAAAAADABoQwAAAAAAAANiCEwYC1tLTo7rvvVktLS7JLAaIwPpGqGJtIVYxNpDLGJ1IVYxODRQiDAWttbdU999yj1tbWZJcCRGF8IlUxNpGqGJtIZYxPpCrGJgaLEAYAAAAAAMAGhDAAAAAAAAA2IIQBAAAAAACwgSvZBWB4HT16VNu3b1dRUZHS09PPyDEaGxsj/gRSCeMTqYqxiVTF2EQqY3wiVTE2I3V2dsrv96umpka5ubnJLielOYwxJtlFYPg8//zzWrZsWbLLAAAAAACMMn/729902WWXJbuMlMZMmBGmqKhI0qnBX1ZWluRqAAAAAAAjXWNjo5YtW2Z9HkV8hDAjTPgSpLKyMlVUVCS5GgAAAADAaHGmlsQYSViYFwAAAAAAwAaEMAAAAAAAADYghAEAAAAAALABIQwAAAAAAIANCGEAAAAAAABsQAgDAAAAAABgA0IYAAAAAAAAGxDCAAAAAAAA2IAQBgAAAAAAwAaEMAAAAAAAADYghAEAAAAAALABIQwAAAAAAIANCGEAAAAAAABsQAgDAAAAAABgA0IYAAAAAAAAGxDCAAAAAAAA2IAQBgAAAAAAwAaEMAAAAAAAADYghAEAAAAAALABIQwAAAAAAIANCGEAAAAAAABsQAgDAAAAAABgA0IYAAAAAAAAGxDCAAAAAAAA2IAQBgAAAAAAwAaEMAAAAAAAADYghAEAAAAAALABIQwAAAAAAIANCGEAAAAAAABsQAgDAAAAAABgA0IYAAAAAAAAGxDCAAAAAAAA2MCV7AIAYLTbsGGDtm7dmuwyEMOj87+QJP1kR16SK0HYypUrdddddyW7DAAAgEEhhIHtHA5HzG1ut1tOp1MdHR397u90OpWRkaHs7GwdOXJEXV1dAzq2MSbh7QPlcrm0fPlybdy4UePGjRtyfxgdtm7dqsbGRpWVlSW7FPRRlNWd7BLQS2Njo7Zu3UoIgyHz+XxavHixPvroo2SXMiAul0tOpzPivY/D4VBWVpZycnI0duxYNTc3Kxg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            "text/plain": [
              "<Figure size 1200x720 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sol = losses.copy()\n",
        "sol.sort()\n",
        "\n",
        "print(\n",
        "    \"Solution Value\",\n",
        "    -sum(sol) / len(sol),\n",
        "    \"order Qty\",\n",
        "    order,\n",
        "    \"investment\",\n",
        "    order * cost,\n",
        "    \"#samples\",\n",
        "    samples,\n",
        ")\n",
        "print(\"Losses stats:\")\n",
        "print(\n",
        "    \"CVaR(95%,90%,80%,70%)\",\n",
        "    CVaR(sol, 0.95),\n",
        "    CVaR(sol, 0.90),\n",
        "    CVaR(sol, 0.80),\n",
        "    CVaR(sol, 0.70),\n",
        ")\n",
        "print(\n",
        "    \"min\",\n",
        "    min(sol),\n",
        "    \"max\",\n",
        "    max(sol),\n",
        "    \"average\",\n",
        "    sum(sol) / samples,\n",
        "    \"VaR_75%\",\n",
        "    sol[int(0.75 * samples)],\n",
        ")\n",
        "doubleHist(sol)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "42c82214",
      "metadata": {},
      "source": [
        "# Maximize expected returns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "730a3cd3",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Writing model1.mod\n"
          ]
        }
      ],
      "source": [
        "%%writefile model1.mod\n",
        "\n",
        "param samples > 0;             # number of samples\n",
        "param demand{1..samples} >= 0; # demand for each sample\n",
        "param cost > 0;                # procurement cost of each unit\n",
        "param retail >= 0;             # selling price of each unit\n",
        "param recover < cost;          # liquidation price of each unit\n",
        "param minrev;                  # bound on minimum revenue\n",
        "param maxrev;                  # bound on maximum revenue\n",
        "\n",
        "var order >= 0;                                # number of units to order\n",
        "var sales{i in 1..samples} >= 0, <= demand[i]; # sales of each sample\n",
        "var discount{1..samples} >= 0;                 # scraped units of each sample\n",
        "var profit{1..samples} >= minrev, <= maxrev;   # profit of each sample\n",
        "\n",
        "# maximize average profit across all samples\n",
        "maximize obj: (1/samples) * sum{i in 1..samples} profit[i];\n",
        "\n",
        "# profit of each sample\n",
        "s.t. sample_profit {i in 1..samples}: profit[i] == -cost * order + retail * sales[i] + recover * discount[i];\n",
        "# sales and discount of each sample\n",
        "s.t. sample_sales {i in 1..samples}: sales[i] + discount[i] == order;"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "5fa2ffa8",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "HiGHS 1.5.1: tech:outlev=1\n",
            "Running HiGHS 1.5.1 [date: 2023-02-27, git hash: 93f1876]\n",
            "Copyright (c) 2023 HiGHS under MIT licence terms\n",
            "Presolving model\n",
            "20000 rows, 30001 cols, 70000 nonzeros\n",
