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diff --git a/_sources/notebooks/04/forex-arbitrage.ipynb b/_sources/notebooks/04/forex-arbitrage.ipynb
index 84086199..fd345ff2 100644
--- a/_sources/notebooks/04/forex-arbitrage.ipynb
+++ b/_sources/notebooks/04/forex-arbitrage.ipynb
@@ -6,9 +6,9 @@
     "id": "uf-ZAG8ddOxf"
    },
    "source": [
-    "# Forex Arbitrage\n",
+    "# Extra material: Forex Arbitrage\n",
     "\n",
-    "This notebook presents an example of linear optimization on a network model for financial transactions. The goal is to identify whether or not an arbitrage opportunity exists given a matrix of cross-currency exchange rates. Other treatments of this problem and application are available, including the following links.\n",
+    "This notebook presents an example of linear optimization on a network model for financial transactions. The goal is to identify whether an arbitrage opportunity exists given a matrix of cross-currency exchange rates. Other treatments of this problem and application are available, including the following links.\n",
     "\n",
     "* [Crypto Arbitrage Framework](https://github.com/hzjken/crypto-arbitrage-framework)\n",
     "* [Crypto Trading and Arbitrage Identification Strategies](https://nbviewer.org/github/rcroessmann/sharing_public/blob/master/arbitrage_identification.ipynb)\n"
@@ -20,12 +20,8 @@
    "source": [
     "## Preamble: Install Pyomo and a solver\n",
     "\n",
-    "This cell selects and verifies a global SOLVER for the notebook.\n",
-    "\n",
-    "If run on Google Colab, the cell installs Pyomo and HiGHS, then sets SOLVER to \n",
-    "use the Highs solver via the appsi module. If run elsewhere, it assumes Pyomo and CBC\n",
-    "have been previously installed and sets SOLVER to use the CBC solver via the Pyomo \n",
-    "SolverFactory. It then verifies that SOLVER is available."
+    "This cell selects and verifies a global SOLVER for the notebook. If run on Google Colab, the cell installs Pyomo and HiGHS, then sets SOLVER to \n",
+    "use the HiGHs solver via the Pyomo SolverFactory. It then verifies that SOLVER is available."
    ]
   },
   {
@@ -41,36 +37,30 @@
    "outputs": [],
    "source": [
     "import sys\n",
-    "\n",
+    " \n",
     "if 'google.colab' in sys.modules:\n",
     "    !pip install pyomo >/dev/null 2>/dev/null\n",
     "    !pip install highspy >/dev/null 2>/dev/null\n",
-    "\n",
-    "    from pyomo.environ import SolverFactory\n",
-    "    SOLVER = SolverFactory('appsi_highs')\n",
-    "    \n",
-    "else:\n",
-    "    from pyomo.environ import SolverFactory\n",
-    "    SOLVER = SolverFactory('cbc')\n",
-    "\n",
-    "assert SOLVER.available(), f\"Solver {SOLVER} is not available.\""
+    " \n",
+    "solver = 'appsi_highs'\n",
+    " \n",
+    "import pyomo.environ as pyo\n",
+    "SOLVER = pyo.SolverFactory(solver)\n",
+    " \n",
+    "assert SOLVER.available(), f\"Solver {solver} is not available.\""
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: graphviz in /Users/jeff/opt/anaconda3/lib/python3.9/site-packages (0.20.1)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "!pip install graphviz --upgrade"
+    "import networkx as nx\n",
+    "import pandas as pd\n",
+    "import pyomo.environ as pyo\n",
+    "import numpy as np\n",
+    "import io"
    ]
   },
   {
@@ -91,8 +81,7 @@
     "\n",
     "Needless to say, if a simple round-trip transaction like this reliably produced a net gain then there would many eager traders ready to take advantage of the situation. Trading situations offering a net gain with no risk are called arbitrage, and are the subject of intense interest by traders in the foreign exchange (forex) and crypto-currency markets around the globe.\n",
     "\n",
-    "As one might expect, arbitrage opportunities involving a simple round-trip between a pair of currencies are almost non-existent in real-world markets. When the do appear, they are easily detected and rapid and automated trading  quickly exploit the situation. More complex arbitrage opportunities, however, can arise when working with three more currencies and a table of cross-currency exchange rates.\n",
-    "\n"
+    "As one might expect, arbitrage opportunities involving a simple round-trip between a pair of currencies are almost non-existent in real-world markets. When the do appear, they are easily detected and rapid and automated trading  quickly exploit the situation. More complex arbitrage opportunities, however, can arise when working with three more currencies and a table of cross-currency exchange rates."
    ]
   },
   {
@@ -105,11 +94,11 @@
     "\n",
     "Consider the following cross-currency matrix. \n",
     "\n",
-    "| i <- J | USD | EUR | JPY |\n",
+    "| i <-- j | USD | EUR | JPY |\n",
     "| :--- | :---: | :---: | :---: |\n",
     "| USD | 1.0 | 2.0 | 0.01 |\n",
     "| EUR | 0.5 | 1.0 | 0.0075 |\n",
-    "| JPY | 100.0 | 133 1/3 | 1.0 |\n",
+    "| JPY | 100.0 | 133.3 | 1.0 |\n",
     "\n",
     "\n",
     "Entry $a_{m, n}$ is the number units of currency $m$ received in exchange for one unit of currency $n$.  We use the notation \n",
@@ -129,7 +118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 11,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -213,15 +202,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1.0\n",
-      "1.0\n",
-      "1.0\n"
+      "Net change factor of the transaction chain USD -> EUR -> USD is: 1.0\n",
+      "Net change factor of the transaction chain USD -> JPY -> USD is: 1.0\n",
+      "Net change factor of the transaction chain USD -> JPY -> USD is: 1.0\n"
      ]
     }
    ],
    "source": [
-    "import pandas as pd\n",
-    "\n",
     "df = pd.DataFrame(\n",
     "    [[1.0, 0.5, 100], [2.0, 1.0, 1 / 0.0075], [0.01, 0.0075, 1.0]],\n",
     "    columns=[\"USD\", \"EUR\", \"JPY\"],\n",
@@ -230,14 +217,15 @@
     "\n",
     "display(df)\n",
     "\n",
-    "# USD -> EUR -> USD\n",
-    "print(df.loc[\"USD\", \"EUR\"] * df.loc[\"EUR\", \"USD\"])\n",
-    "\n",
-    "# USD -> JPY -> USD\n",
-    "print(df.loc[\"USD\", \"JPY\"] * df.loc[\"JPY\", \"USD\"])\n",
-    "\n",
-    "# EUR -> JPY -> EUR\n",
-    "print(df.loc[\"EUR\", \"JPY\"] * df.loc[\"JPY\", \"EUR\"])"
+    "print(\n",
+    "    f\"Net gain factor of the currency exchange chain USD -> EUR -> USD is: {df.loc['USD', 'EUR'] * df.loc['EUR', 'USD']}\"\n",
+    ")\n",
+    "print(\n",
+    "    f\"Net gain factor of the currency exchange chain USD -> JPY -> USD is: {df.loc['USD', 'JPY'] * df.loc['JPY', 'USD']}\"\n",
+    ")\n",
+    "print(\n",
+    "    f\"Net gain factor of the currency exchangen USD -> JPY -> USD is: {df.loc['EUR', 'JPY'] * df.loc['JPY', 'EUR']}\"\n",
+    ")"
    ]
   },
   {
@@ -259,7 +247,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 13,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -283,7 +271,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1.5\n"
+      "Net gain factor of the currency exchangen USD -> JPY -> EUR -> USD is: 1.5\n"
      ]
     }
    ],
@@ -292,7 +280,9 @@
     "J = \"JPY\"\n",
     "K = \"EUR\"\n",
     "\n",
-    "print(df.loc[I, K] * df.loc[K, J] * df.loc[J, I])"
+    "print(\n",
+    "    f\"Net gain factor of the currency exchangen USD -> JPY -> EUR -> USD is: {df.loc[I, K] * df.loc[K, J] * df.loc[J, I]}\"\n",
+    ")"
    ]
   },
   {
@@ -312,11 +302,11 @@
    "source": [
     "## Modeling\n",
     "\n",
-    "The cross-currency table $A$ provides exchange rates among currencies. Entry $a_{i,j}$ in row $i$, column $j$ tells us how many units of currency $i$ are received in exchange for one unit of currency $j$. We'll use the notation $a_{i, j} = a_{i\\leftarrow j}$ to remind ourselves of this relationship.\n",
+    "The cross-currency table $A$ provides exchange rates among currencies. Entry $a_{i,j}$ in row $i$, column $j$ tells us how many units of currency $i$ are received in exchange for one unit of currency $j$. We use the notation $a_{i, j} = a_{i\\leftarrow j}$ to remind ourselves of this relationship.\n",
     "\n",
     "We start with $w_j(0)$ units of currency $j \\in N$, where $N$ is the set of all currencies in the data set. We consider a sequence of trades $t = 1, 2, \\ldots, T$ where $w_j(t)$ is the amount of currency $j$ on hand after completing trade $t$.\n",
     "\n",
-    "Each trade is executed in two phases. In the first phase an amount $x_{i\\leftarrow j}(t)$  of currency $j$ is committed for exchange to currency $i$. This allows a trade to include multiple currency transactions. After the commitment the unencumbered balance for currency $j$ must satisfy trading constraints. Each trade consists of simultaneous transactions in one or more currencies.\n",
+    "Each trade is executed in two phases. In the first phase an amount $x_{i\\leftarrow j}(t)$ of currency $j$ is committed for exchange to currency $i$. This allows a trade to include multiple currency transactions. After the commitment the unencumbered balance for currency $j$ must satisfy trading constraints. Each trade consists of simultaneous transactions in one or more currencies.\n",
     "\n",
     "$$w_j(t-1) - \\sum_{i\\ne j} x_{i\\leftarrow j}(t) \\geq 0$$\n",
     "\n",
@@ -333,7 +323,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 26,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -357,49 +347,67 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Running HiGHS 1.5.3 [date: 2023-05-16, git hash: 594fa5a9d]\n",
+      "Copyright (c) 2023 HiGHS under MIT licence terms\n",
       "\n",
       "t = 0\n",
       "\n",
-      "w[USD,0] =      0.00 \n",
-      "w[EUR,0] =    100.00 \n",
-      "w[JPY,0] =      0.00 \n",
+      "w[USD,0] =     0.00000 \n",
+      "w[EUR,0] =   100.00000 \n",
+      "w[JPY,0] =     0.00000 \n",
+      "w[GBP,0] =     0.00000 \n",
+      "w[CHF,0] =     0.00000 \n",
+      "w[CAD,0] =     0.00000 \n",
+      "w[AUD,0] =     0.00000 \n",
+      "w[HKD,0] =     0.00000 \n",
       "\n",
       "t = 1\n",
       "\n",
-      "EUR -> USD  Convert 100.0 EUR to 200.0 USD\n",
+      "EUR -> JPY  Convert   100.00000 EUR to 13160.97000 JPY\n",
       "\n",
-      "w[USD,1] =    200.00 \n",
-      "w[EUR,1] =     -0.00 \n",
-      "w[JPY,1] =      0.00 \n",
+      "w[USD,1] =     0.00000 \n",
+      "w[EUR,1] =     0.00000 \n",
+      "w[JPY,1] = 13160.97000 \n",
+      "w[GBP,1] =     0.00000 \n",
+      "w[CHF,1] =     0.00000 \n",
+      "w[CAD,1] =     0.00000 \n",
+      "w[AUD,1] =     0.00000 \n",
+      "w[HKD,1] =     0.00000 \n",
       "\n",
       "t = 2\n",
       "\n",
-      "USD -> JPY  Convert 200.0 USD to 20000.0 JPY\n",
+      "JPY -> CAD  Convert 13160.97000 JPY to   140.82238 CAD\n",
       "\n",
-      "w[USD,2] =      0.00 \n",
-      "w[EUR,2] =     -0.00 \n",
-      "w[JPY,2] =  20000.00 \n",
+      "w[USD,2] =     0.00000 \n",
+      "w[EUR,2] =     0.00000 \n",
+      "w[JPY,2] =     0.00000 \n",
+      "w[GBP,2] =     0.00000 \n",
+      "w[CHF,2] =     0.00000 \n",
+      "w[CAD,2] =   140.82238 \n",
+      "w[AUD,2] =     0.00000 \n",
+      "w[HKD,2] =     0.00000 \n",
       "\n",
       "t = 3\n",
       "\n",
-      "JPY -> EUR  Convert 20000.0 JPY to 150.0 EUR\n",
+      "CAD -> EUR  Convert   140.82238 CAD to   100.44860 EUR\n",
       "\n",
-      "w[USD,3] =      0.00 \n",
-      "w[EUR,3] =    150.00 \n",
-      "w[JPY,3] =     -0.00 \n",
-      "100.0\n",
-      "150.0\n"
+      "w[USD,3] =     0.00000 \n",
+      "w[EUR,3] =   100.44860 \n",
+      "w[JPY,3] =     0.00000 \n",
+      "w[GBP,3] =     0.00000 \n",
+      "w[CHF,3] =     0.00000 \n",
+      "w[CAD,3] =     0.00000 \n",
+      "w[AUD,3] =     0.00000 \n",
+      "w[HKD,3] =     0.00000 \n",
+      "\n",
+      "Initial wealth   100.00000 EUR\n",
+      "Final wealth   100.44860 EUR\n"
      ]
     }
    ],
    "source": [
-    "import pyomo.environ as pyo\n",
-    "import numpy as np\n",
-    "from graphviz import Digraph\n",
-    "\n",
-    "\n",
     "def arbitrage(T, df, R=\"EUR\"):\n",
-    "    m = pyo.ConcreteModel()\n",
+    "    m = pyo.ConcreteModel(\"Forex Arbitrage\")\n",
     "\n",
     "    # length of trading chain\n",
     "    m.T0 = pyo.RangeSet(0, T)\n",
@@ -450,19 +458,19 @@
     "            for i, j in m.ARCS:\n",
     "                if m.x[i, j, t]() > 0:\n",
     "                    print(\n",
-    "                        f\"{j} -> {i}  Convert {m.x[i, j, t]()} {j} to {df.loc[i,j]*m.x[i,j,t]()} {i}\"\n",
+    "                        f\"{j} -> {i}  Convert {m.x[i, j, t]():11.5f} {j} to {df.loc[i,j]*m.x[i,j,t]():11.5f} {i}\"\n",
     "                    )\n",
     "            print()\n",
     "\n",
     "        for i in m.NODES:\n",
-    "            print(f\"w[{i},{t}] = {m.w[i, t]():9.2f} \")\n",
+    "            print(f\"w[{i},{t}] = {abs(m.w[i, t]()):11.5f} \")\n",
     "\n",
     "    return m\n",
     "\n",
     "\n",
     "m = arbitrage(3, df, \"EUR\")\n",
-    "print(m.w[\"EUR\", 0]())\n",
-    "print(m.w[\"EUR\", 3]())"
+    "print(f\"\\nInitial wealth {m.w['EUR', 0]():11.5f} EUR\")\n",
+    "print(f\"Final wealth {m.w['EUR', 3]():11.5f} EUR\")"
    ]
   },
   {
@@ -476,7 +484,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -484,12 +492,12 @@
      "output_type": "stream",
      "text": [
       "\n",
-      " 100.0 EUR -> 200.0 USD -> 20000.0 JPY -> 150.0 EUR\n"
+      "   100.00000 USD -> 11861.00000 JPY ->   126.91270 CAD ->   100.45140 USD\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -499,16 +507,13 @@
     }
    ],
    "source": [
-    "import networkx as nx\n",
-    "\n",
-    "\n",
     "def display_graph(m):\n",
     "    path = []\n",
     "\n",
     "    for t in m.T0:\n",
     "        for i in m.NODES:\n",
     "            if m.w[i, t]() >= 1e-6:\n",
-    "                path.append(f\"{m.w[i, t]()} {i}\")\n",
+    "                path.append(f\"{m.w[i, t]():11.5f} {i}\")\n",
     "    path = \" -> \".join(path)\n",
     "    print(\"\\n\", path)\n",
     "\n",
@@ -562,7 +567,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 32,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -738,16 +743,13 @@
     "HKD\t7.8175\t8.6743\t0.0659\t10.2784\t8.3467\t6.1877\t5.7662\t-\n",
     "\"\"\"\n",
     "\n",
-    "import pandas as pd\n",
-    "import io\n",
-    "\n",
     "df = pd.read_csv(io.StringIO(bloomberg.replace(\"-\", \"1.0\")), sep=\"\\t\", index_col=0)\n",
     "display(df)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 33,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -772,68 +774,73 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Running HiGHS 1.5.3 [date: 2023-05-16, git hash: 594fa5a9d]\n",
+      "Copyright (c) 2023 HiGHS under MIT licence terms\n",
       "\n",
       "t = 0\n",
       "\n",
-      "w[USD,0] =    100.00 \n",
-      "w[EUR,0] =      0.00 \n",
-      "w[JPY,0] =      0.00 \n",
-      "w[GBP,0] =      0.00 \n",
-      "w[CHF,0] =      0.00 \n",
-      "w[CAD,0] =      0.00 \n",
-      "w[AUD,0] =      0.00 \n",
-      "w[HKD,0] =      0.00 \n",
+      "w[USD,0] =   100.00000 \n",
+      "w[EUR,0] =     0.00000 \n",
+      "w[JPY,0] =     0.00000 \n",
+      "w[GBP,0] =     0.00000 \n",
+      "w[CHF,0] =     0.00000 \n",
+      "w[CAD,0] =     0.00000 \n",
+      "w[AUD,0] =     0.00000 \n",
+      "w[HKD,0] =     0.00000 \n",
       "\n",
       "t = 1\n",
       "\n",
-      "USD -> JPY  Convert 100.0 USD to 11861.0 JPY\n",
+      "USD -> JPY  Convert   100.00000 USD to 11861.00000 JPY\n",
       "\n",
-      "w[USD,1] =      0.00 \n",
-      "w[EUR,1] =      0.00 \n",
-      "w[JPY,1] =  11861.00 \n",
-      "w[GBP,1] =      0.00 \n",
-      "w[CHF,1] =      0.00 \n",
-      "w[CAD,1] =      0.00 \n",
-      "w[AUD,1] =      0.00 \n",
-      "w[HKD,1] =      0.00 \n",
+      "w[USD,1] =     0.00000 \n",
+      "w[EUR,1] =     0.00000 \n",
+      "w[JPY,1] = 11861.00000 \n",
+      "w[GBP,1] =     0.00000 \n",
+      "w[CHF,1] =     0.00000 \n",
+      "w[CAD,1] =     0.00000 \n",
+      "w[AUD,1] =     0.00000 \n",
+      "w[HKD,1] =     0.00000 \n",
       "\n",
       "t = 2\n",
       "\n",
-      "USD -> JPY  Convert 1.0527111e-13 USD to 1.2486206357100001e-11 JPY\n",
-      "JPY -> CAD  Convert 11861.0 JPY to 126.91269999999999 CAD\n",
+      "JPY -> CAD  Convert 11861.00000 JPY to   126.91270 CAD\n",
       "\n",
-      "w[USD,2] =      0.00 \n",
-      "w[EUR,2] =      0.00 \n",
-      "w[JPY,2] =      0.00 \n",
-      "w[GBP,2] =     -0.00 \n",
-      "w[CHF,2] =      0.00 \n",
-      "w[CAD,2] =    126.91 \n",
-      "w[AUD,2] =      0.00 \n",
-      "w[HKD,2] =      0.00 \n",
+      "w[USD,2] =     0.00000 \n",
+      "w[EUR,2] =     0.00000 \n",
+      "w[JPY,2] =     0.00000 \n",
+      "w[GBP,2] =     0.00000 \n",
+      "w[CHF,2] =     0.00000 \n",
+      "w[CAD,2] =   126.91270 \n",
+      "w[AUD,2] =     0.00000 \n",
+      "w[HKD,2] =     0.00000 \n",
       "\n",
       "t = 3\n",
       "\n",
-      "CAD -> USD  Convert 126.9127 CAD to 100.45140205 USD\n",
-      "JPY -> EUR  Convert 1.1066836e-11 JPY to 8.41079536e-14 EUR\n",
+      "CAD -> USD  Convert   126.91270 CAD to   100.45140 USD\n",
       "\n",
-      "w[USD,3] =    100.45 \n",
-      "w[EUR,3] =      0.00 \n",
-      "w[JPY,3] =      0.00 \n",
-      "w[GBP,3] =      0.00 \n",
-      "w[CHF,3] =      0.00 \n",
-      "w[CAD,3] =      0.00 \n",
-      "w[AUD,3] =      0.00 \n",
-      "w[HKD,3] =      0.00 \n"
+      "w[USD,3] =   100.45140 \n",
+      "w[EUR,3] =     0.00000 \n",
+      "w[JPY,3] =     0.00000 \n",
+      "w[GBP,3] =     0.00000 \n",
+      "w[CHF,3] =     0.00000 \n",
+      "w[CAD,3] =     0.00000 \n",
+      "w[AUD,3] =     0.00000 \n",
+      "w[HKD,3] =     0.00000 \n",
+      "\n",
+      "Initial wealth:   100.00000 USD\n",
+      "Final wealth:     100.45140 USD\n"
      ]
     }
    ],
    "source": [
-    "m = arbitrage(3, df, \"USD\")"
+    "m = arbitrage(3, df, \"USD\")\n",
+    "print(f\"\\nInitial wealth: {m.w['USD', 0]():11.5f} USD\")\n",
+    "print(f\"Final wealth:   {m.w['USD', 3]():11.5f} USD\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -841,12 +848,12 @@
      "output_type": "stream",
      "text": [
       "\n",
-      " 100.0 USD -> 11861.0 JPY -> 126.9127 CAD -> 100.4514 USD\n"
+      "   100.00000 USD -> 11861.00000 JPY ->   126.91270 CAD ->   100.45140 USD\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -858,13 +865,6 @@
    "source": [
     "display_graph(m)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -883,7 +883,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.10.10"
   }
  },
  "nbformat": 4,
diff --git a/_sources/notebooks/04/graph-coloring.ipynb b/_sources/notebooks/04/graph-coloring.ipynb
index 460177f1..466afb07 100644
--- a/_sources/notebooks/04/graph-coloring.ipynb
+++ b/_sources/notebooks/04/graph-coloring.ipynb
@@ -17,7 +17,7 @@
     "```{index} networkx\n",
     "```\n",
     "\n",
-    "# Scheduling as a graph coloring problem"
+    "# Exam room scheduling"
    ]
   },
   {
diff --git a/_sources/notebooks/05/markowitz_portfolio.ipynb b/_sources/notebooks/05/markowitz_portfolio.ipynb
index 87f3aea6..168379b8 100644
--- a/_sources/notebooks/05/markowitz_portfolio.ipynb
+++ b/_sources/notebooks/05/markowitz_portfolio.ipynb
@@ -41,8 +41,7 @@
    },
    "outputs": [],
    "source": [
-    "import sys\n",
-    "import os\n",
+    "import sys, os\n",
     "\n",
     "if 'google.colab' in sys.modules:\n",
     "    !pip install idaes-pse --pre >/dev/null 2>/dev/null\n",
@@ -165,7 +164,7 @@
     }
    ],
    "source": [
-    "# Specify the initial capital, the risk threshold, and the guaranteed return rate. \n",
+    "# Specify the initial capital, the risk threshold, and the guaranteed return rate.\n",
     "C = 1\n",
     "gamma = 1\n",
     "R = 1.01\n",
@@ -179,14 +178,14 @@
     "assert np.all(np.linalg.eigvals(Sigma) >= 0)\n",
     "\n",
     "# If you want to change the covariance matrix Sigma, ensure you input a semi-definite positive one.\n",
-    "# The easiest way to generate a random covariance matrix is first generating a random m x m matrix A \n",
+    "# The easiest way to generate a random covariance matrix is first generating a random m x m matrix A\n",
     "# and then taking the matrix A^T A (which is always semi-definite positive)\n",
     "# m = 3\n",
     "# A = np.random.rand(m, m)\n",
     "# Sigma = A.T @ A\n",
     "\n",
+    "\n",
     "def markowitz(gamma, mu, Sigma):\n",
-    "    \n",
     "    model = pyo.ConcreteModel(\"Markowitz portfolio optimization\")\n",
     "\n",
     "    model.xtilde = pyo.Var(domain=pyo.NonNegativeReals)\n",
@@ -205,13 +204,22 @@
     "        return sum(m.x[i] for i in range(n)) + m.xtilde == C\n",
     "\n",
     "    result = pyo.SolverFactory(SOLVER).solve(model)\n",
-    "    \n",
+    "\n",
     "    return result, model\n",
     "\n",
+    "\n",
     "result, model = markowitz(gamma, mu, Sigma)\n",
     "\n",
-    "display(Markdown(f\"**Solver status:** *{result.solver.status}, {result.solver.termination_condition}*\"))\n",
-    "display(Markdown(f\"**Solution:** $\\\\tilde x = {model.xtilde.value:.3f}$, $x_1 = {model.x[0].value:.3f}$,  $x_2 = {model.x[1].value:.3f}$,  $x_3 = {model.x[2].value:.3f}$\"))\n",
+    "display(\n",
+    "    Markdown(\n",
+    "        f\"**Solver status:** *{result.solver.status}, {result.solver.termination_condition}*\"\n",
+    "    )\n",
+    ")\n",
+    "display(\n",
+    "    Markdown(\n",
+    "        f\"**Solution:** $\\\\tilde x = {model.xtilde.value:.3f}$, $x_1 = {model.x[0].value:.3f}$,  $x_2 = {model.x[1].value:.3f}$,  $x_3 = {model.x[2].value:.3f}$\"\n",
+    "    )\n",
+    ")\n",
     "display(Markdown(f\"**Maximizes objective value to:** ${model.objective():.2f}$\"))"
    ]
   },
@@ -241,17 +249,59 @@
     }
    ],
    "source": [
-    "gamma_values = [0.005, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.20, 0.25, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 4.75, 5, 5.25, 5.5]\n",
+    "gamma_values = [\n",
+    "    0.005,\n",
+    "    0.01,\n",
+    "    0.02,\n",
+    "    0.03,\n",
+    "    0.04,\n",
+    "    0.05,\n",
+    "    0.06,\n",
+    "    0.07,\n",
+    "    0.08,\n",
+    "    0.09,\n",
+    "    0.1,\n",
+    "    0.11,\n",
+    "    0.12,\n",
+    "    0.13,\n",
+    "    0.14,\n",
+    "    0.15,\n",
+    "    0.16,\n",
+    "    0.17,\n",
+    "    0.18,\n",
+    "    0.19,\n",
+    "    0.20,\n",
+    "    0.25,\n",
+    "    0.3,\n",
+    "    0.4,\n",
+    "    0.5,\n",
+    "    0.6,\n",
+    "    0.7,\n",
+    "    0.8,\n",
+    "    0.9,\n",
+    "    1,\n",
+    "    1.5,\n",
+    "    2,\n",
+    "    2.5,\n",
+    "    3,\n",
+    "    3.5,\n",
+    "    4,\n",
+    "    4.5,\n",
+    "    4.75,\n",
+    "    5,\n",
+    "    5.25,\n",
+    "    5.5,\n",
+    "]\n",
     "objective = []\n",
     "\n",
-    "plt.rcParams.update({'font.size': 14})\n",
+    "plt.rcParams.update({\"font.size\": 14})\n",
     "for gamma in gamma_values:\n",
     "    _, model = markowitz(gamma, mu, Sigma)\n",
-    "    objective.append(round(model.objective(),3))\n",
+    "    objective.append(round(model.objective(), 3))\n",
     "\n",
     "plt.plot(gamma_values, objective, color=plt.cm.tab20c(0))\n",
-    "plt.xlabel(r'Risk threshold $\\gamma$')\n",
-    "plt.ylabel('Optimal objective value')\n",
+    "plt.xlabel(r\"Risk threshold $\\gamma$\")\n",
+    "plt.ylabel(\"Optimal objective value\")\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
diff --git a/_sources/notebooks/05/milk-pooling.ipynb b/_sources/notebooks/05/milk-pooling.ipynb
index 895ae2f8..57dc3ac6 100644
--- a/_sources/notebooks/05/milk-pooling.ipynb
+++ b/_sources/notebooks/05/milk-pooling.ipynb
@@ -17,13 +17,13 @@
     "```\n",
     "```{index} single: solver; cbc\n",
     "```\n",
-    "```{index} single: solver; couenne\n",
+    "```{index} single: solver; couenne, ipopt\n",
     "```\n",
     "# Milk pooling and blending\n",
     "\n",
     "Pooling and blending operations involve the \"pooling\" of various streams to create intermediate mixtures that are subsequently blended with other streams to meet final product specifications. These operations are common to the chemical processing and petroleum sectors where limited tankage may be available, or when it is necessary to transport materials by train, truck, or pipeline to remote blending terminals. Similar applications arise in agriculture, food, mining, wastewater treatment, and other industries.\n",
     "\n",
-    "This notebook considers a simple example of a wholesale milk distributor to show how **non-convexity** arises in the optimization of pooling and blending operations. Non-convexity is due to presence of **bilinear** terms that are the product of two decision variables where one is a scale-dependent **extensive** quantity measuring the amount or flow of a product, and the other is scale-independent **intensive** quantity such as product composition. The notebook then shows how to develop and solve a convex approximation of the problem, and finally demonstrates solution the use of [Couenne](https://github.com/coin-or/Couenne), a solver specifically designed to find global solutions to mixed integer nonlinear optimization (MINLO) problems."
+    "This notebook considers a simple example of a wholesale milk distributor to show how **non-convexity** arises in the optimization of pooling and blending operations. Non-convexity is due to presence of **bilinear** terms that are the product of two decision variables where one is a scale-dependent **extensive** quantity measuring the amount or flow of a product, and the other is scale-independent **intensive** quantity such as product composition. The notebook then shows how to develop and solve a convex approximation of the problem, and finally demonstrates solution the use of `ipotp`, a solver specifically designed to find global solutions to nonlinear optimization (NLO) problems."
    ]
   },
   {
@@ -35,17 +35,22 @@
    },
    "outputs": [],
    "source": [
-    "# install Pyomo and solvers\n",
-    "import sys\n",
-    "import os\n",
-    "\n",
-    "SOLVER_LO = \"cbc\"\n",
-    "SOLVER_GNLO = \"couenne\"\n",
-    "\n",
+    "import sys, os\n",
+    " \n",
     "if 'google.colab' in sys.modules:\n",
     "    !pip install idaes-pse --pre >/dev/null 2>/dev/null\n",
     "    !idaes get-extensions --to ./bin \n",
-    "    os.environ['PATH'] += ':bin'\n"
+    "    os.environ['PATH'] += ':bin'\n",
+    "\n",
+    "import pyomo.environ as pyo\n",
+    "\n",
+    "solver_LO = 'appsi_highs'\n",
+    "SOLVER_LO = pyo.SolverFactory(solver_LO)\n",
+    "assert SOLVER_LO.available(), f\"Solver {solver_LO} is not available.\"\n",
+    "\n",
+    "solver_NLO = \"ipopt\"\n",
+    "SOLVER_NLO = pyo.SolverFactory(solver_NLO)\n",
+    "assert SOLVER_NLO.available(), f\"Solver {solver_NLO} is not available.\""
    ]
   },
   {
@@ -68,19 +73,19 @@
    "id": "c5123315-1824-4c68-a345-aee738d187d1",
    "metadata": {},
    "source": [
-    "## Problem: Pooling milk for wholesale blending and distribution\n",
+    "## Problem description: Pooling milk for wholesale blending and distribution\n",
     "\n",
-    "A bulk distributor supplies custom blends of milk to several customers. Each customer has specified a minimum fat content, a maximum price, and a maximum amount for the milk they wish to buy. The distributor sources raw milk from local farms. Each farm produces a milk with a known fat content and cost. \n",
+    "A bulk distributor supplies custom milk blends to several customers. Each customer has specified a minimum fat content, a maximum price, and a maximum amount of milk they wish to buy. The distributor sources raw milk from local farms. Each farm produces a milk with a known fat content and cost. \n",
     "\n",
-    "The distributor has recently identified more affordable sources raw milk from several distant farms. These distant farms produce milk grades that can be blend with milk from the local farms. However, the distributor only has one truck with a single tank available for transporting milk from the distant farms. As a result, milk from the distant farms must be combined in the tank before being transported to the blending station. This creates a \"pool\" of uniform composition which to be blended with local milk to meet customer requirements.\n",
+    "The distributor has recently identified more affordable sources raw milk from some remote farms. These remote farms produce milk grades that can be blended with milk from local farms. However, the distributor only has one truck with a single tank available to transport milk from the remote farms. As a result, milk from the remote farms must be mixed in the tank before being transported to the blending station. This creates a \"pool\" of uniform composition which to be blended with local milk to meet the customer's requirements.\n",
     "\n",
-    "The process is shown in the following diagram. The fat content and cost of raw milk is given for each farm. For each customer, data is given for the required milk fat content, price, and the maximum demand. The arrows indicate pooling and blending of raw milk supplies. Each arrow is labeled with the an amount of raw milk.\n",
+    "The process is shown in the following diagram. The fat content and cost of raw milk is given for each farm. For each customer, data is provided for the required milk fat content, price, and the maximum demand. Arrows indicate pooling and blending of raw milk supplies. Each arrow is labeled with the amount of raw milk.\n",
     "\n",
     "![](milk-pooling.png)\n",
     "\n",
     "What should the distributor do?\n",
     "\n",
-    "* Option 1. Do nothing. Continue operating the business as usual with local suppliers.\n",
+    "* Option 1. Do nothing and continue operating the business as usual with local suppliers.\n",
     "\n",
     "* Option 2. Buy a second truck to transport raw milk from the remote farms to the blending facility without pooling.\n",
     "\n",
@@ -238,21 +243,25 @@
     }
    ],
    "source": [
-    "customers = pd.DataFrame({\n",
-    "    \"Customer 1\": {\"min_fat\": 0.045, \"price\": 52.0, \"demand\": 6000.0},\n",
-    "    \"Customer 2\": {\"min_fat\": 0.030, \"price\": 48.0, \"demand\": 2500.0},\n",
-    "    \"Customer 3\": {\"min_fat\": 0.040, \"price\": 50.0, \"demand\": 4000.0},\n",
-    "}).T\n",
-    "\n",
-    "suppliers = pd.DataFrame({\n",
-    "    \"Farm A\": {\"fat\": 0.045, \"cost\": 45.0, \"location\": \"local\"},\n",
-    "    \"Farm B\": {\"fat\": 0.030, \"cost\": 42.0, \"location\": \"local\"},\n",
-    "    \"Farm C\": {\"fat\": 0.033, \"cost\": 37.0, \"location\": \"remote\"},\n",
-    "    \"Farm D\": {\"fat\": 0.050, \"cost\": 45.0, \"location\": \"remote\"}},\n",
-    "    ).T\n",
-    "\n",
-    "local_suppliers = suppliers[suppliers[\"location\"]==\"local\"]\n",
-    "remote_suppliers = suppliers[suppliers[\"location\"]==\"remote\"]\n",
+    "customers = pd.DataFrame(\n",
+    "    {\n",
+    "        \"Customer 1\": {\"min_fat\": 0.045, \"price\": 52.0, \"demand\": 6000.0},\n",
+    "        \"Customer 2\": {\"min_fat\": 0.030, \"price\": 48.0, \"demand\": 2500.0},\n",
+    "        \"Customer 3\": {\"min_fat\": 0.040, \"price\": 50.0, \"demand\": 4000.0},\n",
+    "    }\n",
+    ").T\n",
+    "\n",
+    "suppliers = pd.DataFrame(\n",
+    "    {\n",
+    "        \"Farm A\": {\"fat\": 0.045, \"cost\": 45.0, \"location\": \"local\"},\n",
+    "        \"Farm B\": {\"fat\": 0.030, \"cost\": 42.0, \"location\": \"local\"},\n",
+    "        \"Farm C\": {\"fat\": 0.033, \"cost\": 37.0, \"location\": \"remote\"},\n",
+    "        \"Farm D\": {\"fat\": 0.050, \"cost\": 45.0, \"location\": \"remote\"},\n",
+    "    },\n",
+    ").T\n",
+    "\n",
+    "local_suppliers = suppliers[suppliers[\"location\"] == \"local\"]\n",
+    "remote_suppliers = suppliers[suppliers[\"location\"] == \"remote\"]\n",
     "\n",
     "print(\"\\nCustomers\")\n",
     "display(customers)\n",
@@ -268,7 +277,7 @@
    "source": [
     "## Option 1. Business as usual\n",
     "\n",
-    "The normal business of the milk distributor is to blend supplies from local farms to meet customer requirements. Let $L$ designate the set of local suppliers, and let $C$ designate the set of customers. Decision variable $z_{l, c}$ is the amount of milk from local supplier $l\\in L$ that is mixed into the blend sold to customer $c\\in C$.\n",
+    "The normal business of the milk distributor is to blend supplies from local farms to meet customer requirements. Let $L = \\{ A, B \\}$ designate the set of local suppliers, and let $C = \\{1,2,3\\}$ designate the set of customers. Decision variable $z_{l, c}$ is the amount of milk from local supplier $l\\in L$ that is mixed into the blend sold to customer $c\\in C$.\n",
     "\n",
     "The distributor's objectives is to maximize profit\n",
     "\n",
@@ -288,11 +297,11 @@
     "\\end{align*}\n",
     "$$\n",
     "\n",
-    "Let $\\text{fat}_l$ denote the fat content of the raw milke produced by farm $l$, and let  $\\text{min_fat}_c$ denote the minimum fat content required by customer $c$, respectively. Assuming linear blending, the model becomes\n",
+    "Let $\\text{fat}_l$ denote the fat content of the raw milk produced by farm $l$, and let $\\text{fat}^{\\min}_c$ denote the minimum fat content required by customer $c$, respectively. Assuming linear blending, the model becomes\n",
     "\n",
     "$$\n",
     "\\begin{align*}\n",
-    "\\sum_{(l,c)\\ \\in\\ L \\times C} \\text{fat}_{l} z_{l,c} & \\geq \\text{min_fat}_{c} \\sum_{l\\in L} z_{l, c} & \\forall c \\in C\n",
+    "\\sum_{(l,c)\\ \\in\\ L \\times C} \\text{fat}_{l} \\cdot z_{l,c} & \\geq \\text{fat}^{\\min}_{c} \\sum_{l\\in L} z_{l, c} & \\forall c \\in C\n",
     "\\end{align*}\n",
     "$$\n",
     "\n",
@@ -313,8 +322,7 @@
      "text": [
       "\n",
       "Profit = 81000.00\n",
-      "\n",
-      "Blending Plan\n"
+      "\n"
      ]
     },
     {
@@ -407,41 +415,55 @@
     }
    ],
    "source": [
-    "m = pyo.ConcreteModel()\n",
+    "m = pyo.ConcreteModel(\"Milk pooling v1\")\n",
     "\n",
     "# define sources and customers\n",
-    "m.L = pyo.Set(initialize=suppliers.index[suppliers[\"location\"]==\"local\"].tolist())\n",
+    "m.L = pyo.Set(initialize=suppliers.index[suppliers[\"location\"] == \"local\"].tolist())\n",
     "m.C = pyo.Set(initialize=customers.index.tolist())\n",
     "\n",
     "# define local -> customer flowrates\n",
     "m.z = pyo.Var(m.L * m.C, domain=pyo.NonNegativeReals)\n",
     "\n",
+    "\n",
     "@m.Objective(sense=pyo.maximize)\n",
     "def profit(m):\n",
-    "    return sum(m.z[l, c]*(customers.loc[c, \"price\"] - suppliers.loc[l, \"cost\"]) for l, c in m.L * m.C)\n",
+    "    return sum(\n",
+    "        m.z[l, c] * (customers.loc[c, \"price\"] - suppliers.loc[l, \"cost\"])\n",
+    "        for l, c in m.L * m.C\n",
+    "    )\n",
+    "\n",
     "\n",
     "@m.Constraint(m.C)\n",
     "def demand(m, c):\n",
     "    return sum(m.z[l, c] for l in m.L) <= customers.loc[c, \"demand\"]\n",
     "\n",
+    "\n",
     "@m.Constraint(m.C)\n",
     "def fat_content(m, c):\n",
-    "    return sum(m.z[l, c] * suppliers.loc[l, \"fat\"] for l in m.L) >= sum(m.z[l, c] for l in m.L) * customers.loc[c, \"min_fat\"]\n",
+    "    return (\n",
+    "        sum(m.z[l, c] * suppliers.loc[l, \"fat\"] for l in m.L)\n",
+    "        >= sum(m.z[l, c] for l in m.L) * customers.loc[c, \"min_fat\"]\n",
+    "    )\n",
+    "\n",
     "\n",
-    "pyo.SolverFactory(SOLVER_LO).solve(m)\n",
+    "SOLVER_LO.solve(m)\n",
     "\n",
-    "# report results\n",
     "print(f\"\\nProfit = {m.profit():0.2f}\\n\")\n",
     "\n",
-    "# create dataframe of results\n",
-    "print(\"Blending Plan\")\n",
-    "Z = pd.DataFrame([[l, c, round(m.z[l, c](), 1)] for l, c in m.L * m.C], columns = [\"supplier\", \"customer\", \"\"])\n",
+    "Z = pd.DataFrame(\n",
+    "    [[l, c, round(m.z[l, c](), 1)] for l, c in m.L * m.C],\n",
+    "    columns=[\"supplier\", \"customer\", \"\"],\n",
+    ")\n",
     "Z = Z.pivot_table(index=\"customer\", columns=\"supplier\")\n",
     "Z[\"Total\"] = Z.sum(axis=1)\n",
-    "Z[\"fat content\"] = [sum(m.z[l, c]() * suppliers.loc[l, \"fat\"] for l in m.L)/sum(m.z[l, c]() for l in m.L) for c in m.C]\n",
+    "Z[\"fat content\"] = [\n",
+    "    sum(m.z[l, c]() * suppliers.loc[l, \"fat\"] for l in m.L)\n",
+    "    / sum(m.z[l, c]() for l in m.L)\n",
+    "    for c in m.C\n",
+    "]\n",
     "Z[\"min_fat\"] = customers[\"min_fat\"]\n",
     "\n",
-    "display(Z)\n"
+    "display(Z)"
    ]
   },
   {
@@ -451,7 +473,7 @@
    "source": [
     "## Option 2. Buy an additional truck\n",
     "\n",
-    "The distributor can earn a profit of 81,000 using only local suppliers. Is is possible earn a higher profit by also sourcing raw milk from the remote suppliers?\n",
+    "As shown before, the distributor can earn a profit of 81,000 using only local suppliers. It is possible to earn a higher profit by also sourcing raw milk from the remote suppliers?\n",
     "\n",
     "Before considering pooling, the distributor may wish to know the maximum profit possible if raw milk from the remote suppliers could be blended just like local suppliers. This would require acquiring and operating a separate transport truck for each remote supplier, and is worth knowing if the additional profit would justify the additional expense.\n",
     "\n",
@@ -575,7 +597,7 @@
     }
    ],
    "source": [
-    "m = pyo.ConcreteModel()\n",
+    "m = pyo.ConcreteModel(\"Milk pooling v2 (additional truck)\")\n",
     "\n",
     "# define sources and customers\n",
     "m.S = pyo.Set(initialize=suppliers.index.tolist())\n",
@@ -584,28 +606,43 @@
     "# define local -> customer flowrates\n",
     "m.z = pyo.Var(m.S * m.C, domain=pyo.NonNegativeReals)\n",
     "\n",
+    "\n",
     "@m.Objective(sense=pyo.maximize)\n",
     "def profit(m):\n",
-    "    return sum(m.z[s, c]*(customers.loc[c, \"price\"] - suppliers.loc[s, \"cost\"]) for s, c in m.S * m.C)\n",
+    "    return sum(\n",
+    "        m.z[s, c] * (customers.loc[c, \"price\"] - suppliers.loc[s, \"cost\"])\n",
+    "        for s, c in m.S * m.C\n",
+    "    )\n",
+    "\n",
     "\n",
     "@m.Constraint(m.C)\n",
     "def demand(m, c):\n",
     "    return sum(m.z[s, c] for s in m.S) <= customers.loc[c, \"demand\"]\n",
     "\n",
+    "\n",
     "@m.Constraint(m.C)\n",
     "def quality(m, c):\n",
-    "    return sum(m.z[s, c] * suppliers.loc[s, \"fat\"] for s in m.S) >= sum(m.z[s, c] for s in m.S) * customers.loc[c, \"min_fat\"]\n",
+    "    return (\n",
+    "        sum(m.z[s, c] * suppliers.loc[s, \"fat\"] for s in m.S)\n",
+    "        >= sum(m.z[s, c] for s in m.S) * customers.loc[c, \"min_fat\"]\n",
+    "    )\n",
+    "\n",
     "\n",
-    "pyo.SolverFactory(SOLVER_LO).solve(m)\n",
+    "SOLVER_LO.solve(m)\n",
     "\n",
-    "# report results\n",
     "print(f\"\\nProfit = {m.profit():0.2f}\\n\")\n",
     "\n",
-    "# create dataframe of results\n",
-    "Z = pd.DataFrame([[s, c, round(m.z[s, c](), 1)] for s, c in m.S * m.C], columns = [\"supplier\", \"customer\", \"\"])\n",
+    "Z = pd.DataFrame(\n",
+    "    [[s, c, round(m.z[s, c](), 1)] for s, c in m.S * m.C],\n",
+    "    columns=[\"supplier\", \"customer\", \"\"],\n",
+    ")\n",
     "Z = Z.pivot_table(index=\"customer\", columns=\"supplier\")\n",
     "Z[\"Total\"] = Z.sum(axis=1)\n",
-    "Z[\"fat content\"] = [sum(m.z[s, c]() * suppliers.loc[s, \"fat\"] for s in m.S)/sum(m.z[s, c]() for s in m.S) for c in m.C]\n",
+    "Z[\"fat content\"] = [\n",
+    "    sum(m.z[s, c]() * suppliers.loc[s, \"fat\"] for s in m.S)\n",
+    "    / sum(m.z[s, c]() for s in m.S)\n",
+    "    for c in m.C\n",
+    "]\n",
     "Z[\"min_fat\"] = customers[\"min_fat\"]\n",
     "\n",
     "display(Z)"
@@ -632,7 +669,7 @@
     "\n",
     "### Pooling problem\n",
     "\n",
-    "There are a several mathematical formulations of pooling problem in the academic literature. The formulation used here is called the \"p-parameterization\" where the pool composition represents a new decision variable $p$. The other additional decision variables are $x_r$ referring to the amount of raw milk purchased from remote supplier $r\\in R$, and $y_c$ which is the amount of the pooled milk included in the blend delivered to customer $c\\in C$.\n",
+    "There are several mathematical formulations of pooling problem in the academic literature. The formulation used here is called the **$p$-parameterization** where the pool composition represents a new decision variable $p$. The other additional decision variables are $x_r$ referring to the amount of raw milk purchased from remote supplier $r\\in R$, and $y_c$ which is the amount of the pooled milk included in the blend delivered to customer $c\\in C$.\n",
     "\n",
     "The profit objective is the difference between the income received for selling blended products and the cost of purchasing raw milk from local and remote suppliers.\n",
     "\n",
@@ -663,7 +700,7 @@
     "\n",
     "$$\n",
     "\\begin{align*}\n",
-    "\\sum_{r\\in R}\\text{fat}_{r}\\ x_{r}  & = \\underbrace{p \\sum_{c\\in C} y_{c}}_{\\text{bilinear}}\n",
+    "\\sum_{r\\in R}\\text{fat}_{r}\\cdot x_{r}  & = \\underbrace{p \\sum_{c\\in C} y_{c}}_{\\text{bilinear}}\n",
     "\\end{align*}\n",
     "$$\n",
     "\n",
@@ -671,8 +708,8 @@
     "\n",
     "$$\n",
     "\\begin{align*}\n",
-    "\\underbrace{p y_{c}}_{\\text{bilinear}}  + \\sum_{(l,c)\\ \\in\\ L \\times C} \\text{fat}_{l}\\ z_{l,c}\n",
-    "& \\geq \\text{min_fat}_{c}\\ (\\sum_{l\\in L} z_{l, c} + y_{c})\n",
+    "\\underbrace{p y_{c}}_{\\text{bilinear}}  + \\sum_{(l,c)\\ \\in\\ L \\times C} \\text{fat}_{l}\\cdot z_{l,c}\n",
+    "& \\geq \\text{fat}^{\\min}_c \\ (\\sum_{l\\in L} z_{l, c} + y_{c})\n",
     "& \\forall c \\in C\n",
     "\\end{align*}\n",
     "$$\n",
@@ -689,17 +726,18 @@
     "\n",
     "$$\n",
     "\\begin{align*}\n",
-    "\\max\\ \\text{Profit} = & \\sum_{(l,c)\\ \\in\\ L \\times C} (\\text{price}_c - \\text{cost}_l)\\ z_{l,c}\n",
+    "\\max \\quad & \\sum_{(l,c)\\ \\in\\ L \\times C} (\\text{price}_c - \\text{cost}_l)\\ z_{l,c}\n",
     "+ \\sum_{c\\in C} \\text{price}_c y_{c} - \\sum_{r\\in R} \\text{cost}_r x_{r} \\\\\n",
-    "\\text{s.t.}\\qquad & \\sum_{l\\in L} z_{l, c} + y_{c} \\leq \\text{demand}_{c} & \\forall c\\in C \\\\\n",
+    "\\text{s.t.}\\quad \n",
+    "& \\sum_{l\\in L} z_{l, c} + y_{c} \\leq \\text{demand}_{c} & \\forall c\\in C \\\\\n",
     "& \\sum_{r\\in R}x_{r} = \\sum_{c\\in C} y_{c} \\\\\n",
-    "& \\underbrace{p y_{c}}_{\\text{bilinear}}  + \\sum_{(l,c)\\ \\in\\ L \\times C} \\text{fat}_{l}\\ z_{l,c} \\geq \\text{min_fat}_{c}\\ (\\sum_{l\\in L} z_{l, c} + y_{c}) & \\forall c \\in C \\\\\n",
-    "& \\sum_{r\\in R}\\text{fat}_{r}\\ x_{r} = \\underbrace{p \\sum_{c\\in C} y_{c}}_{\\text{bilinear}} \\\\\n",
-    "& p, x_r, y_c, z_{l, c} \\geq 0 & \\forall r\\in R, c\\in C, l\\in L \n",
+    "& \\underbrace{p y_{c}}_{\\text{bilinear}}  + \\sum_{(l,c)\\ \\in\\ L \\times C} \\text{fat}_{l}\\cdot z_{l,c} \\geq \\text{fat}^{\\min}_c\\ (\\sum_{l\\in L} z_{l, c} + y_{c}) & \\forall c \\in C \\\\\n",
+    "& \\sum_{r\\in R}\\text{fat}_{r}\\cdot x_{r} = \\underbrace{p \\sum_{c\\in C} y_{c}}_{\\text{bilinear}} \\\\\n",
+    "& p, x_r, y_c, z_{l, c} \\geq 0 & \\forall r\\in R, c\\in C, l\\in L. \n",
     "\\end{align*}\n",
     "$$\n",
     "\n",
-    "Before attempting a solution to this problem, let's first consider the implications of the bilinear terms."
+    "Before attempting a solution to this problem, let us first consider the implications of the bilinear terms."
    ]
   },
   {
@@ -709,7 +747,7 @@
    "source": [
     "### Why are bilinear problems hard?\n",
     "\n",
-    "Bilinearity has a profound consequence on the nature of the optimization problem. To demonstrate this point, we consider a function obtained by fixing $p$ in the milk pooling problem and solving the resulting linear optimization problem for the maximum profit $f(p)$."
+    "Bilinearity has a profound consequence on the nature of the optimization problem. To explore this, we treat $p$ as a parameter and repeatedly solve the resulting linear problem for different values of $p$. "
    ]
   },
   {
@@ -721,14 +759,14 @@
    },
    "outputs": [],
    "source": [
-    "# solve milk pooling problem for a fixed pool composition p\n",
-    "def f(p):\n",
-    "    \n",
-    "    m = pyo.ConcreteModel('Milk Pooling Model')\n",
+    "def milk_pooling_bilinear(p):\n",
+    "    m = pyo.ConcreteModel(\"Milk pooling v3 (Bilinear formulation)\")\n",
     "\n",
-    "    # define sources \n",
-    "    m.L = pyo.Set(initialize=suppliers.index[suppliers[\"location\"]==\"local\"].tolist())\n",
-    "    m.R = pyo.Set(initialize=suppliers.index[suppliers[\"location\"]==\"remote\"].tolist())\n",
+    "    # define sources\n",
+    "    m.L = pyo.Set(initialize=suppliers.index[suppliers[\"location\"] == \"local\"].tolist())\n",
+    "    m.R = pyo.Set(\n",
+    "        initialize=suppliers.index[suppliers[\"location\"] == \"remote\"].tolist()\n",
+    "    )\n",
     "\n",
     "    # define customers\n",
     "    m.C = pyo.Set(initialize=customers.index.tolist())\n",
@@ -737,34 +775,44 @@
     "    m.x = pyo.Var(m.R, domain=pyo.NonNegativeReals)\n",
     "    m.y = pyo.Var(m.C, domain=pyo.NonNegativeReals)\n",
     "    m.z = pyo.Var(m.L * m.C, domain=pyo.NonNegativeReals)\n",
-    "    \n",
+    "\n",
     "    m.p = pyo.Param(default=p)\n",
     "\n",
     "    @m.Objective(sense=pyo.maximize)\n",
     "    def profit(m):\n",
-    "        return + sum(m.z[l, c]*(customers.loc[c, \"price\"] - suppliers.loc[l, \"cost\"]) for l, c in m.L * m.C) \\\n",
-    "               + sum(m.y[c]*customers.loc[c, \"price\"] for c in m.C) \\\n",
-    "               - sum(m.x[r]*suppliers.loc[r, \"cost\"] for r in m.R)\n",
+    "        return (\n",
+    "            +sum(\n",
+    "                m.z[l, c] * (customers.loc[c, \"price\"] - suppliers.loc[l, \"cost\"])\n",
+    "                for l, c in m.L * m.C\n",
+    "            )\n",
+    "            + sum(m.y[c] * customers.loc[c, \"price\"] for c in m.C)\n",
+    "            - sum(m.x[r] * suppliers.loc[r, \"cost\"] for r in m.R)\n",
+    "        )\n",
     "\n",
     "    @m.Constraint(m.C)\n",
     "    def customer_demand(m, c):\n",
     "        return sum(m.z[l, c] for l in m.L) + m.y[c] <= customers.loc[c, \"demand\"]\n",
-    "    \n",
+    "\n",
     "    @m.Constraint()\n",
-    "    def pool_balance(m,):\n",
+    "    def pool_balance(\n",
+    "        m,\n",
+    "    ):\n",
     "        return sum(m.x[r] for r in m.R) == sum(m.y[c] for c in m.C)\n",
-    "    \n",
+    "\n",
     "    @m.Constraint()\n",
     "    def pool_quality(m):\n",
-    "        return sum(suppliers.loc[r, \"fat\"] * m.x[r] for r in m.R) == m.p * sum(m.x[r] for r in m.R)\n",
-    "    \n",
+    "        return sum(suppliers.loc[r, \"fat\"] * m.x[r] for r in m.R) == m.p * sum(\n",
+    "            m.x[r] for r in m.R\n",
+    "        )\n",
+    "\n",
     "    @m.Constraint(m.C)\n",
     "    def customer_quality(m, c):\n",
-    "        return m.p * m.y[c] + sum(suppliers.loc[l, \"fat\"] * m.z[l, c] for l in m.L) \\\n",
-    "                 >= customers.loc[c, \"min_fat\"] * (sum(m.z[l, c] for l in m.L) + m.y[c])\n",
+    "        return m.p * m.y[c] + sum(\n",
+    "            suppliers.loc[l, \"fat\"] * m.z[l, c] for l in m.L\n",
+    "        ) >= customers.loc[c, \"min_fat\"] * (sum(m.z[l, c] for l in m.L) + m.y[c])\n",
+    "\n",
+    "    SOLVER_LO.solve(m)\n",
     "\n",
-    "    pyo.SolverFactory(SOLVER_LO).solve(m)\n",
-    "    \n",
     "    return m"
    ]
   },
@@ -778,9 +826,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 800x300 with 1 Axes>"
+       "<Figure size 1000x500 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -788,15 +836,16 @@
     }
    ],
    "source": [
-    "p_plot = np.linspace(0.025, 0.055, 200)\n",
-    "f_plot = [f(p).profit() for p in p_plot]\n",
+    "p_plot = np.linspace(0.025, 0.06, 200)\n",
+    "f_plot = [milk_pooling_bilinear(p).profit() for p in p_plot]\n",
     "\n",
-    "fig, ax = plt.subplots(figsize=(8, 3))\n",
-    "ax.plot(p_plot, f_plot)\n",
-    "ax.set_title(\"Milk Pooling\")\n",
+    "fig, ax = plt.subplots(figsize=(10, 5))\n",
+    "ax.plot(p_plot, f_plot, lw=2)\n",
     "ax.set_xlabel(\"Pool composition p\")\n",
-    "ax.set_ylabel(\"Profit f\")\n",
+    "ax.set_ylabel(\"Profit\")\n",
     "ax.grid(True)\n",
+    "plt.rcParams.update({\"font.size\": 14})\n",
+    "plt.tight_layout()\n",
     "plt.show()"
    ]
   },
@@ -823,7 +872,7 @@
     "+ \\sum_{c\\in C} \\text{price}_c y_{c} - \\sum_{r\\in R} \\text{cost}_r x_{r} \\\\\n",
     "\\text{s.t.}\\qquad & \\sum_{l\\in L} z_{l, c} + y_{c} \\leq \\text{demand}_{c} & \\forall c\\in C \\\\\n",
     "& \\sum_{r\\in R}x_{r} = \\sum_{c\\in C} y_{c} \\\\\n",
-    "& w_c + \\sum_{(l,c)\\ \\in\\ L \\times C} \\text{fat}_{l}\\ z_{l,c} \\geq \\text{min_fat}_{c}\\ (\\sum_{l\\in L} z_{l, c} + y_{c}) & \\forall c \\in C \\\\\n",
+    "& w_c + \\sum_{(l,c)\\ \\in\\ L \\times C} \\text{fat}_{l}\\ z_{l,c} \\geq \\text{fat}^{\\min}_c \\ (\\sum_{l\\in L} z_{l, c} + y_{c}) & \\forall c \\in C \\\\\n",
     "& \\sum_{r\\in R}\\text{fat}_{r}\\ x_{r} = \\underbrace{p \\sum_{c\\in C} y_{c}}_{\\text{bilinear}} \\\\\n",
     "& w_c, x_r, y_c, z_{l, c} \\geq 0 & \\forall r\\in R, c\\in C, l\\in L \n",
     "\\end{align*}\n",
@@ -880,12 +929,13 @@
    "outputs": [],
    "source": [
     "def milk_pooling_convex():\n",
-    "    \n",
-    "    m = pyo.ConcreteModel('Milk Pooling Model - Convex Approximation')\n",
+    "    m = pyo.ConcreteModel(\"Milk pooling v3 (Convex approximation)\")\n",
     "\n",
-    "    # define sources \n",
-    "    m.L = pyo.Set(initialize=suppliers.index[suppliers[\"location\"]==\"local\"].tolist())\n",
-    "    m.R = pyo.Set(initialize=suppliers.index[suppliers[\"location\"]==\"remote\"].tolist())\n",
+    "    # define sources\n",
+    "    m.L = pyo.Set(initialize=suppliers.index[suppliers[\"location\"] == \"local\"].tolist())\n",
+    "    m.R = pyo.Set(\n",
+    "        initialize=suppliers.index[suppliers[\"location\"] == \"remote\"].tolist()\n",
+    "    )\n",
     "\n",
     "    # define customers\n",
     "    m.C = pyo.Set(initialize=customers.index.tolist())\n",
@@ -894,47 +944,71 @@
     "    m.x = pyo.Var(m.R, domain=pyo.NonNegativeReals)\n",
     "    m.y = pyo.Var(m.C, bounds=lambda m, c: (0, customers.loc[c, \"demand\"]))\n",
     "    m.z = pyo.Var(m.L * m.C, domain=pyo.NonNegativeReals)\n",
-    "    \n",
+    "\n",
     "    # composition of the pool\n",
-    "    m.p = pyo.Var(bounds=(suppliers.loc[m.R, \"fat\"].min(), suppliers.loc[m.R, \"fat\"].max()))\n",
-    "    \n",
+    "    m.p = pyo.Var(\n",
+    "        bounds=(suppliers.loc[m.R, \"fat\"].min(), suppliers.loc[m.R, \"fat\"].max())\n",
+    "    )\n",
+    "\n",
     "    # w[c] to replace bilinear terms p * y[c]\n",
     "    m.w = pyo.Var(m.C, domain=pyo.NonNegativeReals)\n",
     "\n",
     "    @m.Objective(sense=pyo.maximize)\n",
     "    def profit(m):\n",
-    "        return + sum(m.z[l, c]*(customers.loc[c, \"price\"] - suppliers.loc[l, \"cost\"]) for l, c in m.L * m.C) \\\n",
-    "               + sum(m.y[c]*customers.loc[c, \"price\"] for c in m.C) \\\n",
-    "               - sum(m.x[r]*suppliers.loc[r, \"cost\"] for r in m.R)\n",
-    "    \n",
+    "        return (\n",
+    "            +sum(\n",
+    "                m.z[l, c] * (customers.loc[c, \"price\"] - suppliers.loc[l, \"cost\"])\n",
+    "                for l, c in m.L * m.C\n",
+    "            )\n",
+    "            + sum(m.y[c] * customers.loc[c, \"price\"] for c in m.C)\n",
+    "            - sum(m.x[r] * suppliers.loc[r, \"cost\"] for r in m.R)\n",
+    "        )\n",
+    "\n",
     "    # create a block of constraints for every customer\n",
     "    @m.Block(m.C)\n",
     "    def mccormick(b, c):\n",
     "        m = b.model()\n",
-    "        b.ll = pyo.Constraint(expr=m.w[c] >= m.y[c].lower * m.p + m.p.lower * m.y[c] - m.p.lower * m.y[c].lower)\n",
-    "        b.hh = pyo.Constraint(expr=m.w[c] >= m.y[c].upper * m.p + m.p.upper * m.y[c] - m.p.upper * m.y[c].upper)\n",
-    "        b.lh = pyo.Constraint(expr=m.w[c] <= m.y[c].upper * m.p + m.p.lower * m.y[c] - m.p.lower * m.y[c].upper)\n",
-    "        b.hl = pyo.Constraint(expr=m.w[c] <= m.y[c].lower * m.p + m.p.upper * m.y[c] - m.p.upper * m.y[c].lower)  \n",
+    "        b.ll = pyo.Constraint(\n",
+    "            expr=m.w[c]\n",
+    "            >= m.y[c].lower * m.p + m.p.lower * m.y[c] - m.p.lower * m.y[c].lower\n",
+    "        )\n",
+    "        b.hh = pyo.Constraint(\n",
+    "            expr=m.w[c]\n",
+    "            >= m.y[c].upper * m.p + m.p.upper * m.y[c] - m.p.upper * m.y[c].upper\n",
+    "        )\n",
+    "        b.lh = pyo.Constraint(\n",
+    "            expr=m.w[c]\n",
+    "            <= m.y[c].upper * m.p + m.p.lower * m.y[c] - m.p.lower * m.y[c].upper\n",
+    "        )\n",
+    "        b.hl = pyo.Constraint(\n",
+    "            expr=m.w[c]\n",
+    "            <= m.y[c].lower * m.p + m.p.upper * m.y[c] - m.p.upper * m.y[c].lower\n",
+    "        )\n",
     "\n",
     "    @m.Constraint(m.C)\n",
     "    def customer_demand(m, c):\n",
     "        return sum(m.z[l, c] for l in m.L) + m.y[c] <= customers.loc[c, \"demand\"]\n",
-    "    \n",
+    "\n",
     "    @m.Constraint()\n",
-    "    def pool_balance(m,):\n",
+    "    def pool_balance(\n",
+    "        m,\n",
+    "    ):\n",
     "        return sum(m.x[r] for r in m.R) == sum(m.y[c] for c in m.C)\n",
-    "    \n",
+    "\n",
     "    @m.Constraint()\n",
     "    def pool_quality(m):\n",
-    "        return sum(suppliers.loc[r, \"fat\"] * m.x[r] for r in m.R) == sum(m.w[c] for c in m.C)\n",
-    "    \n",
+    "        return sum(suppliers.loc[r, \"fat\"] * m.x[r] for r in m.R) == sum(\n",
+    "            m.w[c] for c in m.C\n",
+    "        )\n",
+    "\n",
     "    @m.Constraint(m.C)\n",
     "    def customer_quality(m, c):\n",
-    "        return m.w[c] + sum(suppliers.loc[l, \"fat\"] * m.z[l, c] for l in m.L) \\\n",
-    "                 >= customers.loc[c, \"min_fat\"] * (sum(m.z[l, c] for l in m.L) + m.y[c])\n",
+    "        return m.w[c] + sum(\n",
+    "            suppliers.loc[l, \"fat\"] * m.z[l, c] for l in m.L\n",
+    "        ) >= customers.loc[c, \"min_fat\"] * (sum(m.z[l, c] for l in m.L) + m.y[c])\n",
+    "\n",
+    "    SOLVER_LO.solve(m)\n",
     "\n",
-    "    pyo.SolverFactory(SOLVER_LO).solve(m)\n",
-    "    \n",
     "    return m"
    ]
   },
@@ -950,9 +1024,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Milk Pooling Model - Convex Approximation\n",
+      "'Milk pooling v3 (Convex approximation)'\n",
       "\n",
-      "Pool composition = 0.04\n",
+      "Pool composition = 0.0400\n",
       "Profit = 111411.76\n",
       "\n",
       "Supplier Report\n",
@@ -998,8 +1072,8 @@
        "      <td>45.0</td>\n",
        "      <td>local</td>\n",
        "      <td>2500.0</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>-0.0000</td>\n",
+       "      <td>-0.0</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>2500.0000</td>\n",
        "      <td>112500.0000</td>\n",
@@ -1046,7 +1120,7 @@
       ],
       "text/plain": [
        "          fat  cost location  Customer 1  Customer 2  Customer 3       Pool  \\\n",
-       "Farm A  0.045  45.0    local      2500.0      0.0000         0.0     0.0000   \n",
+       "Farm A  0.045  45.0    local      2500.0     -0.0000        -0.0     0.0000   \n",
        "Farm B  0.030  42.0    local         0.0   1029.4118         0.0     0.0000   \n",
        "Farm C  0.033  37.0   remote         0.0      0.0000         0.0  4852.9412   \n",
        "Farm D  0.050  45.0   remote         0.0      0.0000         0.0  4117.6471   \n",
@@ -1120,7 +1194,7 @@
        "      <td>0.030</td>\n",
        "      <td>48.0</td>\n",
        "      <td>2500.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>-0.0</td>\n",
        "      <td>1029.4118</td>\n",
        "      <td>1470.5882</td>\n",
        "      <td>2500.0</td>\n",
@@ -1132,7 +1206,7 @@
        "      <td>0.040</td>\n",
        "      <td>50.0</td>\n",
        "      <td>4000.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>-0.0</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>4000.0000</td>\n",
        "      <td>4000.0</td>\n",
@@ -1146,8 +1220,8 @@
       "text/plain": [
        "            min_fat  price  demand  Farm A     Farm B       Pool  Amount  \\\n",
        "Customer 1    0.045   52.0  6000.0  2500.0     0.0000  3500.0000  6000.0   \n",
-       "Customer 2    0.030   48.0  2500.0     0.0  1029.4118  1470.5882  2500.0   \n",
-       "Customer 3    0.040   50.0  4000.0     0.0     0.0000  4000.0000  4000.0   \n",
+       "Customer 2    0.030   48.0  2500.0    -0.0  1029.4118  1470.5882  2500.0   \n",
+       "Customer 3    0.040   50.0  4000.0    -0.0     0.0000  4000.0000  4000.0   \n",
        "\n",
        "            fat delivered    Income  \n",
        "Customer 1         0.0421  312000.0  \n",
@@ -1161,39 +1235,40 @@
    ],
    "source": [
     "def report_solution(m):\n",
-    "    \n",
     "    # Supplier report\n",
     "    S = suppliers.copy()\n",
     "    for l in m.L:\n",
     "        for c in m.C:\n",
-    "            S.loc[l, c] = m.z[l, c]() \n",
+    "            S.loc[l, c] = m.z[l, c]()\n",
     "    for r in m.R:\n",
     "        S.loc[r, \"Pool\"] = m.x[r]()\n",
     "    S = S.fillna(0)\n",
     "    S[\"Amount\"] = S[m.C].sum(axis=1) + S[\"Pool\"]\n",
-    "    S[\"Expense\"] = S[\"Amount\"]*S[\"cost\"]\n",
+    "    S[\"Expense\"] = S[\"Amount\"] * S[\"cost\"]\n",
     "\n",
-    "    \n",
     "    # Customer report\n",
     "    C = customers.copy()\n",
     "    for c in m.C:\n",
     "        for l in m.L:\n",
     "            C.loc[c, l] = m.z[l, c]()\n",
     "    for c in m.C:\n",
-    "        C.loc[c, \"Pool\"] = m.y[c]()   \n",
+    "        C.loc[c, \"Pool\"] = m.y[c]()\n",
     "    C = C.fillna(0)\n",
     "    C[\"Amount\"] = C[m.L].sum(axis=1) + C[\"Pool\"]\n",
-    "    C[\"fat delivered\"] = (sum(C[l]*S.loc[l, \"fat\"] for l in m.L) + C[\"Pool\"] * m.p())/C[\"Amount\"]\n",
+    "    C[\"fat delivered\"] = (\n",
+    "        sum(C[l] * S.loc[l, \"fat\"] for l in m.L) + C[\"Pool\"] * m.p()\n",
+    "    ) / C[\"Amount\"]\n",
     "    C[\"Income\"] = C[\"Amount\"] * C[\"price\"]\n",
-    "    \n",
+    "\n",
     "    print(m)\n",
-    "    print(f\"\\nPool composition = {m.p()}\")\n",
+    "    print(f\"\\nPool composition = {m.p():5.4f}\")\n",
     "    print(f\"Profit = {m.profit():0.2f}\")\n",
     "    print(f\"\\nSupplier Report\\n\")\n",
     "    display(S.round(4))\n",
     "    print(f\"\\nCustomer Report\\n\")\n",
     "    display(C.round(4))\n",
     "\n",
+    "\n",
     "m_convex = milk_pooling_convex()\n",
     "report_solution(m_convex)"
    ]
@@ -1216,19 +1291,9 @@
    "outputs": [
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
-       "Text(0.036, 106000, 'convex approximation')"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
+       "<Figure size 900x500 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1236,19 +1301,26 @@
     }
    ],
    "source": [
-    "fig, ax = plt.subplots(figsize=(8, 4))\n",
-    "ax.plot(p_plot, f_plot)\n",
-    "ax.set_title(\"Milk Pooling\")\n",
+    "fig, ax = plt.subplots(figsize=(9, 5))\n",
+    "ax.plot(p_plot, f_plot, lw=2)\n",
     "ax.set_xlabel(\"Pool composition p\")\n",
     "ax.set_ylabel(\"Profit\")\n",
     "ax.grid(True)\n",
     "\n",
-    "ax.plot(m_convex.p(), m_convex.profit(), 'ro', ms=10)\n",
-    "ax.axhline(m_convex.profit(), color='r', linestyle='--')\n",
-    "ax.axvline(m_convex.p(), color='r', linestyle='--')\n",
-    "ax.annotate(\"convex approximation\", xy=(m_convex.p(), m_convex.profit()),\n",
-    "            xytext=(0.036, 106000), ha=\"right\", fontsize=14,\n",
-    "            arrowprops=dict(shrink=0.1, width=1, headwidth=5))"
+    "ax.plot(m_convex.p(), m_convex.profit(), \"ro\", ms=10)\n",
+    "ax.axhline(m_convex.profit(), color=\"r\", linestyle=\"--\")\n",
+    "ax.axvline(m_convex.p(), color=\"r\", linestyle=\"--\")\n",
+    "ax.annotate(\n",
+    "    \"convex approximation\",\n",
+    "    xy=(m_convex.p(), m_convex.profit()),\n",
+    "    xytext=(0.036, 106000),\n",
+    "    ha=\"right\",\n",
+    "    fontsize=14,\n",
+    "    arrowprops=dict(shrink=0.1, width=1, headwidth=5, facecolor=\"black\"),\n",
+    ")\n",
+    "plt.rcParams.update({\"font.size\": 14})\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
    ]
   },
   {
@@ -1274,68 +1346,14 @@
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "m_est = f(m_convex.p())\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(8, 4))\n",
-    "ax.plot(p_plot, f_plot)\n",
-    "ax.set_title(\"Milk Pooling\")\n",
-    "ax.set_xlabel(\"Pool composition p\")\n",
-    "ax.set_ylabel(\"Profit\")\n",
-    "ax.grid(True)\n",
-    "\n",
-    "ax.plot(m_convex.p(), m_convex.profit(), 'ro', ms=10)\n",
-    "ax.axhline(m_convex.profit(), color='r', linestyle='--')\n",
-    "ax.axvline(m_convex.p(), color='r', linestyle='--')\n",
-    "ax.annotate(\"convex approximation\", xy=(m_convex.p(), m_convex.profit()),\n",
-    "            xytext=(0.036, 106000), ha=\"right\", fontsize=14,\n",
-    "            arrowprops=dict(shrink=0.1, width=1, headwidth=5))\n",
-    "\n",
-    "ax.plot(m_est.p(), m_est.profit(), 'go', ms=10)\n",
-    "ax.axhline(m_est.profit(), color='g', linestyle='--')\n",
-    "ax.annotate(\"local maxima\", xy=(m_convex.p(), m_est.profit()),\n",
-    "            xytext=(0.045, 105000), ha=\"left\", fontsize=14,\n",
-    "            arrowprops=dict(shrink=0.1, width=1, headwidth=5))\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8333e318-da8e-4fe8-b726-adebec04c23a",