            "20000 rows, 30001 cols, 70000 nonzeros\n",
            "Presolve : Reductions: rows 20000(-0); columns 30001(-0); elements 70000(-0) - Not reduced\n",
            "Problem not reduced by presolve: solving the LP\n",
            "Using EKK dual simplex solver - serial\n",
            "  Iteration        Objective     Infeasibilities num(sum)\n",
            "          0     0.0000000000e+00 Ph1: 0(0) 0s\n",
            "      20000     4.6223579332e+03 Pr: 0(0) 3s\n",
            "Model   status      : Optimal\n",
            "Simplex   iterations: 20000\n",
            "Objective value     :  4.6223579332e+03\n",
            "HiGHS run time      :          3.41\n",
            "HiGHS 1.5.1: optimal solution; objective 4622.357933\n",
            "20000 simplex iterations\n",
            "0 barrier iterations\n"
          ]
        }
      ],
      "source": [
        "# Create AMPL instance and load the model\n",
        "ampl = AMPL()\n",
        "ampl.read(\"model1.mod\")\n",
        "\n",
        "# Load the data\n",
        "ampl.param[\"samples\"] = samples\n",
        "ampl.param[\"cost\"] = cost\n",
        "ampl.param[\"recover\"] = recover\n",
        "ampl.param[\"retail\"] = retail\n",
        "ampl.param[\"minrev\"] = minrev\n",
        "ampl.param[\"maxrev\"] = maxrev\n",
        "ampl.param[\"demand\"] = demand\n",
        "\n",
        "# Set options and solve\n",
        "ampl.option[\"solver\"] = solver\n",
        "ampl.option[\"highs_options\"] = \"outlev=1\"\n",
        "ampl.solve()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "83d5e99a",
      "metadata": {},
      "source": [
        "### Explore solution"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "0eba9b2b",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model Value 4622.357933244379 order Qty 461.2352777789903 investment 922.4705555579806 #samples 10000\n",
            "Losses stats:\n",
            "CVaR(95%,90%,80%,70%) -1242.1415038411253 -1792.0472472524186 -2419.626768705242 -2851.1784224805892\n",
            "min -5996.058611126874 max 1607.765659923248 average -4622.357933244431 VaR_75% -3723.485092727757\n"
          ]
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1200x720 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sol1 = getLosses(ampl)\n",
        "solutionStats(ampl, sol1)\n",
        "doubleHist(sol1, xlabel=\"Losses Expected Value\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cac4e41f",
      "metadata": {},
      "source": [
        "# Maximize worst case"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "fe317485",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Writing model2.mod\n"
          ]
        }
      ],
      "source": [
        "%%writefile model2.mod\n",
        "\n",
        "param samples > 0;             # number of samples\n",
        "param demand{1..samples} >= 0; # demand for each sample\n",
        "param cost > 0;                # procurement cost of each unit\n",
        "param retail >= 0;             # selling price of each unit\n",
        "param recover < cost;          # liquidation price of each unit\n",
        "param minrev;                  # bound on minimum revenue\n",
        "param maxrev;                  # bound on maximum revenue\n",
        "\n",
        "var order >= 0;                                # number of units to order\n",
        "var sales{i in 1..samples} >= 0, <= demand[i]; # sales of each sample\n",
        "var discount{1..samples} >= 0;                 # scraped units of each sample\n",
        "var profit{1..samples} >= minrev, <= maxrev;   # profit of each sample\n",
        "var worst >= minrev, <= maxrev;                # worst profit\n",
        "\n",
        "# maximize worst case\n",
        "maximize obj: worst;  \n",
        "\n",
        "# profit of each sample\n",
        "s.t. sample_profit {i in 1..samples}: profit[i] == -cost * order + retail * sales[i] + recover * discount[i];\n",
        "# sales and discount of each sample\n",
        "s.t. sample_sales {i in 1..samples}: sales[i] + discount[i] == order;\n",
        "# bound worst across all samples\n",