-   "metadata": {},
-   "source": [
-    "The result shows the profit if the pooled milk transported from the remote farms has a fat content $p = 0.04$ them a profit of 100,088 is realized which is better than 81,000 earned for business as usual with just local suppliers, but falls short of the 122,441 earned if the remote milk supply could be transported without pooling.\n",
-    "\n",
-    "The following cell presents a full report of the solution."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "08283164-7c36-4788-bb74-d429af542cce",
-   "metadata": {
-    "tags": []
-   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Milk Pooling Model\n",
+      "'Milk pooling v3 (Bilinear formulation)'\n",
       "\n",
-      "Pool composition = 0.04\n",
+      "Pool composition = 0.0400\n",
       "Profit = 100088.24\n",
       "\n",
       "Supplier Report\n",
@@ -1540,10 +1558,61 @@
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "report_solution(m_est)"
+    "m_est = milk_pooling_bilinear(m_convex.p())\n",
+    "report_solution(m_est)\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(9, 5))\n",
+    "ax.plot(p_plot, f_plot, lw=2)\n",
+    "ax.set_xlabel(\"Pool composition p\")\n",
+    "ax.set_ylabel(\"Profit\")\n",
+    "ax.grid(True)\n",
+    "\n",
+    "ax.plot(m_convex.p(), m_convex.profit(), \"ro\", ms=10)\n",
+    "ax.axhline(m_convex.profit(), color=\"r\", linestyle=\"--\")\n",
+    "ax.axvline(m_convex.p(), color=\"r\", linestyle=\"--\")\n",
+    "ax.annotate(\n",
+    "    \"convex approximation\",\n",
+    "    xy=(m_convex.p(), m_convex.profit()),\n",
+    "    xytext=(0.036, 106000),\n",
+    "    ha=\"right\",\n",
+    "    fontsize=14,\n",
+    "    arrowprops=dict(shrink=0.1, width=1, headwidth=5, facecolor=\"black\"),\n",
+    ")\n",
+    "\n",
+    "ax.plot(m_est.p(), m_est.profit(), \"go\", ms=10)\n",
+    "ax.axhline(m_est.profit(), color=\"g\", linestyle=\"--\")\n",
+    "ax.annotate(\n",
+    "    \"local max found\",\n",
+    "    xy=(m_convex.p(), m_est.profit()),\n",
+    "    xytext=(0.045, 105000),\n",
+    "    ha=\"left\",\n",
+    "    fontsize=14,\n",
+    "    arrowprops=dict(shrink=0.1, width=1, headwidth=5, facecolor=\"black\"),\n",
+    ")\n",
+    "plt.rcParams.update({\"font.size\": 14})\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8333e318-da8e-4fe8-b726-adebec04c23a",
+   "metadata": {},
+   "source": [
+    "The result shows the profit if the pooled milk transported from the remote farms has a fat content $p = 0.04$ them a profit of 100,088 is realized which is better than 81,000 earned for business as usual with just local suppliers, but falls short of the 122,441 earned if the remote milk supply could be transported without pooling."
    ]
   },
   {
@@ -1551,7 +1620,7 @@
    "id": "8be412fa-564a-4b9a-8dda-f79fea889bab",
    "metadata": {},
    "source": [
-    "With regard to the practical impact, the results of using this particular convex approximation are mixed. The approximation successfully produced a value for the pool composition $p$ which would produce a profit of over 100,088. However, the reported value for $p$ was actually the smallest of the three local maxima for this problem. This discrepancy may have large consequences regarding the choice of suppliers."
+    "With regard to the practical impact, the results of using this particular convex approximation are mixed. The approximation successfully produced a value for the pool composition $p$ which would produce a profit of over $100088$. However, the reported value for $p$ was actually the smallest of the three local maxima for this problem. This discrepancy may have large consequences regarding the choice of suppliers."
    ]
   },
   {
@@ -1561,29 +1630,28 @@
     "tags": []
    },
    "source": [
-    "## Global Nonlinear Optimization (GNLO) solution with Couenne\n",
+    "## Nonlinear Optimization (NLO) solution with ipopt\n",
     "\n",
-    "The final version of this milk pooling model returns to the bilinear formulation with pool composition $p$ as a decision variable. The following Pyomo implementation needs to specify a solver capable of solving the resulting problem. This has been tested with nonlinear solvers [`ipopt`](https://github.com/coin-or/Ipopt) and [`couenne`](https://github.com/coin-or/Couenne). [Pre-compiled binaries for these solvers can be downloaded from AMPL](https://ampl.com/products/solvers/open-source/). "
+    "The final version of this milk pooling model returns to the bilinear formulation with pool composition $p$ as a decision variable. The following Pyomo implementation needs to specify a solver capable of solving the resulting problem, in this case we use the nonlinear solver [`ipopt`](https://github.com/coin-or/Ipopt)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "id": "6472ae12-da84-430c-baaf-1310870d8050",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "import pyomo.environ as pyo\n",
-    "\n",
-    "def milk_pooling_bilinear():\n",
-    "    \n",
-    "    m = pyo.ConcreteModel('Milk Pooling Model - Bilinear')\n",
+    "def milk_pooling_bilinear_NLO(localmin=False):\n",
+    "    m = pyo.ConcreteModel(\"Milk Pooling Model - Bilinear (NLO)\")\n",
     "\n",
-    "    # define sources \n",
-    "    m.L = pyo.Set(initialize=suppliers.index[suppliers[\"location\"]==\"local\"].tolist())\n",
-    "    m.R = pyo.Set(initialize=suppliers.index[suppliers[\"location\"]==\"remote\"].tolist())\n",
+    "    # define sources\n",
+    "    m.L = pyo.Set(initialize=suppliers.index[suppliers[\"location\"] == \"local\"].tolist())\n",
+    "    m.R = pyo.Set(\n",
+    "        initialize=suppliers.index[suppliers[\"location\"] == \"remote\"].tolist()\n",
+    "    )\n",
     "\n",
     "    # define customers\n",
     "    m.C = pyo.Set(initialize=customers.index.tolist())\n",
@@ -1592,40 +1660,55 @@
     "    m.x = pyo.Var(m.R, domain=pyo.NonNegativeReals)\n",
     "    m.y = pyo.Var(m.C, bounds=lambda m, c: (0, customers.loc[c, \"demand\"]))\n",
     "    m.z = pyo.Var(m.L * m.C, domain=pyo.NonNegativeReals)\n",
-    "    \n",
     "    m.p = pyo.Var(bounds=(remote_suppliers[\"fat\"].min(), remote_suppliers[\"fat\"].max()))\n",
     "\n",
     "    @m.Objective(sense=pyo.maximize)\n",
     "    def profit(m):\n",
-    "        return + sum(m.z[l, c]*(customers.loc[c, \"price\"] - suppliers.loc[l, \"cost\"]) for l, c in m.L * m.C) \\\n",
-    "               + sum(m.y[c]*customers.loc[c, \"price\"] for c in m.C) \\\n",
-    "               - sum(m.x[r]*suppliers.loc[r, \"cost\"] for r in m.R)\n",
+    "        return (\n",
+    "            +sum(\n",
+    "                m.z[l, c] * (customers.loc[c, \"price\"] - suppliers.loc[l, \"cost\"])\n",
+    "                for l, c in m.L * m.C\n",
+    "            )\n",
+    "            + sum(m.y[c] * customers.loc[c, \"price\"] for c in m.C)\n",
+    "            - sum(m.x[r] * suppliers.loc[r, \"cost\"] for r in m.R)\n",
+    "        )\n",
     "\n",
     "    @m.Constraint(m.C)\n",
     "    def customer_demand(m, c):\n",
     "        return sum(m.z[l, c] for l in m.L) + m.y[c] <= customers.loc[c, \"demand\"]\n",
-    "    \n",
+    "\n",
     "    @m.Constraint()\n",
-    "    def pool_balance(m,):\n",
+    "    def pool_balance(\n",
+    "        m,\n",
+    "    ):\n",
     "        return sum(m.x[r] for r in m.R) == sum(m.y[c] for c in m.C)\n",
-    "    \n",
+    "\n",
     "    @m.Constraint()\n",
     "    def pool_quality(m):\n",
-    "        return sum(suppliers.loc[r, \"fat\"] * m.x[r] for r in m.R) == m.p * sum(m.x[r] for r in m.R)\n",
-    "    \n",
+    "        return sum(suppliers.loc[r, \"fat\"] * m.x[r] for r in m.R) == m.p * sum(\n",
+    "            m.x[r] for r in m.R\n",
+    "        )\n",
+    "\n",
     "    @m.Constraint(m.C)\n",
     "    def customer_quality(m, c):\n",
-    "        return m.p * m.y[c] + sum(suppliers.loc[l, \"fat\"] * m.z[l, c] for l in m.L) \\\n",
-    "                 >= customers.loc[c, \"min_fat\"] * (sum(m.z[l, c] for l in m.L) + m.y[c])\n",
+    "        return m.p * m.y[c] + sum(\n",
+    "            suppliers.loc[l, \"fat\"] * m.z[l, c] for l in m.L\n",
+    "        ) >= customers.loc[c, \"min_fat\"] * (sum(m.z[l, c] for l in m.L) + m.y[c])\n",
+    "\n",
+    "    # change ipopt options to find local minima instead of the global one\n",
+    "    if localmin:\n",
+    "        SOLVER_NLO.options[\"bound_frac\"] = 0.5\n",
+    "    else:\n",
+    "        SOLVER_NLO.options[\"bound_frac\"] = 0.01\n",
+    "\n",
+    "    SOLVER_NLO.solve(m)\n",
     "\n",
-    "    pyo.SolverFactory(SOLVER_GNLO).solve(m)\n",
-    "    \n",
-    "    return m\n"
+    "    return m"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "id": "653ba91b-8c7d-4aaa-9478-0160db734594",
    "metadata": {
     "tags": []
@@ -1635,9 +1718,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Milk Pooling Model - Bilinear\n",
+      "'Milk Pooling Model - Bilinear (NLO)'\n",
       "\n",
-      "Pool composition = 0.033\n",
+      "Pool composition = 0.0330\n",
       "Profit = 102833.33\n",
       "\n",
       "Supplier Report\n",
@@ -1682,19 +1765,19 @@
        "      <td>0.045</td>\n",
        "      <td>45.0</td>\n",
        "      <td>local</td>\n",
-       "      <td>6000.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2333.3333</td>\n",
+       "      <td>6000.0001</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>2333.3334</td>\n",
        "      <td>0.0000</td>\n",
-       "      <td>8333.3333</td>\n",
-       "      <td>375000.0000</td>\n",
+       "      <td>8333.3334</td>\n",
+       "      <td>375000.0037</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Farm B</th>\n",
        "      <td>0.030</td>\n",
        "      <td>42.0</td>\n",
        "      <td>local</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>-0.0000</td>\n",
        "      <td>0.0</td>\n",
        "      <td>-0.0000</td>\n",
        "      <td>0.0000</td>\n",
@@ -1706,19 +1789,19 @@
        "      <td>0.033</td>\n",
        "      <td>37.0</td>\n",
        "      <td>remote</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>0.0000</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>4166.6667</td>\n",
        "      <td>4166.6667</td>\n",
-       "      <td>154166.6667</td>\n",
+       "      <td>154166.6683</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Farm D</th>\n",
        "      <td>0.050</td>\n",
        "      <td>45.0</td>\n",
        "      <td>remote</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>0.0000</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0000</td>\n",
        "      <td>-0.0000</td>\n",
@@ -1731,15 +1814,15 @@
       ],
       "text/plain": [
        "          fat  cost location  Customer 1  Customer 2  Customer 3       Pool  \\\n",
-       "Farm A  0.045  45.0    local      6000.0         0.0   2333.3333     0.0000   \n",
-       "Farm B  0.030  42.0    local         0.0         0.0     -0.0000     0.0000   \n",
-       "Farm C  0.033  37.0   remote         0.0         0.0      0.0000  4166.6667   \n",
-       "Farm D  0.050  45.0   remote         0.0         0.0      0.0000    -0.0000   \n",
+       "Farm A  0.045  45.0    local   6000.0001        -0.0   2333.3334     0.0000   \n",
+       "Farm B  0.030  42.0    local     -0.0000         0.0     -0.0000     0.0000   \n",
+       "Farm C  0.033  37.0   remote      0.0000         0.0      0.0000  4166.6667   \n",
+       "Farm D  0.050  45.0   remote      0.0000         0.0      0.0000    -0.0000   \n",
        "\n",
        "           Amount      Expense  \n",
-       "Farm A  8333.3333  375000.0000  \n",
+       "Farm A  8333.3334  375000.0037  \n",
        "Farm B    -0.0000      -0.0000  \n",
-       "Farm C  4166.6667  154166.6667  \n",
+       "Farm C  4166.6667  154166.6683  \n",
        "Farm D    -0.0000      -0.0000  "
       ]
      },
@@ -1793,51 +1876,51 @@
        "      <td>0.045</td>\n",
        "      <td>52.0</td>\n",
        "      <td>6000.0</td>\n",
-       "      <td>6000.0000</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>6000.0001</td>\n",
+       "      <td>-0.0</td>\n",
        "      <td>0.0000</td>\n",
-       "      <td>6000.0</td>\n",
+       "      <td>6000.0001</td>\n",
        "      <td>0.045</td>\n",
-       "      <td>312000.0</td>\n",
+       "      <td>312000.0031</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Customer 2</th>\n",
        "      <td>0.030</td>\n",
        "      <td>48.0</td>\n",
        "      <td>2500.0</td>\n",
-       "      <td>0.0000</td>\n",
+       "      <td>-0.0000</td>\n",
        "      <td>0.0</td>\n",
        "      <td>2500.0000</td>\n",
-       "      <td>2500.0</td>\n",
+       "      <td>2500.0000</td>\n",
        "      <td>0.033</td>\n",
-       "      <td>120000.0</td>\n",
+       "      <td>120000.0012</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Customer 3</th>\n",
        "      <td>0.040</td>\n",
        "      <td>50.0</td>\n",
        "      <td>4000.0</td>\n",
-       "      <td>2333.3333</td>\n",
+       "      <td>2333.3334</td>\n",
        "      <td>-0.0</td>\n",
        "      <td>1666.6667</td>\n",
-       "      <td>4000.0</td>\n",
+       "      <td>4000.0000</td>\n",
        "      <td>0.040</td>\n",
-       "      <td>200000.0</td>\n",
+       "      <td>200000.0020</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "            min_fat  price  demand     Farm A  Farm B       Pool  Amount  \\\n",
-       "Customer 1    0.045   52.0  6000.0  6000.0000     0.0     0.0000  6000.0   \n",
-       "Customer 2    0.030   48.0  2500.0     0.0000     0.0  2500.0000  2500.0   \n",
-       "Customer 3    0.040   50.0  4000.0  2333.3333    -0.0  1666.6667  4000.0   \n",
+       "            min_fat  price  demand     Farm A  Farm B       Pool     Amount  \\\n",
+       "Customer 1    0.045   52.0  6000.0  6000.0001    -0.0     0.0000  6000.0001   \n",
+       "Customer 2    0.030   48.0  2500.0    -0.0000     0.0  2500.0000  2500.0000   \n",
+       "Customer 3    0.040   50.0  4000.0  2333.3334    -0.0  1666.6667  4000.0000   \n",
        "\n",
-       "            fat delivered    Income  \n",
-       "Customer 1          0.045  312000.0  \n",
-       "Customer 2          0.033  120000.0  \n",
-       "Customer 3          0.040  200000.0  "
+       "            fat delivered       Income  \n",
+       "Customer 1          0.045  312000.0031  \n",
+       "Customer 2          0.033  120000.0012  \n",
+       "Customer 3          0.040  200000.0020  "
       ]
      },
      "metadata": {},
@@ -1845,9 +1928,9 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
+       "<Figure size 900x500 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1855,70 +1938,314 @@
     }
    ],
    "source": [
-    "m_global = milk_pooling_bilinear()\n",
+    "m_global = milk_pooling_bilinear_NLO()\n",
+    "report_solution(m_global)\n",
     "\n",
-    "fig, ax = plt.subplots(figsize=(8, 4))\n",
-    "ax.plot(p_plot, f_plot)\n",
-    "ax.set_title(\"Milk Pooling\")\n",
+    "fig, ax = plt.subplots(figsize=(9, 5))\n",
+    "ax.plot(p_plot, f_plot, lw=2)\n",
     "ax.set_xlabel(\"Pool composition p\")\n",
     "ax.set_ylabel(\"Profit\")\n",
     "ax.grid(True)\n",
-    "\n",
-    "ax.plot(m_convex.p(), m_convex.profit(), 'ro', ms=10)\n",
-    "ax.axhline(m_convex.profit(), color='r', linestyle='--')\n",
-    "ax.axvline(m_convex.p(), color='r', linestyle='--')\n",
-    "ax.annotate(\"convex approximation\", xy=(m_convex.p(), m_convex.profit()),\n",
-    "            xytext=(0.036, 106000), ha=\"right\", fontsize=14,\n",
-    "            arrowprops=dict(shrink=0.1, width=1, headwidth=5))\n",
-    "\n",
-    "ax.plot(m_est.p(), m_est.profit(), 'go', ms=10)\n",
-    "ax.axhline(m_est.profit(), color='g', linestyle='--')\n",
-    "ax.annotate(\"local maxima\", xy=(m_convex.p(), m_est.profit()),\n",
-    "            xytext=(0.045, 105000), ha=\"left\", fontsize=14,\n",
-    "            arrowprops=dict(shrink=0.1, width=1, headwidth=5))\n",
-    "\n",
-    "ax.plot(m_global.p(), m_global.profit(), 'bo', ms=10)\n",
-    "ax.axhline(m_global.profit(), color='b', linestyle='--')\n",
-    "ax.annotate(\"global maxima\", xy=(m_global.p(), m_global.profit()),\n",
-    "            xytext=(0.025, 95000), ha=\"left\", fontsize=14,\n",
-    "            arrowprops=dict(shrink=0.1, width=1, headwidth=5))\n",
-    "\n",
-    "report_solution(m_global)"
+    "ax.plot(m_global.p(), m_global.profit(), \"go\", ms=10)\n",
+    "ax.axhline(m_global.profit(), color=\"g\", linestyle=\"--\")\n",
+    "ax.annotate(\n",
+    "    \"global max found\",\n",
+    "    xy=(m_global.p(), m_global.profit()),\n",
+    "    xytext=(0.035, 87000),\n",
+    "    ha=\"left\",\n",
+    "    fontsize=14,\n",
+    "    arrowprops=dict(shrink=0.1, width=1, headwidth=5, facecolor=\"black\"),\n",
+    ")\n",
+    "plt.rcParams.update({\"font.size\": 14})\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "7e32fe3b-7b1e-4714-a64d-b29da624a10b",
+   "id": "fadba4ca",
    "metadata": {},
    "source": [
-    "## Concluding Remarks\n",
+    "In this case, the convergence to the global optimum was pure luck. The way general non-linear optimization solvers work is that they begin from an initial solution and improve from then on, stopping when a set of conditions, known as the **KKT conditions**, is met. If we change the `ipopt` settings for how to pick the initial point, we can end up in one of the suboptimal local minima. \n",
     "\n",
-    "The solution for the bilinear pooling model reveals several features of the problem. \n",
-    "\n",
-    "* For the given parameters, pooling raw materials for shipment from remote suppliers yields the most profitable solution, but that solution is only possible because there are local suppliers to augment the pool blend to meet individual customer requirements. \n",
+    "Setting the `localmin` optional argument to `True`, we can trigger this behavior and obtain a solution that is not the global optimum."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "01a5f695",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "'Milk Pooling Model - Bilinear (NLO)'\n",
+      "\n",
+      "Pool composition = 0.0450\n",
+      "Profit = 101392.16\n",
+      "\n",
+      "Supplier Report\n",
+      "\n"
+     ]
+    },
+    {
+     "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>fat</th>\n",
+       "      <th>cost</th>\n",
+       "      <th>location</th>\n",
+       "      <th>Customer 1</th>\n",
+       "      <th>Customer 2</th>\n",
+       "      <th>Customer 3</th>\n",
+       "      <th>Pool</th>\n",
+       "      <th>Amount</th>\n",
+       "      <th>Expense</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Farm A</th>\n",
+       "      <td>0.045</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>local</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>-0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>-0.0000</td>\n",
+       "      <td>-0.0000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Farm B</th>\n",
+       "      <td>0.030</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>local</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>2500.0</td>\n",
+       "      <td>1333.3333</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>3833.3334</td>\n",
+       "      <td>161000.0016</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Farm C</th>\n",
+       "      <td>0.033</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>remote</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>2549.0196</td>\n",
+       "      <td>2549.0196</td>\n",
+       "      <td>94313.7265</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Farm D</th>\n",
+       "      <td>0.050</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>remote</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>6117.6471</td>\n",
+       "      <td>6117.6471</td>\n",
+       "      <td>275294.1203</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          fat  cost location  Customer 1  Customer 2  Customer 3       Pool  \\\n",
+       "Farm A  0.045  45.0    local         0.0        -0.0     -0.0000     0.0000   \n",
+       "Farm B  0.030  42.0    local        -0.0      2500.0   1333.3333     0.0000   \n",
+       "Farm C  0.033  37.0   remote         0.0         0.0      0.0000  2549.0196   \n",
+       "Farm D  0.050  45.0   remote         0.0         0.0      0.0000  6117.6471   \n",
+       "\n",
+       "           Amount      Expense  \n",
+       "Farm A    -0.0000      -0.0000  \n",
+       "Farm B  3833.3334  161000.0016  \n",
+       "Farm C  2549.0196   94313.7265  \n",
+       "Farm D  6117.6471  275294.1203  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Customer Report\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\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>min_fat</th>\n",
+       "      <th>price</th>\n",
+       "      <th>demand</th>\n",
+       "      <th>Farm A</th>\n",
+       "      <th>Farm B</th>\n",
+       "      <th>Pool</th>\n",
+       "      <th>Amount</th>\n",
+       "      <th>fat delivered</th>\n",
+       "      <th>Income</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Customer 1</th>\n",
+       "      <td>0.045</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>6000.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.0000</td>\n",
+       "      <td>6000.0001</td>\n",
+       "      <td>6000.0001</td>\n",
+       "      <td>0.045</td>\n",
+       "      <td>312000.0031</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Customer 2</th>\n",
+       "      <td>0.030</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>2500.0</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>2500.0000</td>\n",
+       "      <td>-0.0000</td>\n",
+       "      <td>2500.0000</td>\n",
+       "      <td>0.030</td>\n",
+       "      <td>120000.0012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Customer 3</th>\n",
+       "      <td>0.040</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>4000.0</td>\n",
+       "      <td>-0.0</td>\n",
+       "      <td>1333.3333</td>\n",
+       "      <td>2666.6667</td>\n",
+       "      <td>4000.0000</td>\n",
+       "      <td>0.040</td>\n",
+       "      <td>200000.0020</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            min_fat  price  demand  Farm A     Farm B       Pool     Amount  \\\n",
+       "Customer 1    0.045   52.0  6000.0     0.0    -0.0000  6000.0001  6000.0001   \n",
+       "Customer 2    0.030   48.0  2500.0    -0.0  2500.0000    -0.0000  2500.0000   \n",
+       "Customer 3    0.040   50.0  4000.0    -0.0  1333.3333  2666.6667  4000.0000   \n",
+       "\n",
+       "            fat delivered       Income  \n",
+       "Customer 1          0.045  312000.0031  \n",
+       "Customer 2          0.030  120000.0012  \n",
+       "Customer 3          0.040  200000.0020  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "m_global2 = milk_pooling_bilinear_NLO(localmin=True)\n",
+    "report_solution(m_global2)\n",
     "\n",
-    "* Customer 2 receives a blend that is 3.3% exceeding the requirement of 3%. This results in some \"give away\" of product quality in return for the economic benefits of pooling."
+    "fig, ax = plt.subplots(figsize=(9, 5))\n",
+    "ax.plot(p_plot, f_plot, lw=2)\n",
+    "ax.set_xlabel(\"Pool composition p\")\n",
+    "ax.set_ylabel(\"Profit\")\n",
+    "ax.grid(True)\n",
+    "ax.plot(m_global.p(), m_global.profit(), \"bo\", ms=10)\n",
+    "ax.axhline(m_global.profit(), color=\"b\", linestyle=\"--\")\n",
+    "ax.annotate(\n",
+    "    \"global max\",\n",
+    "    xy=(m_global.p(), m_global.profit()),\n",
+    "    xytext=(0.0245, 96000),\n",
+    "    ha=\"left\",\n",
+    "    fontsize=14,\n",
+    "    arrowprops=dict(shrink=0.1, width=1, headwidth=5, facecolor=\"black\"),\n",
+    ")\n",
+    "ax.plot(m_global2.p(), m_global2.profit(), \"go\", ms=10)\n",
+    "ax.axhline(m_global2.profit(), color=\"g\", linestyle=\"--\")\n",
+    "ax.annotate(\n",
+    "    \"local max found\",\n",
+    "    xy=(m_global2.p(), m_global2.profit()),\n",
+    "    xytext=(0.0495, 93000),\n",
+    "    ha=\"left\",\n",
+    "    fontsize=14,\n",
+    "    arrowprops=dict(shrink=0.1, width=1, headwidth=5, facecolor=\"black\"),\n",
+    ")\n",
+    "plt.rcParams.update({\"font.size\": 14})\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "1ff20541-70e9-4ba9-a6e1-8c072a8964f1",
-   "metadata": {
-    "jp-MarkdownHeadingCollapsed": true,
-    "tags": []
-   },
+   "id": "7e32fe3b-7b1e-4714-a64d-b29da624a10b",
+   "metadata": {},
    "source": [
-    "## Suggested Exercises\n",
-    "\n",
-    "This simple model demonstrates practical issues that arise in modeling the non-convex problems. Take time explore the behavior of the model to parameter changes and the nature of the solution. \n",
-    "\n",
-    "1. Examine the model data and explain why the enhanced profits are observed only for a particular range of values in $p$.\n",
+    "## Concluding Remarks\n",
     "\n",
-    "2. Think carefully about the non-convex behavior observed in the plot of profit versus parameter $p$.  Why are the local maxima located where they are, and how are they related to problem data? Test your hypothesis by changing the problem data.  What happens when the product specification for Customer A is set equal to 0.045? What happens when it is set to 0.04?\n",
+    "The solution for the bilinear pooling model reveals several features of the problem. \n",
     "\n",
-    "3. Revise the Pyomo model using 'cbc', 'gurobi_direct', 'ipopt', and 'bonmin' to find a solution. Did you find a solver that could solve this nonlinear problem?\n",
+    "* For the given parameters, pooling raw materials for shipment from remote suppliers yields the most profitable solution, but that solution is only possible because there are local suppliers to augment the pool blend to meet individual customer requirements. \n",
     "\n",
-    "4. The above analysis assumed unlimited transport. If the truck turns out to have a limit of 4,000 units of milk, write the mathematical constraint necessary to introduce that limit into the model. Add that constraint to the Pyomo model and discuss the impact on profit."
+    "* Customer 2 receives a blend that is 3.3% exceeding the requirement of 3%. This results in some \"give away\" of product quality in return for the economic benefits of pooling."
    ]
   },
   {
@@ -1951,14 +2278,6 @@
     "\n",
     "Applications for pooling and blending are probably underappreciated. In particular, what role might pooling and blending problems have in projects like the World Food Programme (WFP)?"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1c8b31c5-f529-4dda-8b88-9a3100d39b0d",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -1977,7 +2296,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.10.10"
   }
  },
  "nbformat": 4,
diff --git a/genindex.html b/genindex.html
index dca859ac..e97b2340 100644
--- a/genindex.html
+++ b/genindex.html
@@ -211,13 +211,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="notebooks/04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebooks/04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebooks/04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebooks/04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="notebooks/04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="notebooks/05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -673,7 +673,7 @@ <h2 id="S">S</h2>
       <ul>
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-        <li><a href="notebooks/05/milk-pooling.html#index-6">couenne</a>
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diff --git a/intro.html b/intro.html
index dd1d38c6..6982c246 100644
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 <li class="toctree-l2"><a class="reference internal" href="notebooks/04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebooks/04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebooks/04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebooks/04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="notebooks/04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="notebooks/05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
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index a338e766..3bec7dde 100644
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 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
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 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
diff --git a/notebooks/01/production-planning-advanced.html b/notebooks/01/production-planning-advanced.html
index e5752c4a..663bf020 100644
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 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
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index dde0e5a4..1e4b2633 100644
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 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
diff --git a/notebooks/02/02.00.html b/notebooks/02/02.00.html
index 72d1ca5c..ba31139d 100644
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-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
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 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
diff --git a/notebooks/02/L1-regression-wine-quality.html b/notebooks/02/L1-regression-wine-quality.html
index 1accf923..1bc34482 100644
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+++ b/notebooks/02/L1-regression-wine-quality.html
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 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
diff --git a/notebooks/02/bim-dual.html b/notebooks/02/bim-dual.html
index a10e239c..69c479f6 100644
--- a/notebooks/02/bim-dual.html
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@@ -216,13 +216,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
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 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
diff --git a/notebooks/02/bim-fractional.html b/notebooks/02/bim-fractional.html
index 87d30054..a58449e1 100644
--- a/notebooks/02/bim-fractional.html
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@@ -216,13 +216,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
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diff --git a/notebooks/02/bim-maxmin.html b/notebooks/02/bim-maxmin.html
index 1774ec5e..cdf9fde3 100644
--- a/notebooks/02/bim-maxmin.html
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@@ -216,13 +216,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
diff --git a/notebooks/02/bim-rawmaterialplanning.html b/notebooks/02/bim-rawmaterialplanning.html
index b33a60f1..533f3d96 100644
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@@ -216,13 +216,13 @@
 