        "s.t. worst_case {i in 1..samples}: worst <= profit[i];"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "52720312",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "HiGHS 1.5.1: tech:outlev=1\n",
            "Running HiGHS 1.5.1 [date: 2023-02-27, git hash: 93f1876]\n",
            "Copyright (c) 2023 HiGHS under MIT licence terms\n",
            "Presolving model\n",
            "30000 rows, 30002 cols, 90000 nonzeros\n",
            "20040 rows, 20042 cols, 70080 nonzeros\n",
            "20040 rows, 20042 cols, 70080 nonzeros\n",
            "Presolve : Reductions: rows 20040(-9960); columns 20042(-9960); elements 70080(-19920)\n",
            "Solving the presolved LP\n",
            "Using EKK dual simplex solver - serial\n",
            "  Iteration        Objective     Infeasibilities num(sum)\n",
            "          0     0.0000000000e+00 Ph1: 0(0) 0s\n",
            "      10041    -5.0440774870e+02 Pr: 0(0) 1s\n",
            "Solving the original LP from the solution after postsolve\n",
            "Model   status      : Optimal\n",
            "Simplex   iterations: 10041\n",
            "Objective value     :  5.0440774870e+02\n",
            "HiGHS run time      :          1.13\n",
            "HiGHS 1.5.1: optimal solution; objective 504.4077487\n",
            "10041 simplex iterations\n",
            "0 barrier iterations\n"
          ]
        }
      ],
      "source": [
        "# Create AMPL instance and load the model\n",
        "ampl = AMPL()\n",
        "ampl.read(\"model2.mod\")\n",
        "\n",
        "# Load the data\n",
        "ampl.param[\"samples\"] = samples\n",
        "ampl.param[\"cost\"] = cost\n",
        "ampl.param[\"recover\"] = recover\n",
        "ampl.param[\"retail\"] = retail\n",
        "ampl.param[\"minrev\"] = minrev\n",
        "ampl.param[\"maxrev\"] = maxrev\n",
        "ampl.param[\"demand\"] = demand\n",
        "\n",
        "# Set options and solve\n",
        "ampl.option[\"solver\"] = solver\n",
        "ampl.option[\"highs_options\"] = \"outlev=1\"\n",
        "ampl.solve()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b1c1894e",
      "metadata": {},
      "source": [
        "### Explore solution"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "832e089c",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model Value 504.4077487017854 order Qty 38.8005960539835 investment 77.601192107967 #samples 10000\n",
            "Losses stats:\n",
            "CVaR(95%,90%,80%,70%) -504.40774870178035 -504.40774870179337 -504.40774870177256 -504.40774870176483\n",
            "min -504.4077487017854 max -504.4077487017854 average -504.40774870189 VaR_75% -504.4077487017854\n"
          ]
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1200x720 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sol2 = getLosses(ampl)\n",
        "solutionStats(ampl, sol2)\n",
        "doubleHist(sol2, xlabel=\"Losses Worst\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "de823f3c",
      "metadata": {},
      "source": [
        "# Maximize worst alpha percentile ($\\alpha$-**Value at Risk**)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "5ab50651",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Writing model3.mod\n"
          ]
        }
      ],
      "source": [
        "%%writefile model3.mod\n",
        "\n",
        "param samples > 0;             # number of samples\n",
        "param demand{1..samples} >= 0; # demand for each sample\n",
        "param cost > 0;                # procurement cost of each unit\n",
        "param retail >= 0;             # selling price of each unit\n",
        "param recover < cost;          # liquidation price of each unit\n",
        "param minrev;                  # bound on minimum revenue\n",
        "param maxrev;                  # bound on maximum revenue\n",
        "param alpha >= 0, <= 1;        # value at risk\n",
        "\n",
        "var order >= 0;                                # number of units to order\n",
        "var sales{i in 1..samples} >= 0, <= demand[i]; # sales of each sample\n",
        "var discount{1..samples} >= 0;                 # scraped units of each sample\n",