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-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
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 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
diff --git a/notebooks/02/bim.html b/notebooks/02/bim.html
index adb9cba0..90445b77 100644
--- a/notebooks/02/bim.html
+++ b/notebooks/02/bim.html
@@ -216,13 +216,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
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 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
diff --git a/notebooks/02/lad-regression.html b/notebooks/02/lad-regression.html
index 65a2c989..0f0ab824 100644
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@@ -216,13 +216,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
diff --git a/notebooks/02/mad-portfolio-optimization.html b/notebooks/02/mad-portfolio-optimization.html
index fa3071c8..afeed8fb 100644
--- a/notebooks/02/mad-portfolio-optimization.html
+++ b/notebooks/02/mad-portfolio-optimization.html
@@ -216,13 +216,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
diff --git a/notebooks/02/multiproductionfaciliity_worstcase.html b/notebooks/02/multiproductionfaciliity_worstcase.html
index 558ad0bc..88e0a399 100644
--- a/notebooks/02/multiproductionfaciliity_worstcase.html
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@@ -216,13 +216,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
diff --git a/notebooks/03/03.00.html b/notebooks/03/03.00.html
index 70ec1e02..a29fd81a 100644
--- a/notebooks/03/03.00.html
+++ b/notebooks/03/03.00.html
@@ -216,13 +216,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
diff --git a/notebooks/03/bim-perturbed.html b/notebooks/03/bim-perturbed.html
index 6a80564f..1ce8362f 100644
--- a/notebooks/03/bim-perturbed.html
+++ b/notebooks/03/bim-perturbed.html
@@ -216,13 +216,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
diff --git a/notebooks/03/bim-production-revisited.html b/notebooks/03/bim-production-revisited.html
index a0165b30..8eb1a7b5 100644
--- a/notebooks/03/bim-production-revisited.html
+++ b/notebooks/03/bim-production-revisited.html
@@ -216,13 +216,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
diff --git a/notebooks/03/cryptarithms.html b/notebooks/03/cryptarithms.html
index 54663a01..979a08b6 100644
--- a/notebooks/03/cryptarithms.html
+++ b/notebooks/03/cryptarithms.html
@@ -216,13 +216,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
diff --git a/notebooks/03/facility-location.html b/notebooks/03/facility-location.html
index b47d97e1..4c6d0d94 100644
--- a/notebooks/03/facility-location.html
+++ b/notebooks/03/facility-location.html
@@ -216,13 +216,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
diff --git a/notebooks/03/job-shop-scheduling.html b/notebooks/03/job-shop-scheduling.html
index fbbd6f4d..97757d96 100644
--- a/notebooks/03/job-shop-scheduling.html
+++ b/notebooks/03/job-shop-scheduling.html
@@ -216,13 +216,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
diff --git a/notebooks/03/machine-scheduling.html b/notebooks/03/machine-scheduling.html
index 95690a46..029aff70 100644
--- a/notebooks/03/machine-scheduling.html
+++ b/notebooks/03/machine-scheduling.html
@@ -216,13 +216,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
diff --git a/notebooks/03/maintenance-planning.html b/notebooks/03/maintenance-planning.html
index c0b1f3da..2da61b75 100644
--- a/notebooks/03/maintenance-planning.html
+++ b/notebooks/03/maintenance-planning.html
@@ -216,13 +216,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
diff --git a/notebooks/03/recharging-electric-vehicle.html b/notebooks/03/recharging-electric-vehicle.html
index 6f7b98ee..b5f0fd5e 100644
--- a/notebooks/03/recharging-electric-vehicle.html
+++ b/notebooks/03/recharging-electric-vehicle.html
@@ -216,13 +216,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
diff --git a/notebooks/03/shift-scheduling.html b/notebooks/03/shift-scheduling.html
index 2ad0369f..94e41b87 100644
--- a/notebooks/03/shift-scheduling.html
+++ b/notebooks/03/shift-scheduling.html
@@ -216,13 +216,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
diff --git a/notebooks/03/simple-production-model-gdp.html b/notebooks/03/simple-production-model-gdp.html
index 447e9b37..dd132be7 100644
--- a/notebooks/03/simple-production-model-gdp.html
+++ b/notebooks/03/simple-production-model-gdp.html
@@ -216,13 +216,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
diff --git a/notebooks/03/strip-packing.html b/notebooks/03/strip-packing.html
index f6620744..4326a435 100644
--- a/notebooks/03/strip-packing.html
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@@ -216,13 +216,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
diff --git a/notebooks/04/04.00.html b/notebooks/04/04.00.html
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@@ -214,13 +214,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
diff --git a/notebooks/04/cryptocurrency-arbitrage.html b/notebooks/04/cryptocurrency-arbitrage.html
index 442903e0..82c14d85 100644
--- a/notebooks/04/cryptocurrency-arbitrage.html
+++ b/notebooks/04/cryptocurrency-arbitrage.html
@@ -75,7 +75,7 @@
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
     <link rel="next" title="Extra material: Energy dispatch problem" href="power-network.html" />
-    <link rel="prev" title="Scheduling as a graph coloring problem" href="graph-coloring.html" />
+    <link rel="prev" title="Exam room scheduling" href="graph-coloring.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
   </head>
@@ -216,13 +216,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2 current active"><a class="current reference internal" href="#">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -2373,7 +2373,7 @@ <h2>Bibliographic Notes<a class="headerlink" href="#bibliographic-notes" title="
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title">Scheduling as a graph coloring problem</p>
+        <p class="prev-next-title">Exam room scheduling</p>
       </div>
     </a>
     <a class="right-next"
diff --git a/notebooks/04/dinner-seat-allocation.html b/notebooks/04/dinner-seat-allocation.html
index cd61c949..b4f359cb 100644
--- a/notebooks/04/dinner-seat-allocation.html
+++ b/notebooks/04/dinner-seat-allocation.html
@@ -216,13 +216,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
diff --git a/notebooks/04/forex-arbitrage.html b/notebooks/04/forex-arbitrage.html
index 4733efa1..8122350e 100644
--- a/notebooks/04/forex-arbitrage.html
+++ b/notebooks/04/forex-arbitrage.html
@@ -9,7 +9,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
-    <title>Forex Arbitrage &#8212; Companion code for the book &#34;Hands-On Optimization with Python&#34;</title>
+    <title>Extra material: Forex Arbitrage &#8212; Companion code for the book &#34;Hands-On Optimization with Python&#34;</title>
   