        "var profit{1..samples} >= minrev, <= maxrev;   # profit of each sample\n",
        "var worst >= minrev, <= maxrev;                # worst profit\n",
        "var chance{1..samples} binary;                 # chance of each sample\n",
        "\n",
        "# maximize worst alpha percentile\n",
        "maximize obj: worst;\n",
        "\n",
        "# profit of each sample\n",
        "s.t. sample_profit {i in 1..samples}: profit[i] == -cost * order + retail * sales[i] + recover * discount[i];\n",
        "# sales and discount of each sample\n",
        "s.t. sample_sales {i in 1..samples}: sales[i] + discount[i] == order;\n",
        "# relation between worst, chance and profit\n",
        "s.t. sample_chance {i in 1..samples}: worst - (maxrev - minrev) * chance[i] <= profit[i];\n",
        "# percentile\n",
        "s.t. c4: sum{i in 1..samples} chance[i] <= samples * (1 - alpha);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "90ade616",
      "metadata": {
        "scrolled": false
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "HiGHS 1.5.1: tech:outlev=1\n",
            "lim:time=60\n",
            "Running HiGHS 1.5.1 [date: 2023-02-27, git hash: 93f1876]\n",
            "Copyright (c) 2023 HiGHS under MIT licence terms\n",
            "Presolving model\n",
            "30001 rows, 40002 cols, 110000 nonzeros\n",
            "30001 rows, 40002 cols, 100000 nonzeros\n",
            "\n",
            "Solving MIP model with:\n",
            "   30001 rows\n",
            "   40002 cols (10000 binary, 0 integer, 0 implied int., 30002 continuous)\n",
            "   100000 nonzeros\n",
            "\n",
            "        Nodes      |    B&B Tree     |            Objective Bounds              |  Dynamic Constraints |       Work      \n",
            "     Proc. InQueue |  Leaves   Expl. | BestBound       BestSol              Gap |   Cuts   InLp Confl. | LpIters     Time\n",
            "\n",
            "         0       0         0   0.00%   10067.433634    -inf                 inf        0      0      0         0    13.4s\n",
            " R       0       0         0   0.00%   10067.433634    874.5936399     1051.10%        0      0      0     36169    61.0s\n",
            "\n",
            "Solving report\n",
            "  Status            Time limit reached\n",
            "  Primal bound      874.593639936\n",
            "  Dual bound        10067.4336338\n",
            "  Gap               1051.1% (tolerance: 0.01%)\n",
            "  Solution status   feasible\n",
            "                    874.593639936 (objective)\n",
            "                    0 (bound viol.)\n",
            "                    0 (int. viol.)\n",
            "                    0 (row viol.)\n",
            "  Timing            61.06 (total)\n",
            "                    13.31 (presolve)\n",
            "                    0.00 (postsolve)\n",
            "  Nodes             0\n",
            "  LP iterations     36169 (total)\n",
            "                    0 (strong br.)\n",
            "                    0 (separation)\n",
            "                    0 (heuristics)\n",
            "HiGHS 1.5.1: interrupted\n",
            "36169 simplex iterations\n",
            "0 branching nodes\n",
            "absmipgap=9192.84, relmipgap=10.511\n"
          ]
        }
      ],
      "source": [
        "alpha = 0.75\n",
        "\n",
        "# Create AMPL instance and load the model\n",
        "ampl = AMPL()\n",
        "ampl.read(\"model3.mod\")\n",
        "\n",
        "# Load the data\n",
        "ampl.param[\"samples\"] = samples\n",
        "ampl.param[\"cost\"] = cost\n",
        "ampl.param[\"recover\"] = recover\n",
        "ampl.param[\"retail\"] = retail\n",
        "ampl.param[\"minrev\"] = minrev\n",
        "ampl.param[\"maxrev\"] = maxrev\n",
        "ampl.param[\"demand\"] = demand\n",
        "ampl.param[\"alpha\"] = alpha\n",
        "\n",
        "# Set options and solve\n",
        "ampl.option[\"solver\"] = solver\n",
        "ampl.option[\"highs_options\"] = \"outlev=1 timelim=60.0\"\n",
        "ampl.solve()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "be57315c",
      "metadata": {},
      "source": [
        "### Explore solution"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "ea0a6305",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model Value 874.593639936231 order Qty 67.27643384124855 investment 134.5528676824971 #samples 10000\n",
            "Losses stats:\n",
            "CVaR(95%,90%,80%,70%) 336.3821692062457 336.38216920624063 126.77855241620063 -207.12347904508792\n",
            "min -874.593639936231 max 336.3821692062427 average -674.2982411040476 VaR_75% -874.593639936231\n"
          ]
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1200x720 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sol3 = getLosses(ampl)\n",
        "solutionStats(ampl, sol3)\n",
        "doubleHist(sol3, xlabel=\"Losses Worst Percentile\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a3a160b8",
      "metadata": {},
      "source": [
        "# Minimize Conditional Value at Risk ($CVaR_\\alpha$)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "18ca0a0f",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Writing model4.mod\n"
          ]
        }
      ],
      "source": [
        "%%writefile model4.mod\n",
        "\n",
        "param samples > 0;             # number of samples\n",
        "param demand{1..samples} >= 0; # demand for each sample\n",
        "param cost > 0;                # procurement cost of each unit\n",
        "param retail >= 0;             # selling price of each unit\n",
        "param recover < cost;          # liquidation price of each unit\n",
        "param minrev;                  # bound on minimum revenue\n",
        "param maxrev;                  # bound on maximum revenue\n",
        "param alpha >= 0, <= 1;        # value at risk\n",
        "\n",
        "var order >= 0;                                # number of units to order\n",
        "var sales{i in 1..samples} >= 0, <= demand[i]; # sales of each sample\n",
        "var discount{1..samples} >= 0;                 # scraped units of each sample\n",
        "var profit{1..samples} >= minrev, <= maxrev;   # profit of each sample\n",
        "var nu >= minrev, <= maxrev;\n",
        "var excess{1..samples} >= 0, <= maxrev - minrev;\n",
        "\n",
        "# minimize CVaR\n",
        "minimize obj: nu + (1.0 / ((1 - alpha) * samples)) * sum{i in 1..samples} excess[i];\n",
        "\n",
        "# profit of each sample\n",
        "s.t. sample_profit {i in 1..samples}: profit[i] == -cost * order + retail * sales[i] + recover * discount[i];\n",
        "# sales and discount of each sample\n",
        "s.t. sample_sales {i in 1..samples}: sales[i] + discount[i] == order;\n",
        "# relation between excess and profit of each sample\n",
        "s.t. sample_excess {i in 1..samples}: -profit[i] - nu <= excess[i];"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "70f98e86",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "HiGHS 1.5.1: tech:outlev=1\n",
            "Running HiGHS 1.5.1 [date: 2023-02-27, git hash: 93f1876]\n",
            "Copyright (c) 2023 HiGHS under MIT licence terms\n",
            "Presolving model\n",
            "30000 rows, 40002 cols, 100000 nonzeros\n",
            "30000 rows, 40002 cols, 100000 nonzeros\n",
            "Presolve : Reductions: rows 30000(-0); columns 40002(-0); elements 100000(-0) - Not reduced\n",
            "Problem not reduced by presolve: solving the LP\n",
            "Using EKK dual simplex solver - serial\n",
            "  Iteration        Objective     Infeasibilities num(sum)\n",
            "          0    -3.3023918373e+03 Pr: 20000(5.14075e+06) 0s\n",
            "       9330    -3.2924042645e+03 Pr: 11868(1.37834e+06) 5s\n",
            "      28358    -3.0786916352e+03 Pr: 0(0); Du: 2(0.322954) 10s\n",
            "      30651    -3.0786916352e+03 Pr: 0(0) 13s\n",
            "      30651    -3.0786916352e+03 Pr: 0(0) 13s\n",
            "Model   status      : Optimal\n",
            "Simplex   iterations: 30651\n",
            "Objective value     : -3.0786916352e+03\n",
            "HiGHS run time      :         13.56\n",