   
   
@@ -216,13 +216,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2 current active"><a class="current reference internal" href="#">Forex Arbitrage</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -491,7 +491,7 @@
               
 
 <div id="jb-print-docs-body" class="onlyprint">
-    <h1>Forex Arbitrage</h1>
+    <h1>Extra material: Forex Arbitrage</h1>
     <!-- Table of contents -->
     <div id="print-main-content">
         <div id="jb-print-toc">
@@ -518,48 +518,42 @@ <h2> Contents </h2>
 <div id="searchbox"></div>
                 <article class="bd-article" role="main">
                   
-  <section class="tex2jax_ignore mathjax_ignore" id="forex-arbitrage">
-<h1>Forex Arbitrage<a class="headerlink" href="#forex-arbitrage" title="Permalink to this heading">#</a></h1>
-<p>This notebook presents an example of linear optimization on a network model for financial transactions. The goal is to identify whether or not an arbitrage opportunity exists given a matrix of cross-currency exchange rates. Other treatments of this problem and application are available, including the following links.</p>
+  <section class="tex2jax_ignore mathjax_ignore" id="extra-material-forex-arbitrage">
+<h1>Extra material: Forex Arbitrage<a class="headerlink" href="#extra-material-forex-arbitrage" title="Permalink to this heading">#</a></h1>
+<p>This notebook presents an example of linear optimization on a network model for financial transactions. The goal is to identify whether an arbitrage opportunity exists given a matrix of cross-currency exchange rates. Other treatments of this problem and application are available, including the following links.</p>
 <ul class="simple">
 <li><p><a class="reference external" href="https://github.com/hzjken/crypto-arbitrage-framework">Crypto Arbitrage Framework</a></p></li>
 <li><p><a class="reference external" href="https://nbviewer.org/github/rcroessmann/sharing_public/blob/master/arbitrage_identification.ipynb">Crypto Trading and Arbitrage Identification Strategies</a></p></li>
 </ul>
 <section id="preamble-install-pyomo-and-a-solver">
 <h2>Preamble: Install Pyomo and a solver<a class="headerlink" href="#preamble-install-pyomo-and-a-solver" title="Permalink to this heading">#</a></h2>
-<p>This cell selects and verifies a global SOLVER for the notebook.</p>
-<p>If run on Google Colab, the cell installs Pyomo and HiGHS, then sets SOLVER to
-use the Highs solver via the appsi module. If run elsewhere, it assumes Pyomo and CBC
-have been previously installed and sets SOLVER to use the CBC solver via the Pyomo
-SolverFactory. It then verifies that SOLVER is available.</p>
+<p>This cell selects and verifies a global SOLVER for the notebook. If run on Google Colab, the cell installs Pyomo and HiGHS, then sets SOLVER to
+use the HiGHs solver via the Pyomo SolverFactory. It then verifies that SOLVER is available.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">sys</span>
-
+ 
 <span class="k">if</span> <span class="s1">&#39;google.colab&#39;</span> <span class="ow">in</span> <span class="n">sys</span><span class="o">.</span><span class="n">modules</span><span class="p">:</span>
     <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>pyomo<span class="w"> </span>&gt;/dev/null<span class="w"> </span><span class="m">2</span>&gt;/dev/null
     <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>highspy<span class="w"> </span>&gt;/dev/null<span class="w"> </span><span class="m">2</span>&gt;/dev/null
-
-    <span class="kn">from</span> <span class="nn">pyomo.environ</span> <span class="kn">import</span> <span class="n">SolverFactory</span>
-    <span class="n">SOLVER</span> <span class="o">=</span> <span class="n">SolverFactory</span><span class="p">(</span><span class="s1">&#39;appsi_highs&#39;</span><span class="p">)</span>
-    
-<span class="k">else</span><span class="p">:</span>
-    <span class="kn">from</span> <span class="nn">pyomo.environ</span> <span class="kn">import</span> <span class="n">SolverFactory</span>
-    <span class="n">SOLVER</span> <span class="o">=</span> <span class="n">SolverFactory</span><span class="p">(</span><span class="s1">&#39;cbc&#39;</span><span class="p">)</span>
-
-<span class="k">assert</span> <span class="n">SOLVER</span><span class="o">.</span><span class="n">available</span><span class="p">(),</span> <span class="sa">f</span><span class="s2">&quot;Solver </span><span class="si">{</span><span class="n">SOLVER</span><span class="si">}</span><span class="s2"> is not available.&quot;</span>
+ 
+<span class="n">solver</span> <span class="o">=</span> <span class="s1">&#39;appsi_highs&#39;</span>
+ 
+<span class="kn">import</span> <span class="nn">pyomo.environ</span> <span class="k">as</span> <span class="nn">pyo</span>
+<span class="n">SOLVER</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">SolverFactory</span><span class="p">(</span><span class="n">solver</span><span class="p">)</span>
+ 
+<span class="k">assert</span> <span class="n">SOLVER</span><span class="o">.</span><span class="n">available</span><span class="p">(),</span> <span class="sa">f</span><span class="s2">&quot;Solver </span><span class="si">{</span><span class="n">solver</span><span class="si">}</span><span class="s2"> is not available.&quot;</span>
 </pre></div>
 </div>
 </div>
 </div>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>graphviz<span class="w"> </span>--upgrade
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Requirement already satisfied: graphviz in /Users/jeff/opt/anaconda3/lib/python3.9/site-packages (0.20.1)
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">networkx</span> <span class="k">as</span> <span class="nn">nx</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">pyomo.environ</span> <span class="k">as</span> <span class="nn">pyo</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">io</span>
 </pre></div>
 </div>
 </div>
@@ -580,7 +574,7 @@ <h2>Demonstration of Triangular Arbitrage<a class="headerlink" href="#demonstrat
 <p>Consider the following cross-currency matrix.</p>
 <table class="table">
 <thead>
-<tr class="row-odd"><th class="head text-left"><p>i &lt;- J</p></th>
+<tr class="row-odd"><th class="head text-left"><p>i &lt;– j</p></th>
 <th class="head text-center"><p>USD</p></th>
 <th class="head text-center"><p>EUR</p></th>
 <th class="head text-center"><p>JPY</p></th>
@@ -599,7 +593,7 @@ <h2>Demonstration of Triangular Arbitrage<a class="headerlink" href="#demonstrat
 </tr>
 <tr class="row-even"><td class="text-left"><p>JPY</p></td>
 <td class="text-center"><p>100.0</p></td>
-<td class="text-center"><p>133 1/3</p></td>
+<td class="text-center"><p>133.3</p></td>
 <td class="text-center"><p>1.0</p></td>
 </tr>
 </tbody>
@@ -616,9 +610,7 @@ <h2>Demonstration of Triangular Arbitrage<a class="headerlink" href="#demonstrat
 <p>This data set shows no net cost and no arbitrage for conversion from one currency to another and back again.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-
-<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
     <span class="p">[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mi">100</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">1</span> <span class="o">/</span> <span class="mf">0.0075</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.0075</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]],</span>
     <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;USD&quot;</span><span class="p">,</span> <span class="s2">&quot;EUR&quot;</span><span class="p">,</span> <span class="s2">&quot;JPY&quot;</span><span class="p">],</span>
     <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;USD&quot;</span><span class="p">,</span> <span class="s2">&quot;EUR&quot;</span><span class="p">,</span> <span class="s2">&quot;JPY&quot;</span><span class="p">],</span>
@@ -626,14 +618,15 @@ <h2>Demonstration of Triangular Arbitrage<a class="headerlink" href="#demonstrat
 
 <span class="n">display</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
 
-<span class="c1"># USD -&gt; EUR -&gt; USD</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s2">&quot;USD&quot;</span><span class="p">,</span> <span class="s2">&quot;EUR&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s2">&quot;EUR&quot;</span><span class="p">,</span> <span class="s2">&quot;USD&quot;</span><span class="p">])</span>
-
-<span class="c1"># USD -&gt; JPY -&gt; USD</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s2">&quot;USD&quot;</span><span class="p">,</span> <span class="s2">&quot;JPY&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s2">&quot;JPY&quot;</span><span class="p">,</span> <span class="s2">&quot;USD&quot;</span><span class="p">])</span>
-
-<span class="c1"># EUR -&gt; JPY -&gt; EUR</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s2">&quot;EUR&quot;</span><span class="p">,</span> <span class="s2">&quot;JPY&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s2">&quot;JPY&quot;</span><span class="p">,</span> <span class="s2">&quot;EUR&quot;</span><span class="p">])</span>
+<span class="nb">print</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;Net gain factor of the currency exchange chain USD -&gt; EUR -&gt; USD is: </span><span class="si">{</span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">&#39;USD&#39;</span><span class="p">,</span><span class="w"> </span><span class="s1">&#39;EUR&#39;</span><span class="p">]</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">&#39;EUR&#39;</span><span class="p">,</span><span class="w"> </span><span class="s1">&#39;USD&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span>
+<span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;Net gain factor of the currency exchange chain USD -&gt; JPY -&gt; USD is: </span><span class="si">{</span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">&#39;USD&#39;</span><span class="p">,</span><span class="w"> </span><span class="s1">&#39;JPY&#39;</span><span class="p">]</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">&#39;JPY&#39;</span><span class="p">,</span><span class="w"> </span><span class="s1">&#39;USD&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span>
+<span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;Net gain factor of the currency exchangen USD -&gt; JPY -&gt; USD is: </span><span class="si">{</span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">&#39;EUR&#39;</span><span class="p">,</span><span class="w"> </span><span class="s1">&#39;JPY&#39;</span><span class="p">]</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">&#39;JPY&#39;</span><span class="p">,</span><span class="w"> </span><span class="s1">&#39;EUR&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span>
+<span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -682,9 +675,9 @@ <h2>Demonstration of Triangular Arbitrage<a class="headerlink" href="#demonstrat
     </tr>
   </tbody>
 </table>
-</div></div><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>1.0
-1.0
-1.0
+</div></div><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Net change factor of the transaction chain USD -&gt; EUR -&gt; USD is: 1.0
+Net change factor of the transaction chain USD -&gt; JPY -&gt; USD is: 1.0
+Net change factor of the transaction chain USD -&gt; JPY -&gt; USD is: 1.0
 </pre></div>
 </div>
 </div>
@@ -702,12 +695,14 @@ <h2>Demonstration of Triangular Arbitrage<a class="headerlink" href="#demonstrat
 <span class="n">J</span> <span class="o">=</span> <span class="s2">&quot;JPY&quot;</span>
 <span class="n">K</span> <span class="o">=</span> <span class="s2">&quot;EUR&quot;</span>
 