            "HiGHS 1.5.1: optimal solution; objective -3078.691635\n",
            "30651 simplex iterations\n",
            "0 barrier iterations\n"
          ]
        }
      ],
      "source": [
        "alpha = 0.75\n",
        "\n",
        "# Create AMPL instance and load the model\n",
        "ampl = AMPL()\n",
        "ampl.read(\"model4.mod\")\n",
        "\n",
        "# Load the data\n",
        "ampl.param[\"samples\"] = samples\n",
        "ampl.param[\"cost\"] = cost\n",
        "ampl.param[\"recover\"] = recover\n",
        "ampl.param[\"retail\"] = retail\n",
        "ampl.param[\"minrev\"] = minrev\n",
        "ampl.param[\"maxrev\"] = maxrev\n",
        "ampl.param[\"demand\"] = demand\n",
        "ampl.param[\"alpha\"] = alpha\n",
        "\n",
        "# Set options and solve\n",
        "ampl.option[\"solver\"] = solver\n",
        "ampl.option[\"highs_options\"] = \"outlev=1\"\n",
        "ampl.solve()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a7e8bcf6",
      "metadata": {},
      "source": [
        "### Explore solution"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "ba163248",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model Value -3078.691635183291 order Qty 253.0831475661703 investment 506.1662951323406 #samples 10000\n",
            "Losses stats:\n",
            "CVaR(95%,90%,80%,70%) -2282.902154905224 -2764.9396550818556 -3027.641637712309 -3115.1505675716676\n",
            "min -3290.080918360214 max 567.0050088591486 average -3237.233597566205 VaR_75% -3290.080918360214\n"
          ]
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1200x720 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sol4 = getLosses(ampl)\n",
        "solutionStats(ampl, sol4)\n",
        "doubleHist(sol4, xlabel=\"Losses CVar\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a3a469a9",
      "metadata": {},
      "source": [
        "# Mixture of CVaR and Expected value"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "e4d2f39e",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Writing model5.mod\n"
          ]
        }
      ],
      "source": [
        "%%writefile model5.mod\n",
        "\n",
        "param samples > 0;             # number of samples\n",
        "param demand{1..samples} >= 0; # demand for each sample\n",
        "param cost > 0;                # procurement cost of each unit\n",
        "param retail >= 0;             # selling price of each unit\n",
        "param recover < cost;          # liquidation price of each unit\n",
        "param minrev;                  # bound on minimum revenue\n",
        "param maxrev;                  # bound on maximum revenue\n",
        "param alpha >= 0, <= 1;        # value at risk\n",
        "param beta >= 0, <= 1;         # weight between expected value and CVaR\n",
        "\n",
        "var order >= 0;                                # number of units to order\n",
        "var sales{i in 1..samples} >= 0, <= demand[i]; # sales of each sample\n",
        "var discount{1..samples} >= 0;                 # scraped units of each sample\n",
        "var profit{1..samples} >= minrev, <= maxrev;   # profit of each sample\n",
        "var nu >= minrev, <= maxrev;\n",
        "var excess{1..samples} >= 0, <= maxrev - minrev;\n",
        "\n",
        "# maximize weighted sum of expected value and CVaR\n",
        "maximize obj:\n",
        "    - beta * nu\n",
        "    - (beta / ((1 - alpha) * samples)) * sum{i in 1..samples} excess[i]\n",
        "    + ((1-beta)/samples) * sum{i in 1..samples} profit[i];\n",
        "\n",
        "# profit of each sample\n",
        "s.t. sample_profit {i in 1..samples}: profit[i] == -cost * order + retail * sales[i] + recover * discount[i];\n",
        "# sales and discount of each sample\n",
        "s.t. sample_sales {i in 1..samples}: sales[i] + discount[i] == order;\n",
        "# relation between excess and profit of each sample\n",
        "s.t. sample_excess {i in 1..samples}: -profit[i] - nu <= excess[i];"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "ef3d6763",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "HiGHS 1.5.1: tech:outlev=1\n",