-<span class="nb">print</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">I</span><span class="p">,</span> <span class="n">K</span><span class="p">]</span> <span class="o">*</span> <span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">K</span><span class="p">,</span> <span class="n">J</span><span class="p">]</span> <span class="o">*</span> <span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">J</span><span class="p">,</span> <span class="n">I</span><span class="p">])</span>
+<span class="nb">print</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;Net gain factor of the currency exchangen USD -&gt; JPY -&gt; EUR -&gt; USD is: </span><span class="si">{</span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">I</span><span class="p">,</span><span class="w"> </span><span class="n">K</span><span class="p">]</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">K</span><span class="p">,</span><span class="w"> </span><span class="n">J</span><span class="p">]</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">J</span><span class="p">,</span><span class="w"> </span><span class="n">I</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span>
+<span class="p">)</span>
 </pre></div>
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>1.5
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Net gain factor of the currency exchangen USD -&gt; JPY -&gt; EUR -&gt; USD is: 1.5
 </pre></div>
 </div>
 </div>
@@ -716,9 +711,9 @@ <h2>Demonstration of Triangular Arbitrage<a class="headerlink" href="#demonstrat
 </section>
 <section id="modeling">
 <h2>Modeling<a class="headerlink" href="#modeling" title="Permalink to this heading">#</a></h2>
-<p>The cross-currency table <span class="math notranslate nohighlight">\(A\)</span> provides exchange rates among currencies. Entry <span class="math notranslate nohighlight">\(a_{i,j}\)</span> in row <span class="math notranslate nohighlight">\(i\)</span>, column <span class="math notranslate nohighlight">\(j\)</span> tells us how many units of currency <span class="math notranslate nohighlight">\(i\)</span> are received in exchange for one unit of currency <span class="math notranslate nohighlight">\(j\)</span>. We’ll use the notation <span class="math notranslate nohighlight">\(a_{i, j} = a_{i\leftarrow j}\)</span> to remind ourselves of this relationship.</p>
+<p>The cross-currency table <span class="math notranslate nohighlight">\(A\)</span> provides exchange rates among currencies. Entry <span class="math notranslate nohighlight">\(a_{i,j}\)</span> in row <span class="math notranslate nohighlight">\(i\)</span>, column <span class="math notranslate nohighlight">\(j\)</span> tells us how many units of currency <span class="math notranslate nohighlight">\(i\)</span> are received in exchange for one unit of currency <span class="math notranslate nohighlight">\(j\)</span>. We use the notation <span class="math notranslate nohighlight">\(a_{i, j} = a_{i\leftarrow j}\)</span> to remind ourselves of this relationship.</p>
 <p>We start with <span class="math notranslate nohighlight">\(w_j(0)\)</span> units of currency <span class="math notranslate nohighlight">\(j \in N\)</span>, where <span class="math notranslate nohighlight">\(N\)</span> is the set of all currencies in the data set. We consider a sequence of trades <span class="math notranslate nohighlight">\(t = 1, 2, \ldots, T\)</span> where <span class="math notranslate nohighlight">\(w_j(t)\)</span> is the amount of currency <span class="math notranslate nohighlight">\(j\)</span> on hand after completing trade <span class="math notranslate nohighlight">\(t\)</span>.</p>
-<p>Each trade is executed in two phases. In the first phase an amount <span class="math notranslate nohighlight">\(x_{i\leftarrow j}(t)\)</span>  of currency <span class="math notranslate nohighlight">\(j\)</span> is committed for exchange to currency <span class="math notranslate nohighlight">\(i\)</span>. This allows a trade to include multiple currency transactions. After the commitment the unencumbered balance for currency <span class="math notranslate nohighlight">\(j\)</span> must satisfy trading constraints. Each trade consists of simultaneous transactions in one or more currencies.</p>
+<p>Each trade is executed in two phases. In the first phase an amount <span class="math notranslate nohighlight">\(x_{i\leftarrow j}(t)\)</span> of currency <span class="math notranslate nohighlight">\(j\)</span> is committed for exchange to currency <span class="math notranslate nohighlight">\(i\)</span>. This allows a trade to include multiple currency transactions. After the commitment the unencumbered balance for currency <span class="math notranslate nohighlight">\(j\)</span> must satisfy trading constraints. Each trade consists of simultaneous transactions in one or more currencies.</p>
 <div class="math notranslate nohighlight">
 \[w_j(t-1) - \sum_{i\ne j} x_{i\leftarrow j}(t) \geq 0\]</div>
 <p>Here a lower bound has been placed to prohibit short-selling of currency <span class="math notranslate nohighlight">\(j\)</span>. This constraint could be modified if leveraging is allowed on the exchange.</p>
@@ -729,13 +724,8 @@ <h2>Modeling<a class="headerlink" href="#modeling" title="Permalink to this head
 <p>The goal of this calculation is to find a set of transactions <span class="math notranslate nohighlight">\(x_{i\leftarrow j}(t) \geq 0\)</span> to maximize the value of portfolio after a specified number of trades <span class="math notranslate nohighlight">\(T\)</span>.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pyomo.environ</span> <span class="k">as</span> <span class="nn">pyo</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">graphviz</span> <span class="kn">import</span> <span class="n">Digraph</span>
-
-
-<span class="k">def</span> <span class="nf">arbitrage</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="n">df</span><span class="p">,</span> <span class="n">R</span><span class="o">=</span><span class="s2">&quot;EUR&quot;</span><span class="p">):</span>
-    <span class="n">m</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">ConcreteModel</span><span class="p">()</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">arbitrage</span><span class="p">(</span><span class="n">T</span><span class="p">,</span> <span class="n">df</span><span class="p">,</span> <span class="n">R</span><span class="o">=</span><span class="s2">&quot;EUR&quot;</span><span class="p">):</span>
+    <span class="n">m</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">ConcreteModel</span><span class="p">(</span><span class="s2">&quot;Forex Arbitrage&quot;</span><span class="p">)</span>
 
     <span class="c1"># length of trading chain</span>
     <span class="n">m</span><span class="o">.</span><span class="n">T0</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">RangeSet</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">T</span><span class="p">)</span>
@@ -786,54 +776,78 @@ <h2>Modeling<a class="headerlink" href="#modeling" title="Permalink to this head
             <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">ARCS</span><span class="p">:</span>
                 <span class="k">if</span> <span class="n">m</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">t</span><span class="p">]()</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
                     <span class="nb">print</span><span class="p">(</span>
-                        <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">j</span><span class="si">}</span><span class="s2"> -&gt; </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">  Convert </span><span class="si">{</span><span class="n">m</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="w"> </span><span class="n">j</span><span class="p">,</span><span class="w"> </span><span class="n">t</span><span class="p">]()</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">j</span><span class="si">}</span><span class="s2"> to </span><span class="si">{</span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span><span class="o">*</span><span class="n">m</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">,</span><span class="n">t</span><span class="p">]()</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">&quot;</span>
+                        <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">j</span><span class="si">}</span><span class="s2"> -&gt; </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">  Convert </span><span class="si">{</span><span class="n">m</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="w"> </span><span class="n">j</span><span class="p">,</span><span class="w"> </span><span class="n">t</span><span class="p">]()</span><span class="si">:</span><span class="s2">11.5f</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">j</span><span class="si">}</span><span class="s2"> to </span><span class="si">{</span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span><span class="o">*</span><span class="n">m</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">,</span><span class="n">t</span><span class="p">]()</span><span class="si">:</span><span class="s2">11.5f</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">&quot;</span>
                     <span class="p">)</span>
             <span class="nb">print</span><span class="p">()</span>
 
         <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">NODES</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;w[</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">,</span><span class="si">{</span><span class="n">t</span><span class="si">}</span><span class="s2">] = </span><span class="si">{</span><span class="n">m</span><span class="o">.</span><span class="n">w</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="w"> </span><span class="n">t</span><span class="p">]()</span><span class="si">:</span><span class="s2">9.2f</span><span class="si">}</span><span class="s2"> &quot;</span><span class="p">)</span>
+            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;w[</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">,</span><span class="si">{</span><span class="n">t</span><span class="si">}</span><span class="s2">] = </span><span class="si">{</span><span class="nb">abs</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">w</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="w"> </span><span class="n">t</span><span class="p">]())</span><span class="si">:</span><span class="s2">11.5f</span><span class="si">}</span><span class="s2"> &quot;</span><span class="p">)</span>
 
     <span class="k">return</span> <span class="n">m</span>
 
 
 <span class="n">m</span> <span class="o">=</span> <span class="n">arbitrage</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">df</span><span class="p">,</span> <span class="s2">&quot;EUR&quot;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">w</span><span class="p">[</span><span class="s2">&quot;EUR&quot;</span><span class="p">,</span> <span class="mi">0</span><span class="p">]())</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">w</span><span class="p">[</span><span class="s2">&quot;EUR&quot;</span><span class="p">,</span> <span class="mi">3</span><span class="p">]())</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Initial wealth </span><span class="si">{</span><span class="n">m</span><span class="o">.</span><span class="n">w</span><span class="p">[</span><span class="s1">&#39;EUR&#39;</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">]()</span><span class="si">:</span><span class="s2">11.5f</span><span class="si">}</span><span class="s2"> EUR&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Final wealth </span><span class="si">{</span><span class="n">m</span><span class="o">.</span><span class="n">w</span><span class="p">[</span><span class="s1">&#39;EUR&#39;</span><span class="p">,</span><span class="w"> </span><span class="mi">3</span><span class="p">]()</span><span class="si">:</span><span class="s2">11.5f</span><span class="si">}</span><span class="s2"> EUR&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>t = 0
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Running HiGHS 1.5.3 [date: 2023-05-16, git hash: 594fa5a9d]
+Copyright (c) 2023 HiGHS under MIT licence terms
+
+t = 0
 
-w[USD,0] =      0.00 
-w[EUR,0] =    100.00 
-w[JPY,0] =      0.00 
+w[USD,0] =     0.00000 
+w[EUR,0] =   100.00000 
+w[JPY,0] =     0.00000 
+w[GBP,0] =     0.00000 
+w[CHF,0] =     0.00000 
+w[CAD,0] =     0.00000 
+w[AUD,0] =     0.00000 
+w[HKD,0] =     0.00000 
 
 t = 1
 
-EUR -&gt; USD  Convert 100.0 EUR to 200.0 USD
+EUR -&gt; JPY  Convert   100.00000 EUR to 13160.97000 JPY
 
-w[USD,1] =    200.00 
-w[EUR,1] =     -0.00 
-w[JPY,1] =      0.00 
+w[USD,1] =     0.00000 
+w[EUR,1] =     0.00000 
+w[JPY,1] = 13160.97000 
+w[GBP,1] =     0.00000 
+w[CHF,1] =     0.00000 
+w[CAD,1] =     0.00000 
+w[AUD,1] =     0.00000 
+w[HKD,1] =     0.00000 
 
 t = 2
 
-USD -&gt; JPY  Convert 200.0 USD to 20000.0 JPY
+JPY -&gt; CAD  Convert 13160.97000 JPY to   140.82238 CAD
 
-w[USD,2] =      0.00 
-w[EUR,2] =     -0.00 
-w[JPY,2] =  20000.00 
+w[USD,2] =     0.00000 
+w[EUR,2] =     0.00000 
+w[JPY,2] =     0.00000 
+w[GBP,2] =     0.00000 
+w[CHF,2] =     0.00000 
+w[CAD,2] =   140.82238 
+w[AUD,2] =     0.00000 
+w[HKD,2] =     0.00000 
 
 t = 3
 
-JPY -&gt; EUR  Convert 20000.0 JPY to 150.0 EUR
+CAD -&gt; EUR  Convert   140.82238 CAD to   100.44860 EUR
 
-w[USD,3] =      0.00 
-w[EUR,3] =    150.00 
-w[JPY,3] =     -0.00 
-100.0
-150.0
+w[USD,3] =     0.00000 
+w[EUR,3] =   100.44860 
+w[JPY,3] =     0.00000 
+w[GBP,3] =     0.00000 
+w[CHF,3] =     0.00000 
+w[CAD,3] =     0.00000 
+w[AUD,3] =     0.00000 
+w[HKD,3] =     0.00000 
+
+Initial wealth   100.00000 EUR
+Final wealth   100.44860 EUR
 </pre></div>
 </div>
 </div>
@@ -843,16 +857,13 @@ <h2>Modeling<a class="headerlink" href="#modeling" title="Permalink to this head
 <h2>Display graph<a class="headerlink" href="#display-graph" title="Permalink to this heading">#</a></h2>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">networkx</span> <span class="k">as</span> <span class="nn">nx</span>
-
-
-<span class="k">def</span> <span class="nf">display_graph</span><span class="p">(</span><span class="n">m</span><span class="p">):</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">display_graph</span><span class="p">(</span><span class="n">m</span><span class="p">):</span>
     <span class="n">path</span> <span class="o">=</span> <span class="p">[]</span>
 
     <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">T0</span><span class="p">:</span>
         <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">NODES</span><span class="p">:</span>
             <span class="k">if</span> <span class="n">m</span><span class="o">.</span><span class="n">w</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">t</span><span class="p">]()</span> <span class="o">&gt;=</span> <span class="mf">1e-6</span><span class="p">:</span>
-                <span class="n">path</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">m</span><span class="o">.</span><span class="n">w</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="w"> </span><span class="n">t</span><span class="p">]()</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+                <span class="n">path</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">m</span><span class="o">.</span><span class="n">w</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="w"> </span><span class="n">t</span><span class="p">]()</span><span class="si">:</span><span class="s2">11.5f</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
     <span class="n">path</span> <span class="o">=</span> <span class="s2">&quot; -&gt; &quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
     <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">path</span><span class="p">)</span>
 
@@ -893,10 +904,10 @@ <h2>Display graph<a class="headerlink" href="#display-graph" title="Permalink to
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 100.0 EUR -&gt; 200.0 USD -&gt; 20000.0 JPY -&gt; 150.0 EUR
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>   100.00000 USD -&gt; 11861.00000 JPY -&gt;   126.91270 CAD -&gt;   100.45140 USD
 </pre></div>
 </div>
-<img alt="../../_images/9db164a2203ffd8df06aee727cd554773e10cb2d6f248b489fa647103756fd3b.png" src="../../_images/9db164a2203ffd8df06aee727cd554773e10cb2d6f248b489fa647103756fd3b.png" />
+<img alt="../../_images/22a1af045b511be0444ab4ae34975512955298840c4cfe0a565befc0a05750f6.png" src="../../_images/22a1af045b511be0444ab4ae34975512955298840c4cfe0a565befc0a05750f6.png" />
 </div>
 </div>
 </section>
@@ -920,9 +931,6 @@ <h2>FOREX data<a class="headerlink" href="#forex-data" title="Permalink to this
 <span class="s2">HKD	7.8175	8.6743	0.0659	10.2784	8.3467	6.1877	5.7662	-</span>
 <span class="s2">&quot;&quot;&quot;</span>
 
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">import</span> <span class="nn">io</span>
-
 <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">io</span><span class="o">.</span><span class="n">StringIO</span><span class="p">(</span><span class="n">bloomberg</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;-&quot;</span><span class="p">,</span> <span class="s2">&quot;1.0&quot;</span><span class="p">)),</span> <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">index_col</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
 <span class="n">display</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
 </pre></div>
@@ -1053,61 +1061,67 @@ <h2>FOREX data<a class="headerlink" href="#forex-data" title="Permalink to this
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">m</span> <span class="o">=</span> <span class="n">arbitrage</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">df</span><span class="p">,</span> <span class="s2">&quot;USD&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Initial wealth: </span><span class="si">{</span><span class="n">m</span><span class="o">.</span><span class="n">w</span><span class="p">[</span><span class="s1">&#39;USD&#39;</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">]()</span><span class="si">:</span><span class="s2">11.5f</span><span class="si">}</span><span class="s2"> USD&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Final wealth:   </span><span class="si">{</span><span class="n">m</span><span class="o">.</span><span class="n">w</span><span class="p">[</span><span class="s1">&#39;USD&#39;</span><span class="p">,</span><span class="w"> </span><span class="mi">3</span><span class="p">]()</span><span class="si">:</span><span class="s2">11.5f</span><span class="si">}</span><span class="s2"> USD&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>t = 0
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Running HiGHS 1.5.3 [date: 2023-05-16, git hash: 594fa5a9d]
+Copyright (c) 2023 HiGHS under MIT licence terms
 
-w[USD,0] =    100.00 
-w[EUR,0] =      0.00 
-w[JPY,0] =      0.00 
-w[GBP,0] =      0.00 
-w[CHF,0] =      0.00 
-w[CAD,0] =      0.00 
-w[AUD,0] =      0.00 
-w[HKD,0] =      0.00 
+t = 0
+
+w[USD,0] =   100.00000 
+w[EUR,0] =     0.00000 
+w[JPY,0] =     0.00000 
+w[GBP,0] =     0.00000 
+w[CHF,0] =     0.00000 
+w[CAD,0] =     0.00000 
+w[AUD,0] =     0.00000 
+w[HKD,0] =     0.00000 
 
 t = 1
 
-USD -&gt; JPY  Convert 100.0 USD to 11861.0 JPY
+USD -&gt; JPY  Convert   100.00000 USD to 11861.00000 JPY
 
-w[USD,1] =      0.00 
-w[EUR,1] =      0.00 
-w[JPY,1] =  11861.00 
-w[GBP,1] =      0.00 
-w[CHF,1] =      0.00 
-w[CAD,1] =      0.00 
-w[AUD,1] =      0.00 
-w[HKD,1] =      0.00 
+w[USD,1] =     0.00000 
+w[EUR,1] =     0.00000 
+w[JPY,1] = 11861.00000 
+w[GBP,1] =     0.00000 
+w[CHF,1] =     0.00000 
+w[CAD,1] =     0.00000 
+w[AUD,1] =     0.00000 
+w[HKD,1] =     0.00000 
 
 t = 2
 
-USD -&gt; JPY  Convert 1.0527111e-13 USD to 1.2486206357100001e-11 JPY
-JPY -&gt; CAD  Convert 11861.0 JPY to 126.91269999999999 CAD
+JPY -&gt; CAD  Convert 11861.00000 JPY to   126.91270 CAD
 
-w[USD,2] =      0.00 
-w[EUR,2] =      0.00 
-w[JPY,2] =      0.00 
-w[GBP,2] =     -0.00 
-w[CHF,2] =      0.00 
-w[CAD,2] =    126.91 
-w[AUD,2] =      0.00 
-w[HKD,2] =      0.00 
+w[USD,2] =     0.00000 
+w[EUR,2] =     0.00000 
+w[JPY,2] =     0.00000 
+w[GBP,2] =     0.00000 
+w[CHF,2] =     0.00000 
+w[CAD,2] =   126.91270 
+w[AUD,2] =     0.00000 
+w[HKD,2] =     0.00000 
 
 t = 3
 
-CAD -&gt; USD  Convert 126.9127 CAD to 100.45140205 USD
-JPY -&gt; EUR  Convert 1.1066836e-11 JPY to 8.41079536e-14 EUR
-
-w[USD,3] =    100.45 
-w[EUR,3] =      0.00 
-w[JPY,3] =      0.00 
-w[GBP,3] =      0.00 
-w[CHF,3] =      0.00 
-w[CAD,3] =      0.00 
-w[AUD,3] =      0.00 
-w[HKD,3] =      0.00 
+CAD -&gt; USD  Convert   126.91270 CAD to   100.45140 USD
+
+w[USD,3] =   100.45140 
+w[EUR,3] =     0.00000 
+w[JPY,3] =     0.00000 
+w[GBP,3] =     0.00000 
+w[CHF,3] =     0.00000 
+w[CAD,3] =     0.00000 
+w[AUD,3] =     0.00000 
+w[HKD,3] =     0.00000 
+
+Initial wealth:   100.00000 USD
+Final wealth:     100.45140 USD
 </pre></div>
 </div>
 </div>
@@ -1119,10 +1133,10 @@ <h2>FOREX data<a class="headerlink" href="#forex-data" title="Permalink to this
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 100.0 USD -&gt; 11861.0 JPY -&gt; 126.9127 CAD -&gt; 100.4514 USD
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>   100.00000 USD -&gt; 11861.00000 JPY -&gt;   126.91270 CAD -&gt;   100.45140 USD
 </pre></div>
 </div>
-<img alt="../../_images/3198c9a8b76f0d43f62a6e7c6a7f464325fbd4744630e52105f22e62fe7a7e83.png" src="../../_images/3198c9a8b76f0d43f62a6e7c6a7f464325fbd4744630e52105f22e62fe7a7e83.png" />
+<img alt="../../_images/b9187d2fa7e1a69c76e00a4d54789ade5864f3af768dfb79fcf4561451d736f5.png" src="../../_images/b9187d2fa7e1a69c76e00a4d54789ade5864f3af768dfb79fcf4561451d736f5.png" />
 </div>
 </div>
 </section>
diff --git a/notebooks/04/gasoline-distribution.html b/notebooks/04/gasoline-distribution.html
index 63463094..eb50a1e3 100644
--- a/notebooks/04/gasoline-distribution.html
+++ b/notebooks/04/gasoline-distribution.html
@@ -74,7 +74,7 @@
     <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/04/gasoline-distribution';</script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="Scheduling as a graph coloring problem" href="graph-coloring.html" />
+    <link rel="next" title="Exam room scheduling" href="graph-coloring.html" />
     <link rel="prev" title="Minimum-Cost Flow Problem" href="mincost-flow.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
@@ -216,13 +216,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2 current active"><a class="current reference internal" href="#">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -2180,7 +2180,7 @@ <h3>Graphviz<a class="headerlink" href="#graphviz" title="Permalink to this head
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title">Scheduling as a graph coloring problem</p>
+        <p class="prev-next-title">Exam room scheduling</p>
       </div>
       <i class="fa-solid fa-angle-right"></i>
     </a>
diff --git a/notebooks/04/graph-coloring.html b/notebooks/04/graph-coloring.html
index 11c8045a..47233839 100644
--- a/notebooks/04/graph-coloring.html
+++ b/notebooks/04/graph-coloring.html
@@ -9,7 +9,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
-    <title>Scheduling as a graph coloring problem &#8212; Companion code for the book &#34;Hands-On Optimization with Python&#34;</title>
+    <title>Exam room scheduling &#8212; Companion code for the book &#34;Hands-On Optimization with Python&#34;</title>
   
   
   
@@ -216,13 +216,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2 current active"><a class="current reference internal" href="#">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -491,7 +491,7 @@
               
 
 <div id="jb-print-docs-body" class="onlyprint">
-    <h1>Scheduling as a graph coloring problem</h1>
+    <h1>Exam room scheduling</h1>
     <!-- Table of contents -->
     <div id="print-main-content">
         <div id="jb-print-toc">
@@ -515,8 +515,8 @@ <h2> Contents </h2>
 <div id="searchbox"></div>
                 <article class="bd-article" role="main">
                   
-  <span class="target" id="index-0"></span><span class="target" id="index-1"></span><span class="target" id="index-2"></span><span class="target" id="index-3"></span><section class="tex2jax_ignore mathjax_ignore" id="scheduling-as-a-graph-coloring-problem">
-<span id="index-4"></span><h1>Scheduling as a graph coloring problem<a class="headerlink" href="#scheduling-as-a-graph-coloring-problem" title="Permalink to this heading">#</a></h1>
+  <span class="target" id="index-0"></span><span class="target" id="index-1"></span><span class="target" id="index-2"></span><span class="target" id="index-3"></span><section class="tex2jax_ignore mathjax_ignore" id="exam-room-scheduling">
+<span id="index-4"></span><h1>Exam room scheduling<a class="headerlink" href="#exam-room-scheduling" title="Permalink to this heading">#</a></h1>
 <section id="preamble-install-pyomo-and-a-solver">
 <h2>Preamble: Install Pyomo and a solver<a class="headerlink" href="#preamble-install-pyomo-and-a-solver" title="Permalink to this heading">#</a></h2>
 <p>This cell selects and verifies a global SOLVER for the notebook. If run on Google Colab, the cell installs Pyomo and HiGHS.</p>
diff --git a/notebooks/04/mincost-flow.html b/notebooks/04/mincost-flow.html
index 35f2f857..0a755a3d 100644
--- a/notebooks/04/mincost-flow.html
+++ b/notebooks/04/mincost-flow.html
@@ -216,13 +216,13 @@
 