            "Running HiGHS 1.5.1 [date: 2023-02-27, git hash: 93f1876]\n",
            "Copyright (c) 2023 HiGHS under MIT licence terms\n",
            "Presolving model\n",
            "30000 rows, 40002 cols, 100000 nonzeros\n",
            "30000 rows, 40002 cols, 100000 nonzeros\n",
            "Presolve : Reductions: rows 30000(-0); columns 40002(-0); elements 100000(-0) - Not reduced\n",
            "Problem not reduced by presolve: solving the LP\n",
            "Using EKK dual simplex solver - serial\n",
            "  Iteration        Objective     Infeasibilities num(sum)\n",
            "          0     0.0000000000e+00 Ph1: 0(0) 0s\n",
            "      18511     3.7045413447e+03 Pr: 5442(14570.3) 5s\n",
            "      21520     3.6476039870e+03 Pr: 0(0) 7s\n",
            "      21520     3.6476039870e+03 Pr: 0(0) 7s\n",
            "Model   status      : Optimal\n",
            "Simplex   iterations: 21520\n",
            "Objective value     :  3.6476039870e+03\n",
            "HiGHS run time      :          7.46\n",
            "HiGHS 1.5.1: optimal solution; objective 3647.603987\n",
            "21520 simplex iterations\n",
            "0 barrier iterations\n"
          ]
        }
      ],
      "source": [
        "beta = 0.50\n",
        "alpha = 0.75\n",
        "\n",
        "# Create AMPL instance and load the model\n",
        "ampl = AMPL()\n",
        "ampl.read(\"model5.mod\")\n",
        "\n",
        "# Load the data\n",
        "ampl.param[\"samples\"] = samples\n",
        "ampl.param[\"cost\"] = cost\n",
        "ampl.param[\"recover\"] = recover\n",
        "ampl.param[\"retail\"] = retail\n",
        "ampl.param[\"minrev\"] = minrev\n",
        "ampl.param[\"maxrev\"] = maxrev\n",
        "ampl.param[\"demand\"] = demand\n",
        "ampl.param[\"alpha\"] = alpha\n",
        "ampl.param[\"beta\"] = beta\n",
        "\n",
        "# Set options and solve\n",
        "ampl.option[\"solver\"] = solver\n",
        "ampl.option[\"highs_options\"] = \"outlev=1\"\n",
        "ampl.solve()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8b7952d1",
      "metadata": {},
      "source": [
        "### Explore solution"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "62fe94a7",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model Value 3647.6039869767988 order Qty 415.1543649507064 investment 830.3087299014128 #samples 10000\n",
            "Losses stats:\n",
            "CVaR(95%,90%,80%,70%) -1472.5460679825462 -2022.4518113938377 -2650.031332846664 -3081.582986622015\n",
            "min -5397.006744359185 max 1377.361095781829 average -4554.551834745325 VaR_75% -3953.889656869175\n"
          ]
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 1200x720 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sol5 = getLosses(ampl)\n",
        "solutionStats(ampl, sol5)\n",
        "doubleHist(sol5, xlabel=\"Losses Exp+CVar\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "79eb6acf",
      "metadata": {},
      "source": [
        "# Comparing solution approaches and behavior"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "0c753b9a",
      "metadata": {
        "scrolled": false
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1740x720 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1740x720 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots(figsize=(14.5, 6), dpi=dpi)\n",
        "xmin = min(sol1 + sol2 + sol3 + sol4 + sol5)\n",
        "xmax = max(sol1 + sol2 + sol3 + sol4 + sol5)\n",
        "bins = numpy.linspace(xmin, xmax, 150)\n",
        "ax.grid(True)\n",
        "ax.set_title(\"Comparison\")\n",
        "d = True\n",
        "c = True\n",
        "a = 0.4\n",
        "# plot the density and cumulative histogram\n",
        "n, bins2, patches = ax.hist(\n",
        "    sol, bins, alpha=a, density=d, cumulative=c, label=\"Fixed\", color=\"blue\"\n",
        ")\n",
        "n, bins2, patches = ax.hist(\n",
        "    sol1, bins, alpha=a, density=d, cumulative=c, label=\"Expected Value\", color=\"red\"\n",
        ")\n",
        "n, bins2, patches = ax.hist(\n",