 <li class="toctree-l2 current active"><a class="current reference internal" href="#">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
diff --git a/notebooks/04/power-network.html b/notebooks/04/power-network.html
index 6ad9d1d0..ca664222 100644
--- a/notebooks/04/power-network.html
+++ b/notebooks/04/power-network.html
@@ -74,7 +74,7 @@
     <script>DOCUMENTATION_OPTIONS.pagename = 'notebooks/04/power-network';</script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="Forex Arbitrage" href="forex-arbitrage.html" />
+    <link rel="next" title="Extra material: Forex Arbitrage" href="forex-arbitrage.html" />
     <link rel="prev" title="Cryptocurrency arbitrage search" href="cryptocurrency-arbitrage.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
@@ -216,13 +216,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2 current active"><a class="current reference internal" href="#">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -1781,7 +1781,7 @@ <h1>Comparing the three models<a class="headerlink" href="#comparing-the-three-m
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title">Forex Arbitrage</p>
+        <p class="prev-next-title">Extra material: Forex Arbitrage</p>
       </div>
       <i class="fa-solid fa-angle-right"></i>
     </a>
diff --git a/notebooks/05/05.00.html b/notebooks/05/05.00.html
index 38fc18f0..bf95df20 100644
--- a/notebooks/05/05.00.html
+++ b/notebooks/05/05.00.html
@@ -73,7 +73,7 @@
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
     <link rel="next" title="Milk pooling and blending" href="milk-pooling.html" />
-    <link rel="prev" title="Forex Arbitrage" href="../04/forex-arbitrage.html" />
+    <link rel="prev" title="Extra material: Forex Arbitrage" href="../04/forex-arbitrage.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
   </head>
@@ -214,13 +214,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="current reference internal" href="#">5. Convex Optimization</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
@@ -524,7 +524,7 @@ <h1>5. Convex Optimization<a class="headerlink" href="#convex-optimization" titl
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title">Forex Arbitrage</p>
+        <p class="prev-next-title">Extra material: Forex Arbitrage</p>
       </div>
     </a>
     <a class="right-next"
diff --git a/notebooks/05/cutting-stock.html b/notebooks/05/cutting-stock.html
index c0aadb03..e9e6b64e 100644
--- a/notebooks/05/cutting-stock.html
+++ b/notebooks/05/cutting-stock.html
@@ -216,13 +216,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="05.00.html">5. Convex Optimization</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
diff --git a/notebooks/05/markowitz_portfolio.html b/notebooks/05/markowitz_portfolio.html
index fcdbc717..2fb7f301 100644
--- a/notebooks/05/markowitz_portfolio.html
+++ b/notebooks/05/markowitz_portfolio.html
@@ -216,13 +216,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="05.00.html">5. Convex Optimization</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
@@ -525,8 +525,7 @@ <h2>Preamble: Install Pyomo and a solver<a class="headerlink" href="#preamble-in
 SolverFactory. It then verifies that SOLVER is available.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">sys</span>
-<span class="kn">import</span> <span class="nn">os</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">sys</span><span class="o">,</span> <span class="nn">os</span>
 
 <span class="k">if</span> <span class="s1">&#39;google.colab&#39;</span> <span class="ow">in</span> <span class="n">sys</span><span class="o">.</span><span class="n">modules</span><span class="p">:</span>
     <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>idaes-pse<span class="w"> </span>--pre<span class="w"> </span>&gt;/dev/null<span class="w"> </span><span class="m">2</span>&gt;/dev/null
@@ -588,7 +587,7 @@ <h2>Problem description and model formulation<a class="headerlink" href="#proble
 <p>One can easily show that the quadratic constraint <span class="math notranslate nohighlight">\(x^\top \Sigma x \leq \gamma\)</span> is convex in <span class="math notranslate nohighlight">\(x\)</span> due to the fact that <span class="math notranslate nohighlight">\(\Sigma\)</span> is positive semidefinite, being a covariance matrix. Therefore, the optimization problem is convex. Let us implement it in Pyomo and solve it.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Specify the initial capital, the risk threshold, and the guaranteed return rate. </span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Specify the initial capital, the risk threshold, and the guaranteed return rate.</span>
 <span class="n">C</span> <span class="o">=</span> <span class="mi">1</span>
 <span class="n">gamma</span> <span class="o">=</span> <span class="mi">1</span>
 <span class="n">R</span> <span class="o">=</span> <span class="mf">1.01</span>
@@ -602,14 +601,14 @@ <h2>Problem description and model formulation<a class="headerlink" href="#proble
 <span class="k">assert</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">eigvals</span><span class="p">(</span><span class="n">Sigma</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">)</span>
 
 <span class="c1"># If you want to change the covariance matrix Sigma, ensure you input a semi-definite positive one.</span>
-<span class="c1"># The easiest way to generate a random covariance matrix is first generating a random m x m matrix A </span>
+<span class="c1"># The easiest way to generate a random covariance matrix is first generating a random m x m matrix A</span>
 <span class="c1"># and then taking the matrix A^T A (which is always semi-definite positive)</span>
 <span class="c1"># m = 3</span>
 <span class="c1"># A = np.random.rand(m, m)</span>
 <span class="c1"># Sigma = A.T @ A</span>
 
+
 <span class="k">def</span> <span class="nf">markowitz</span><span class="p">(</span><span class="n">gamma</span><span class="p">,</span> <span class="n">mu</span><span class="p">,</span> <span class="n">Sigma</span><span class="p">):</span>
-    
     <span class="n">model</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">ConcreteModel</span><span class="p">(</span><span class="s2">&quot;Markowitz portfolio optimization&quot;</span><span class="p">)</span>
 
     <span class="n">model</span><span class="o">.</span><span class="n">xtilde</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">domain</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">NonNegativeReals</span><span class="p">)</span>
@@ -628,13 +627,22 @@ <h2>Problem description and model formulation<a class="headerlink" href="#proble
         <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">))</span> <span class="o">+</span> <span class="n">m</span><span class="o">.</span><span class="n">xtilde</span> <span class="o">==</span> <span class="n">C</span>
 
     <span class="n">result</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">SolverFactory</span><span class="p">(</span><span class="n">SOLVER</span><span class="p">)</span><span class="o">.</span><span class="n">solve</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
-    
+
     <span class="k">return</span> <span class="n">result</span><span class="p">,</span> <span class="n">model</span>
 
+
 <span class="n">result</span><span class="p">,</span> <span class="n">model</span> <span class="o">=</span> <span class="n">markowitz</span><span class="p">(</span><span class="n">gamma</span><span class="p">,</span> <span class="n">mu</span><span class="p">,</span> <span class="n">Sigma</span><span class="p">)</span>
 
-<span class="n">display</span><span class="p">(</span><span class="n">Markdown</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;**Solver status:** *</span><span class="si">{</span><span class="n">result</span><span class="o">.</span><span class="n">solver</span><span class="o">.</span><span class="n">status</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">result</span><span class="o">.</span><span class="n">solver</span><span class="o">.</span><span class="n">termination_condition</span><span class="si">}</span><span class="s2">*&quot;</span><span class="p">))</span>
-<span class="n">display</span><span class="p">(</span><span class="n">Markdown</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;**Solution:** $</span><span class="se">\\</span><span class="s2">tilde x = </span><span class="si">{</span><span class="n">model</span><span class="o">.</span><span class="n">xtilde</span><span class="o">.</span><span class="n">value</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2">$, $x_1 = </span><span class="si">{</span><span class="n">model</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">value</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2">$,  $x_2 = </span><span class="si">{</span><span class="n">model</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">value</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2">$,  $x_3 = </span><span class="si">{</span><span class="n">model</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">value</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2">$&quot;</span><span class="p">))</span>
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">Markdown</span><span class="p">(</span>
+        <span class="sa">f</span><span class="s2">&quot;**Solver status:** *</span><span class="si">{</span><span class="n">result</span><span class="o">.</span><span class="n">solver</span><span class="o">.</span><span class="n">status</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">result</span><span class="o">.</span><span class="n">solver</span><span class="o">.</span><span class="n">termination_condition</span><span class="si">}</span><span class="s2">*&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+<span class="n">display</span><span class="p">(</span>
+    <span class="n">Markdown</span><span class="p">(</span>
+        <span class="sa">f</span><span class="s2">&quot;**Solution:** $</span><span class="se">\\</span><span class="s2">tilde x = </span><span class="si">{</span><span class="n">model</span><span class="o">.</span><span class="n">xtilde</span><span class="o">.</span><span class="n">value</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2">$, $x_1 = </span><span class="si">{</span><span class="n">model</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">value</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2">$,  $x_2 = </span><span class="si">{</span><span class="n">model</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">value</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2">$,  $x_3 = </span><span class="si">{</span><span class="n">model</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">value</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2">$&quot;</span>
+    <span class="p">)</span>
+<span class="p">)</span>
 <span class="n">display</span><span class="p">(</span><span class="n">Markdown</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;**Maximizes objective value to:** $</span><span class="si">{</span><span class="n">model</span><span class="o">.</span><span class="n">objective</span><span class="p">()</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">$&quot;</span><span class="p">))</span>
 </pre></div>
 </div>
@@ -648,17 +656,59 @@ <h2>Problem description and model formulation<a class="headerlink" href="#proble
 <p>The next plot shows how the objective function changes as the risk threshold parameter <span class="math notranslate nohighlight">\(\gamma\)</span> varies.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">gamma_values</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.005</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.02</span><span class="p">,</span> <span class="mf">0.03</span><span class="p">,</span> <span class="mf">0.04</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">,</span> <span class="mf">0.06</span><span class="p">,</span> <span class="mf">0.07</span><span class="p">,</span> <span class="mf">0.08</span><span class="p">,</span> <span class="mf">0.09</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.11</span><span class="p">,</span> <span class="mf">0.12</span><span class="p">,</span> <span class="mf">0.13</span><span class="p">,</span> <span class="mf">0.14</span><span class="p">,</span> <span class="mf">0.15</span><span class="p">,</span> <span class="mf">0.16</span><span class="p">,</span> <span class="mf">0.17</span><span class="p">,</span> <span class="mf">0.18</span><span class="p">,</span> <span class="mf">0.19</span><span class="p">,</span> <span class="mf">0.20</span><span class="p">,</span> <span class="mf">0.25</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.6</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">1.5</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mf">2.5</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mf">3.5</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mf">4.5</span><span class="p">,</span> <span class="mf">4.75</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mf">5.25</span><span class="p">,</span> <span class="mf">5.5</span><span class="p">]</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">gamma_values</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="mf">0.005</span><span class="p">,</span>
+    <span class="mf">0.01</span><span class="p">,</span>
+    <span class="mf">0.02</span><span class="p">,</span>
+    <span class="mf">0.03</span><span class="p">,</span>
+    <span class="mf">0.04</span><span class="p">,</span>
+    <span class="mf">0.05</span><span class="p">,</span>
+    <span class="mf">0.06</span><span class="p">,</span>
+    <span class="mf">0.07</span><span class="p">,</span>
+    <span class="mf">0.08</span><span class="p">,</span>
+    <span class="mf">0.09</span><span class="p">,</span>
+    <span class="mf">0.1</span><span class="p">,</span>
+    <span class="mf">0.11</span><span class="p">,</span>
+    <span class="mf">0.12</span><span class="p">,</span>
+    <span class="mf">0.13</span><span class="p">,</span>
+    <span class="mf">0.14</span><span class="p">,</span>
+    <span class="mf">0.15</span><span class="p">,</span>
+    <span class="mf">0.16</span><span class="p">,</span>
+    <span class="mf">0.17</span><span class="p">,</span>
+    <span class="mf">0.18</span><span class="p">,</span>
+    <span class="mf">0.19</span><span class="p">,</span>
+    <span class="mf">0.20</span><span class="p">,</span>
+    <span class="mf">0.25</span><span class="p">,</span>
+    <span class="mf">0.3</span><span class="p">,</span>
+    <span class="mf">0.4</span><span class="p">,</span>
+    <span class="mf">0.5</span><span class="p">,</span>
+    <span class="mf">0.6</span><span class="p">,</span>
+    <span class="mf">0.7</span><span class="p">,</span>
+    <span class="mf">0.8</span><span class="p">,</span>
+    <span class="mf">0.9</span><span class="p">,</span>
+    <span class="mi">1</span><span class="p">,</span>
+    <span class="mf">1.5</span><span class="p">,</span>
+    <span class="mi">2</span><span class="p">,</span>
+    <span class="mf">2.5</span><span class="p">,</span>
+    <span class="mi">3</span><span class="p">,</span>
+    <span class="mf">3.5</span><span class="p">,</span>
+    <span class="mi">4</span><span class="p">,</span>
+    <span class="mf">4.5</span><span class="p">,</span>
+    <span class="mf">4.75</span><span class="p">,</span>
+    <span class="mi">5</span><span class="p">,</span>
+    <span class="mf">5.25</span><span class="p">,</span>
+    <span class="mf">5.5</span><span class="p">,</span>
+<span class="p">]</span>
 <span class="n">objective</span> <span class="o">=</span> <span class="p">[]</span>
 
-<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="o">.</span><span class="n">update</span><span class="p">({</span><span class="s1">&#39;font.size&#39;</span><span class="p">:</span> <span class="mi">14</span><span class="p">})</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="o">.</span><span class="n">update</span><span class="p">({</span><span class="s2">&quot;font.size&quot;</span><span class="p">:</span> <span class="mi">14</span><span class="p">})</span>
 <span class="k">for</span> <span class="n">gamma</span> <span class="ow">in</span> <span class="n">gamma_values</span><span class="p">:</span>
     <span class="n">_</span><span class="p">,</span> <span class="n">model</span> <span class="o">=</span> <span class="n">markowitz</span><span class="p">(</span><span class="n">gamma</span><span class="p">,</span> <span class="n">mu</span><span class="p">,</span> <span class="n">Sigma</span><span class="p">)</span>
-    <span class="n">objective</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">objective</span><span class="p">(),</span><span class="mi">3</span><span class="p">))</span>
+    <span class="n">objective</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">objective</span><span class="p">(),</span> <span class="mi">3</span><span class="p">))</span>
 
 <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">gamma_values</span><span class="p">,</span> <span class="n">objective</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">tab20c</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;Risk threshold $\gamma$&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Optimal objective value&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s2">&quot;Risk threshold $\gamma$&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Optimal objective value&quot;</span><span class="p">)</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
diff --git a/notebooks/05/milk-pooling.html b/notebooks/05/milk-pooling.html
index aa2ceb9c..04834fb1 100644
--- a/notebooks/05/milk-pooling.html
+++ b/notebooks/05/milk-pooling.html
@@ -216,13 +216,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="05.00.html">5. Convex Optimization</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
@@ -501,7 +501,7 @@ <h2> Contents </h2>
             </div>
             <nav aria-label="Page">
                 <ul class="visible nav section-nav flex-column">
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#problem-pooling-milk-for-wholesale-blending-and-distribution">Problem: Pooling milk for wholesale blending and distribution</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#problem-description-pooling-milk-for-wholesale-blending-and-distribution">Problem description: Pooling milk for wholesale blending and distribution</a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#option-1-business-as-usual">Option 1. Business as usual</a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#option-2-buy-an-additional-truck">Option 2. Buy an additional truck</a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#option-3-pool-delivery-from-remote-suppliers">Option 3. Pool delivery from remote suppliers</a><ul class="nav section-nav flex-column">
@@ -510,9 +510,8 @@ <h2> Contents </h2>
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#convex-approximation">Convex Approximation</a></li>
 </ul>
 </li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#global-nonlinear-optimization-gnlo-solution-with-couenne">Global Nonlinear Optimization (GNLO) solution with Couenne</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#nonlinear-optimization-nlo-solution-with-ipopt">Nonlinear Optimization (NLO) solution with ipopt</a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#concluding-remarks">Concluding Remarks</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#suggested-exercises">Suggested Exercises</a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#bibliographic-notes">Bibliographic Notes</a></li>
 </ul>
             </nav>
@@ -528,20 +527,25 @@ <h2> Contents </h2>
   <span class="target" id="index-0"></span><span class="target" id="index-1"></span><span class="target" id="index-2"></span><span class="target" id="index-3"></span><span class="target" id="index-4"></span><span class="target" id="index-5"></span><section class="tex2jax_ignore mathjax_ignore" id="milk-pooling-and-blending">
 <span id="index-6"></span><h1>Milk pooling and blending<a class="headerlink" href="#milk-pooling-and-blending" title="Permalink to this heading">#</a></h1>
 <p>Pooling and blending operations involve the “pooling” of various streams to create intermediate mixtures that are subsequently blended with other streams to meet final product specifications. These operations are common to the chemical processing and petroleum sectors where limited tankage may be available, or when it is necessary to transport materials by train, truck, or pipeline to remote blending terminals. Similar applications arise in agriculture, food, mining, wastewater treatment, and other industries.</p>
-<p>This notebook considers a simple example of a wholesale milk distributor to show how <strong>non-convexity</strong> arises in the optimization of pooling and blending operations. Non-convexity is due to presence of <strong>bilinear</strong> terms that are the product of two decision variables where one is a scale-dependent <strong>extensive</strong> quantity measuring the amount or flow of a product, and the other is scale-independent <strong>intensive</strong> quantity such as product composition. The notebook then shows how to develop and solve a convex approximation of the problem, and finally demonstrates solution the use of <a class="reference external" href="https://github.com/coin-or/Couenne">Couenne</a>, a solver specifically designed to find global solutions to mixed integer nonlinear optimization (MINLO) problems.</p>
+<p>This notebook considers a simple example of a wholesale milk distributor to show how <strong>non-convexity</strong> arises in the optimization of pooling and blending operations. Non-convexity is due to presence of <strong>bilinear</strong> terms that are the product of two decision variables where one is a scale-dependent <strong>extensive</strong> quantity measuring the amount or flow of a product, and the other is scale-independent <strong>intensive</strong> quantity such as product composition. The notebook then shows how to develop and solve a convex approximation of the problem, and finally demonstrates solution the use of <code class="docutils literal notranslate"><span class="pre">ipotp</span></code>, a solver specifically designed to find global solutions to nonlinear optimization (NLO) problems.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># install Pyomo and solvers</span>
-<span class="kn">import</span> <span class="nn">sys</span>
-<span class="kn">import</span> <span class="nn">os</span>
-
-<span class="n">SOLVER_LO</span> <span class="o">=</span> <span class="s2">&quot;cbc&quot;</span>
-<span class="n">SOLVER_GNLO</span> <span class="o">=</span> <span class="s2">&quot;couenne&quot;</span>
-
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">sys</span><span class="o">,</span> <span class="nn">os</span>
+ 
 <span class="k">if</span> <span class="s1">&#39;google.colab&#39;</span> <span class="ow">in</span> <span class="n">sys</span><span class="o">.</span><span class="n">modules</span><span class="p">:</span>
     <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>idaes-pse<span class="w"> </span>--pre<span class="w"> </span>&gt;/dev/null<span class="w"> </span><span class="m">2</span>&gt;/dev/null
     <span class="o">!</span>idaes<span class="w"> </span>get-extensions<span class="w"> </span>--to<span class="w"> </span>./bin<span class="w"> </span>
     <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">&#39;PATH&#39;</span><span class="p">]</span> <span class="o">+=</span> <span class="s1">&#39;:bin&#39;</span>
+
+<span class="kn">import</span> <span class="nn">pyomo.environ</span> <span class="k">as</span> <span class="nn">pyo</span>
+
+<span class="n">solver_LO</span> <span class="o">=</span> <span class="s1">&#39;appsi_highs&#39;</span>
+<span class="n">SOLVER_LO</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">SolverFactory</span><span class="p">(</span><span class="n">solver_LO</span><span class="p">)</span>
+<span class="k">assert</span> <span class="n">SOLVER_LO</span><span class="o">.</span><span class="n">available</span><span class="p">(),</span> <span class="sa">f</span><span class="s2">&quot;Solver </span><span class="si">{</span><span class="n">solver_LO</span><span class="si">}</span><span class="s2"> is not available.&quot;</span>
+
+<span class="n">solver_NLO</span> <span class="o">=</span> <span class="s2">&quot;ipopt&quot;</span>
+<span class="n">SOLVER_NLO</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">SolverFactory</span><span class="p">(</span><span class="n">solver_NLO</span><span class="p">)</span>
+<span class="k">assert</span> <span class="n">SOLVER_NLO</span><span class="o">.</span><span class="n">available</span><span class="p">(),</span> <span class="sa">f</span><span class="s2">&quot;Solver </span><span class="si">{</span><span class="n">solver_NLO</span><span class="si">}</span><span class="s2"> is not available.&quot;</span>
 </pre></div>
 </div>
 </div>
@@ -556,35 +560,39 @@ <h2> Contents </h2>
 </div>
 </div>
 </div>
-<section id="problem-pooling-milk-for-wholesale-blending-and-distribution">
-<h2>Problem: Pooling milk for wholesale blending and distribution<a class="headerlink" href="#problem-pooling-milk-for-wholesale-blending-and-distribution" title="Permalink to this heading">#</a></h2>
-<p>A bulk distributor supplies custom blends of milk to several customers. Each customer has specified a minimum fat content, a maximum price, and a maximum amount for the milk they wish to buy. The distributor sources raw milk from local farms. Each farm produces a milk with a known fat content and cost.</p>
-<p>The distributor has recently identified more affordable sources raw milk from several distant farms. These distant farms produce milk grades that can be blend with milk from the local farms. However, the distributor only has one truck with a single tank available for transporting milk from the distant farms. As a result, milk from the distant farms must be combined in the tank before being transported to the blending station. This creates a “pool” of uniform composition which to be blended with local milk to meet customer requirements.</p>
-<p>The process is shown in the following diagram. The fat content and cost of raw milk is given for each farm. For each customer, data is given for the required milk fat content, price, and the maximum demand. The arrows indicate pooling and blending of raw milk supplies. Each arrow is labeled with the an amount of raw milk.</p>
+<section id="problem-description-pooling-milk-for-wholesale-blending-and-distribution">
+<h2>Problem description: Pooling milk for wholesale blending and distribution<a class="headerlink" href="#problem-description-pooling-milk-for-wholesale-blending-and-distribution" title="Permalink to this heading">#</a></h2>
+<p>A bulk distributor supplies custom milk blends to several customers. Each customer has specified a minimum fat content, a maximum price, and a maximum amount of milk they wish to buy. The distributor sources raw milk from local farms. Each farm produces a milk with a known fat content and cost.</p>
+<p>The distributor has recently identified more affordable sources raw milk from some remote farms. These remote farms produce milk grades that can be blended with milk from local farms. However, the distributor only has one truck with a single tank available to transport milk from the remote farms. As a result, milk from the remote farms must be mixed in the tank before being transported to the blending station. This creates a “pool” of uniform composition which to be blended with local milk to meet the customer’s requirements.</p>
+<p>The process is shown in the following diagram. The fat content and cost of raw milk is given for each farm. For each customer, data is provided for the required milk fat content, price, and the maximum demand. Arrows indicate pooling and blending of raw milk supplies. Each arrow is labeled with the amount of raw milk.</p>
 <p><img alt="" src="../../_images/milk-pooling.png" /></p>
 <p>What should the distributor do?</p>
 <ul class="simple">
-<li><p>Option 1. Do nothing. Continue operating the business as usual with local suppliers.</p></li>
+<li><p>Option 1. Do nothing and continue operating the business as usual with local suppliers.</p></li>
 <li><p>Option 2. Buy a second truck to transport raw milk from the remote farms to the blending facility without pooling.</p></li>
 <li><p>Option 3. Pool raw milk from the remote farms into a single truck for transport to the blending facility.</p></li>
 </ul>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">customers</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span>
-    <span class="s2">&quot;Customer 1&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;min_fat&quot;</span><span class="p">:</span> <span class="mf">0.045</span><span class="p">,</span> <span class="s2">&quot;price&quot;</span><span class="p">:</span> <span class="mf">52.0</span><span class="p">,</span> <span class="s2">&quot;demand&quot;</span><span class="p">:</span> <span class="mf">6000.0</span><span class="p">},</span>
-    <span class="s2">&quot;Customer 2&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;min_fat&quot;</span><span class="p">:</span> <span class="mf">0.030</span><span class="p">,</span> <span class="s2">&quot;price&quot;</span><span class="p">:</span> <span class="mf">48.0</span><span class="p">,</span> <span class="s2">&quot;demand&quot;</span><span class="p">:</span> <span class="mf">2500.0</span><span class="p">},</span>
-    <span class="s2">&quot;Customer 3&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;min_fat&quot;</span><span class="p">:</span> <span class="mf">0.040</span><span class="p">,</span> <span class="s2">&quot;price&quot;</span><span class="p">:</span> <span class="mf">50.0</span><span class="p">,</span> <span class="s2">&quot;demand&quot;</span><span class="p">:</span> <span class="mf">4000.0</span><span class="p">},</span>
-<span class="p">})</span><span class="o">.</span><span class="n">T</span>
-
-<span class="n">suppliers</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span>
-    <span class="s2">&quot;Farm A&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;fat&quot;</span><span class="p">:</span> <span class="mf">0.045</span><span class="p">,</span> <span class="s2">&quot;cost&quot;</span><span class="p">:</span> <span class="mf">45.0</span><span class="p">,</span> <span class="s2">&quot;location&quot;</span><span class="p">:</span> <span class="s2">&quot;local&quot;</span><span class="p">},</span>
-    <span class="s2">&quot;Farm B&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;fat&quot;</span><span class="p">:</span> <span class="mf">0.030</span><span class="p">,</span> <span class="s2">&quot;cost&quot;</span><span class="p">:</span> <span class="mf">42.0</span><span class="p">,</span> <span class="s2">&quot;location&quot;</span><span class="p">:</span> <span class="s2">&quot;local&quot;</span><span class="p">},</span>
-    <span class="s2">&quot;Farm C&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;fat&quot;</span><span class="p">:</span> <span class="mf">0.033</span><span class="p">,</span> <span class="s2">&quot;cost&quot;</span><span class="p">:</span> <span class="mf">37.0</span><span class="p">,</span> <span class="s2">&quot;location&quot;</span><span class="p">:</span> <span class="s2">&quot;remote&quot;</span><span class="p">},</span>
-    <span class="s2">&quot;Farm D&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;fat&quot;</span><span class="p">:</span> <span class="mf">0.050</span><span class="p">,</span> <span class="s2">&quot;cost&quot;</span><span class="p">:</span> <span class="mf">45.0</span><span class="p">,</span> <span class="s2">&quot;location&quot;</span><span class="p">:</span> <span class="s2">&quot;remote&quot;</span><span class="p">}},</span>
-    <span class="p">)</span><span class="o">.</span><span class="n">T</span>
-
-<span class="n">local_suppliers</span> <span class="o">=</span> <span class="n">suppliers</span><span class="p">[</span><span class="n">suppliers</span><span class="p">[</span><span class="s2">&quot;location&quot;</span><span class="p">]</span><span class="o">==</span><span class="s2">&quot;local&quot;</span><span class="p">]</span>
-<span class="n">remote_suppliers</span> <span class="o">=</span> <span class="n">suppliers</span><span class="p">[</span><span class="n">suppliers</span><span class="p">[</span><span class="s2">&quot;location&quot;</span><span class="p">]</span><span class="o">==</span><span class="s2">&quot;remote&quot;</span><span class="p">]</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">customers</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
+    <span class="p">{</span>
+        <span class="s2">&quot;Customer 1&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;min_fat&quot;</span><span class="p">:</span> <span class="mf">0.045</span><span class="p">,</span> <span class="s2">&quot;price&quot;</span><span class="p">:</span> <span class="mf">52.0</span><span class="p">,</span> <span class="s2">&quot;demand&quot;</span><span class="p">:</span> <span class="mf">6000.0</span><span class="p">},</span>
+        <span class="s2">&quot;Customer 2&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;min_fat&quot;</span><span class="p">:</span> <span class="mf">0.030</span><span class="p">,</span> <span class="s2">&quot;price&quot;</span><span class="p">:</span> <span class="mf">48.0</span><span class="p">,</span> <span class="s2">&quot;demand&quot;</span><span class="p">:</span> <span class="mf">2500.0</span><span class="p">},</span>
+        <span class="s2">&quot;Customer 3&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;min_fat&quot;</span><span class="p">:</span> <span class="mf">0.040</span><span class="p">,</span> <span class="s2">&quot;price&quot;</span><span class="p">:</span> <span class="mf">50.0</span><span class="p">,</span> <span class="s2">&quot;demand&quot;</span><span class="p">:</span> <span class="mf">4000.0</span><span class="p">},</span>
+    <span class="p">}</span>
+<span class="p">)</span><span class="o">.</span><span class="n">T</span>
+
+<span class="n">suppliers</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
+    <span class="p">{</span>
+        <span class="s2">&quot;Farm A&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;fat&quot;</span><span class="p">:</span> <span class="mf">0.045</span><span class="p">,</span> <span class="s2">&quot;cost&quot;</span><span class="p">:</span> <span class="mf">45.0</span><span class="p">,</span> <span class="s2">&quot;location&quot;</span><span class="p">:</span> <span class="s2">&quot;local&quot;</span><span class="p">},</span>
+        <span class="s2">&quot;Farm B&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;fat&quot;</span><span class="p">:</span> <span class="mf">0.030</span><span class="p">,</span> <span class="s2">&quot;cost&quot;</span><span class="p">:</span> <span class="mf">42.0</span><span class="p">,</span> <span class="s2">&quot;location&quot;</span><span class="p">:</span> <span class="s2">&quot;local&quot;</span><span class="p">},</span>
+        <span class="s2">&quot;Farm C&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;fat&quot;</span><span class="p">:</span> <span class="mf">0.033</span><span class="p">,</span> <span class="s2">&quot;cost&quot;</span><span class="p">:</span> <span class="mf">37.0</span><span class="p">,</span> <span class="s2">&quot;location&quot;</span><span class="p">:</span> <span class="s2">&quot;remote&quot;</span><span class="p">},</span>
+        <span class="s2">&quot;Farm D&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;fat&quot;</span><span class="p">:</span> <span class="mf">0.050</span><span class="p">,</span> <span class="s2">&quot;cost&quot;</span><span class="p">:</span> <span class="mf">45.0</span><span class="p">,</span> <span class="s2">&quot;location&quot;</span><span class="p">:</span> <span class="s2">&quot;remote&quot;</span><span class="p">},</span>
+    <span class="p">},</span>
+<span class="p">)</span><span class="o">.</span><span class="n">T</span>
+
+<span class="n">local_suppliers</span> <span class="o">=</span> <span class="n">suppliers</span><span class="p">[</span><span class="n">suppliers</span><span class="p">[</span><span class="s2">&quot;location&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;local&quot;</span><span class="p">]</span>
+<span class="n">remote_suppliers</span> <span class="o">=</span> <span class="n">suppliers</span><span class="p">[</span><span class="n">suppliers</span><span class="p">[</span><span class="s2">&quot;location&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;remote&quot;</span><span class="p">]</span>
 