        "    sol2, bins, alpha=a, density=d, cumulative=c, label=\"Worst Case\", color=\"cyan\"\n",
        ")\n",
        "n, bins2, patches = ax.hist(\n",
        "    sol3, bins, alpha=a, density=d, cumulative=c, label=\"VaR_75%\", color=\"green\"\n",
        ")\n",
        "n, bins2, patches = ax.hist(\n",
        "    sol4, bins, alpha=a, density=d, cumulative=c, label=\"CVaR_75%\", color=\"blue\"\n",
        ")\n",
        "n, bins2, patches = ax.hist(\n",
        "    sol5, bins, alpha=a, density=d, cumulative=c, label=\"Expected +CVaR\", color=\"yellow\"\n",
        ")\n",
        "# tidy up the figure\n",
        "ax.legend(loc=\"upper left\")\n",
        "plt.show()\n",
        "\n",
        "data = [sol1, sol2, sol3, sol4, sol5]\n",
        "plt.figure(figsize=(14.5, 6), dpi=dpi)\n",
        "plt.boxplot(data, whis=[5, 95])\n",
        "plt.xticks([1, 2, 3, 4, 5], [\"Expected\", \"Worst\", \"VaR_75%\", \"CVaR_75%\", \"E+CVaR_75%\"])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "e7ac984c",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<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>Metric</th>\n",
              "      <th>Expected</th>\n",
              "      <th>Worst</th>\n",
              "      <th>VaR_75%</th>\n",
              "      <th>CVaR_75%</th>\n",
              "      <th>Ex+CVaR_75%</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Expected Value</td>\n",
              "      <td>-4622.357933</td>\n",
              "      <td>-504.407749</td>\n",
              "      <td>-674.298241</td>\n",
              "      <td>-3237.233598</td>\n",
              "      <td>-4554.551835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CVaR_70%</td>\n",
              "      <td>-2851.178422</td>\n",
              "      <td>-504.407749</td>\n",
              "      <td>-207.123479</td>\n",
              "      <td>-3115.150568</td>\n",
              "      <td>-3081.582987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CVaR_80%</td>\n",
              "      <td>-2419.626769</td>\n",
              "      <td>-504.407749</td>\n",
              "      <td>126.778552</td>\n",
              "      <td>-3027.641638</td>\n",
              "      <td>-2650.031333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CVaR_90%</td>\n",
              "      <td>-1792.047247</td>\n",
              "      <td>-504.407749</td>\n",
              "      <td>336.382169</td>\n",
              "      <td>-2764.939655</td>\n",
              "      <td>-2022.451811</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CVaR_95%</td>\n",
              "      <td>-1242.141504</td>\n",
              "      <td>-504.407749</td>\n",
              "      <td>336.382169</td>\n",
              "      <td>-2282.902155</td>\n",
              "      <td>-1472.546068</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Metric     Expected       Worst     VaR_75%     CVaR_75%  \\\n",
              "0  Expected Value -4622.357933 -504.407749 -674.298241 -3237.233598   \n",
              "1        CVaR_70% -2851.178422 -504.407749 -207.123479 -3115.150568   \n",
              "2        CVaR_80% -2419.626769 -504.407749  126.778552 -3027.641638   \n",
              "3        CVaR_90% -1792.047247 -504.407749  336.382169 -2764.939655   \n",
              "4        CVaR_95% -1242.141504 -504.407749  336.382169 -2282.902155   \n",
              "\n",
              "   Ex+CVaR_75%  \n",
              "0 -4554.551835  \n",
              "1 -3081.582987  \n",
              "2 -2650.031333  \n",
              "3 -2022.451811  \n",
              "4 -1472.546068  "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "data = [sol1, sol2, sol3, sol4, sol5]\n",
        "colnames = [\"Expected\", \"Worst\", \"VaR_75%\", \"CVaR_75%\", \"Ex+CVaR_75%\"]\n",
        "cvars = [70, 80, 90, 95]\n",
        "\n",
        "display(getTable(data, colnames, cvars))"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.6"
    },
    "vscode": {
      "interpreter": {
        "hash": "ee4198aac13dae9b6fccd443041680d99c2643c4956ef7c80ff6dcc0057cb523"
      }
    },
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