 <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Customers&quot;</span><span class="p">)</span>
 <span class="n">display</span><span class="p">(</span><span class="n">customers</span><span class="p">)</span>
@@ -700,7 +708,7 @@ <h2>Problem: Pooling milk for wholesale blending and distribution<a class="heade
 </section>
 <section id="option-1-business-as-usual">
 <h2>Option 1. Business as usual<a class="headerlink" href="#option-1-business-as-usual" title="Permalink to this heading">#</a></h2>
-<p>The normal business of the milk distributor is to blend supplies from local farms to meet customer requirements. Let <span class="math notranslate nohighlight">\(L\)</span> designate the set of local suppliers, and let <span class="math notranslate nohighlight">\(C\)</span> designate the set of customers. Decision variable <span class="math notranslate nohighlight">\(z_{l, c}\)</span> is the amount of milk from local supplier <span class="math notranslate nohighlight">\(l\in L\)</span> that is mixed into the blend sold to customer <span class="math notranslate nohighlight">\(c\in C\)</span>.</p>
+<p>The normal business of the milk distributor is to blend supplies from local farms to meet customer requirements. Let <span class="math notranslate nohighlight">\(L = \{ A, B \}\)</span> designate the set of local suppliers, and let <span class="math notranslate nohighlight">\(C = \{1,2,3\}\)</span> designate the set of customers. Decision variable <span class="math notranslate nohighlight">\(z_{l, c}\)</span> is the amount of milk from local supplier <span class="math notranslate nohighlight">\(l\in L\)</span> that is mixed into the blend sold to customer <span class="math notranslate nohighlight">\(c\in C\)</span>.</p>
 <p>The distributor’s objectives is to maximize profit</p>
 <div class="math notranslate nohighlight">
 \[
@@ -716,48 +724,62 @@ <h2>Option 1. Business as usual<a class="headerlink" href="#option-1-business-as
 \sum_{l\in L} z_{l, c} &amp; \leq \text{demand}_{c} &amp; \forall c\in C
 \end{align*}
 \]</div>
-<p>Let <span class="math notranslate nohighlight">\(\text{fat}_l\)</span> denote the fat content of the raw milke produced by farm <span class="math notranslate nohighlight">\(l\)</span>, and let  <span class="math notranslate nohighlight">\(\text{min_fat}_c\)</span> denote the minimum fat content required by customer <span class="math notranslate nohighlight">\(c\)</span>, respectively. Assuming linear blending, the model becomes</p>
+<p>Let <span class="math notranslate nohighlight">\(\text{fat}_l\)</span> denote the fat content of the raw milk produced by farm <span class="math notranslate nohighlight">\(l\)</span>, and let <span class="math notranslate nohighlight">\(\text{fat}^{\min}_c\)</span> denote the minimum fat content required by customer <span class="math notranslate nohighlight">\(c\)</span>, respectively. Assuming linear blending, the model becomes</p>
 <div class="math notranslate nohighlight">
 \[
 \begin{align*}
-\sum_{(l,c)\ \in\ L \times C} \text{fat}_{l} z_{l,c} &amp; \geq \text{min_fat}_{c} \sum_{l\in L} z_{l, c} &amp; \forall c \in C
+\sum_{(l,c)\ \in\ L \times C} \text{fat}_{l} \cdot z_{l,c} &amp; \geq \text{fat}^{\min}_{c} \sum_{l\in L} z_{l, c} &amp; \forall c \in C
 \end{align*}
 \]</div>
 <p>This is a standard linear blending problem that can be solved by linear optimization (LO).</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">m</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">ConcreteModel</span><span class="p">()</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">m</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">ConcreteModel</span><span class="p">(</span><span class="s2">&quot;Milk pooling v1&quot;</span><span class="p">)</span>
 
 <span class="c1"># define sources and customers</span>
-<span class="n">m</span><span class="o">.</span><span class="n">L</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Set</span><span class="p">(</span><span class="n">initialize</span><span class="o">=</span><span class="n">suppliers</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">suppliers</span><span class="p">[</span><span class="s2">&quot;location&quot;</span><span class="p">]</span><span class="o">==</span><span class="s2">&quot;local&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span>
+<span class="n">m</span><span class="o">.</span><span class="n">L</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Set</span><span class="p">(</span><span class="n">initialize</span><span class="o">=</span><span class="n">suppliers</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">suppliers</span><span class="p">[</span><span class="s2">&quot;location&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;local&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span>
 <span class="n">m</span><span class="o">.</span><span class="n">C</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Set</span><span class="p">(</span><span class="n">initialize</span><span class="o">=</span><span class="n">customers</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span>
 
 <span class="c1"># define local -&gt; customer flowrates</span>
 <span class="n">m</span><span class="o">.</span><span class="n">z</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">L</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">C</span><span class="p">,</span> <span class="n">domain</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">NonNegativeReals</span><span class="p">)</span>
 
+
 <span class="nd">@m</span><span class="o">.</span><span class="n">Objective</span><span class="p">(</span><span class="n">sense</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">maximize</span><span class="p">)</span>
 <span class="k">def</span> <span class="nf">profit</span><span class="p">(</span><span class="n">m</span><span class="p">):</span>
-    <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span><span class="o">*</span><span class="p">(</span><span class="n">customers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="s2">&quot;price&quot;</span><span class="p">]</span> <span class="o">-</span> <span class="n">suppliers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="s2">&quot;cost&quot;</span><span class="p">])</span> <span class="k">for</span> <span class="n">l</span><span class="p">,</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">L</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">C</span><span class="p">)</span>
+    <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span>
+        <span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="n">customers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="s2">&quot;price&quot;</span><span class="p">]</span> <span class="o">-</span> <span class="n">suppliers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="s2">&quot;cost&quot;</span><span class="p">])</span>
+        <span class="k">for</span> <span class="n">l</span><span class="p">,</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">L</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">C</span>
+    <span class="p">)</span>
+
 
 <span class="nd">@m</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">C</span><span class="p">)</span>
 <span class="k">def</span> <span class="nf">demand</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">c</span><span class="p">):</span>
     <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">L</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="n">customers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="s2">&quot;demand&quot;</span><span class="p">]</span>
 
+
 <span class="nd">@m</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">C</span><span class="p">)</span>
 <span class="k">def</span> <span class="nf">fat_content</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">c</span><span class="p">):</span>
-    <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span> <span class="o">*</span> <span class="n">suppliers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="s2">&quot;fat&quot;</span><span class="p">]</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">L</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">L</span><span class="p">)</span> <span class="o">*</span> <span class="n">customers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="s2">&quot;min_fat&quot;</span><span class="p">]</span>
+    <span class="k">return</span> <span class="p">(</span>
+        <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span> <span class="o">*</span> <span class="n">suppliers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="s2">&quot;fat&quot;</span><span class="p">]</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">L</span><span class="p">)</span>
+        <span class="o">&gt;=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">L</span><span class="p">)</span> <span class="o">*</span> <span class="n">customers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="s2">&quot;min_fat&quot;</span><span class="p">]</span>
+    <span class="p">)</span>
 
-<span class="n">pyo</span><span class="o">.</span><span class="n">SolverFactory</span><span class="p">(</span><span class="n">SOLVER_LO</span><span class="p">)</span><span class="o">.</span><span class="n">solve</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
 
-<span class="c1"># report results</span>
+<span class="n">SOLVER_LO</span><span class="o">.</span><span class="n">solve</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
+
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Profit = </span><span class="si">{</span><span class="n">m</span><span class="o">.</span><span class="n">profit</span><span class="p">()</span><span class="si">:</span><span class="s2">0.2f</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
 
-<span class="c1"># create dataframe of results</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Blending Plan&quot;</span><span class="p">)</span>
-<span class="n">Z</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([[</span><span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="nb">round</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">](),</span> <span class="mi">1</span><span class="p">)]</span> <span class="k">for</span> <span class="n">l</span><span class="p">,</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">L</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">C</span><span class="p">],</span> <span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;supplier&quot;</span><span class="p">,</span> <span class="s2">&quot;customer&quot;</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">])</span>
+<span class="n">Z</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
+    <span class="p">[[</span><span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="nb">round</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">](),</span> <span class="mi">1</span><span class="p">)]</span> <span class="k">for</span> <span class="n">l</span><span class="p">,</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">L</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">C</span><span class="p">],</span>
+    <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;supplier&quot;</span><span class="p">,</span> <span class="s2">&quot;customer&quot;</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">],</span>
+<span class="p">)</span>
 <span class="n">Z</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">pivot_table</span><span class="p">(</span><span class="n">index</span><span class="o">=</span><span class="s2">&quot;customer&quot;</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="s2">&quot;supplier&quot;</span><span class="p">)</span>
 <span class="n">Z</span><span class="p">[</span><span class="s2">&quot;Total&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">Z</span><span class="p">[</span><span class="s2">&quot;fat content&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">]()</span> <span class="o">*</span> <span class="n">suppliers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="s2">&quot;fat&quot;</span><span class="p">]</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">L</span><span class="p">)</span><span class="o">/</span><span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">]()</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">L</span><span class="p">)</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">C</span><span class="p">]</span>
+<span class="n">Z</span><span class="p">[</span><span class="s2">&quot;fat content&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">]()</span> <span class="o">*</span> <span class="n">suppliers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="s2">&quot;fat&quot;</span><span class="p">]</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">L</span><span class="p">)</span>
+    <span class="o">/</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">]()</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">L</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">C</span>
+<span class="p">]</span>
 <span class="n">Z</span><span class="p">[</span><span class="s2">&quot;min_fat&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">customers</span><span class="p">[</span><span class="s2">&quot;min_fat&quot;</span><span class="p">]</span>
 
 <span class="n">display</span><span class="p">(</span><span class="n">Z</span><span class="p">)</span>
@@ -766,8 +788,6 @@ <h2>Option 1. Business as usual<a class="headerlink" href="#option-1-business-as
 </div>
 <div class="cell_output docutils container">
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Profit = 81000.00
-
-Blending Plan
 </pre></div>
 </div>
 <div class="output text_html"><div>
@@ -846,12 +866,12 @@ <h2>Option 1. Business as usual<a class="headerlink" href="#option-1-business-as
 </section>
 <section id="option-2-buy-an-additional-truck">
 <h2>Option 2. Buy an additional truck<a class="headerlink" href="#option-2-buy-an-additional-truck" title="Permalink to this heading">#</a></h2>
-<p>The distributor can earn a profit of 81,000 using only local suppliers. Is is possible earn a higher profit by also sourcing raw milk from the remote suppliers?</p>
+<p>As shown before, the distributor can earn a profit of 81,000 using only local suppliers. It is possible to earn a higher profit by also sourcing raw milk from the remote suppliers?</p>
 <p>Before considering pooling, the distributor may wish to know the maximum profit possible if raw milk from the remote suppliers could be blended just like local suppliers. This would require acquiring and operating a separate transport truck for each remote supplier, and is worth knowing if the additional profit would justify the additional expense.</p>
 <p>The linear optimization model presented Option 1 extends the to include both local and remote suppliers.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">m</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">ConcreteModel</span><span class="p">()</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">m</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">ConcreteModel</span><span class="p">(</span><span class="s2">&quot;Milk pooling v2 (additional truck)&quot;</span><span class="p">)</span>
 
 <span class="c1"># define sources and customers</span>
 <span class="n">m</span><span class="o">.</span><span class="n">S</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Set</span><span class="p">(</span><span class="n">initialize</span><span class="o">=</span><span class="n">suppliers</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span>
@@ -860,28 +880,43 @@ <h2>Option 2. Buy an additional truck<a class="headerlink" href="#option-2-buy-a
 <span class="c1"># define local -&gt; customer flowrates</span>
 <span class="n">m</span><span class="o">.</span><span class="n">z</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">S</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">C</span><span class="p">,</span> <span class="n">domain</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">NonNegativeReals</span><span class="p">)</span>
 
+
 <span class="nd">@m</span><span class="o">.</span><span class="n">Objective</span><span class="p">(</span><span class="n">sense</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">maximize</span><span class="p">)</span>
 <span class="k">def</span> <span class="nf">profit</span><span class="p">(</span><span class="n">m</span><span class="p">):</span>
-    <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">s</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span><span class="o">*</span><span class="p">(</span><span class="n">customers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="s2">&quot;price&quot;</span><span class="p">]</span> <span class="o">-</span> <span class="n">suppliers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">s</span><span class="p">,</span> <span class="s2">&quot;cost&quot;</span><span class="p">])</span> <span class="k">for</span> <span class="n">s</span><span class="p">,</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">S</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">C</span><span class="p">)</span>
+    <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span>
+        <span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">s</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="n">customers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="s2">&quot;price&quot;</span><span class="p">]</span> <span class="o">-</span> <span class="n">suppliers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">s</span><span class="p">,</span> <span class="s2">&quot;cost&quot;</span><span class="p">])</span>
+        <span class="k">for</span> <span class="n">s</span><span class="p">,</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">S</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">C</span>
+    <span class="p">)</span>
+
 
 <span class="nd">@m</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">C</span><span class="p">)</span>
 <span class="k">def</span> <span class="nf">demand</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">c</span><span class="p">):</span>
     <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">s</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">S</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="n">customers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="s2">&quot;demand&quot;</span><span class="p">]</span>
 
+
 <span class="nd">@m</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">C</span><span class="p">)</span>
 <span class="k">def</span> <span class="nf">quality</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">c</span><span class="p">):</span>
-    <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">s</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span> <span class="o">*</span> <span class="n">suppliers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">s</span><span class="p">,</span> <span class="s2">&quot;fat&quot;</span><span class="p">]</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">S</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">s</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">S</span><span class="p">)</span> <span class="o">*</span> <span class="n">customers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="s2">&quot;min_fat&quot;</span><span class="p">]</span>
+    <span class="k">return</span> <span class="p">(</span>
+        <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">s</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span> <span class="o">*</span> <span class="n">suppliers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">s</span><span class="p">,</span> <span class="s2">&quot;fat&quot;</span><span class="p">]</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">S</span><span class="p">)</span>
+        <span class="o">&gt;=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">s</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">S</span><span class="p">)</span> <span class="o">*</span> <span class="n">customers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="s2">&quot;min_fat&quot;</span><span class="p">]</span>
+    <span class="p">)</span>
+
 
-<span class="n">pyo</span><span class="o">.</span><span class="n">SolverFactory</span><span class="p">(</span><span class="n">SOLVER_LO</span><span class="p">)</span><span class="o">.</span><span class="n">solve</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
+<span class="n">SOLVER_LO</span><span class="o">.</span><span class="n">solve</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
 
-<span class="c1"># report results</span>
 <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Profit = </span><span class="si">{</span><span class="n">m</span><span class="o">.</span><span class="n">profit</span><span class="p">()</span><span class="si">:</span><span class="s2">0.2f</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
 
-<span class="c1"># create dataframe of results</span>
-<span class="n">Z</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([[</span><span class="n">s</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="nb">round</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">s</span><span class="p">,</span> <span class="n">c</span><span class="p">](),</span> <span class="mi">1</span><span class="p">)]</span> <span class="k">for</span> <span class="n">s</span><span class="p">,</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">S</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">C</span><span class="p">],</span> <span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;supplier&quot;</span><span class="p">,</span> <span class="s2">&quot;customer&quot;</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">])</span>
+<span class="n">Z</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
+    <span class="p">[[</span><span class="n">s</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="nb">round</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">s</span><span class="p">,</span> <span class="n">c</span><span class="p">](),</span> <span class="mi">1</span><span class="p">)]</span> <span class="k">for</span> <span class="n">s</span><span class="p">,</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">S</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">C</span><span class="p">],</span>
+    <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;supplier&quot;</span><span class="p">,</span> <span class="s2">&quot;customer&quot;</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">],</span>
+<span class="p">)</span>
 <span class="n">Z</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">pivot_table</span><span class="p">(</span><span class="n">index</span><span class="o">=</span><span class="s2">&quot;customer&quot;</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="s2">&quot;supplier&quot;</span><span class="p">)</span>
 <span class="n">Z</span><span class="p">[</span><span class="s2">&quot;Total&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">Z</span><span class="p">[</span><span class="s2">&quot;fat content&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">s</span><span class="p">,</span> <span class="n">c</span><span class="p">]()</span> <span class="o">*</span> <span class="n">suppliers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">s</span><span class="p">,</span> <span class="s2">&quot;fat&quot;</span><span class="p">]</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">S</span><span class="p">)</span><span class="o">/</span><span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">s</span><span class="p">,</span> <span class="n">c</span><span class="p">]()</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">S</span><span class="p">)</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">C</span><span class="p">]</span>
+<span class="n">Z</span><span class="p">[</span><span class="s2">&quot;fat content&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">s</span><span class="p">,</span> <span class="n">c</span><span class="p">]()</span> <span class="o">*</span> <span class="n">suppliers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">s</span><span class="p">,</span> <span class="s2">&quot;fat&quot;</span><span class="p">]</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">S</span><span class="p">)</span>
+    <span class="o">/</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">s</span><span class="p">,</span> <span class="n">c</span><span class="p">]()</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">S</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">C</span>
+<span class="p">]</span>
 <span class="n">Z</span><span class="p">[</span><span class="s2">&quot;min_fat&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">customers</span><span class="p">[</span><span class="s2">&quot;min_fat&quot;</span><span class="p">]</span>
 
 <span class="n">display</span><span class="p">(</span><span class="n">Z</span><span class="p">)</span>
@@ -983,7 +1018,7 @@ <h2>Option 3. Pool delivery from remote suppliers<a class="headerlink" href="#op
 <p>Because only one truck with a single tank is available for transport from the remote farms, the pool and blending problem is to combine purchases from the remote suppliers into a single pool of uniform composition, transport that pool to the distribution facility, then blend with raw milk from local suppliers to meet individual customer requirements. Compared to option 2, the profit potential may be reduced due to pooling, but without the need to acquire an additional truck.</p>
 <section id="pooling-problem">
 <h3>Pooling problem<a class="headerlink" href="#pooling-problem" title="Permalink to this heading">#</a></h3>
-<p>There are a several mathematical formulations of pooling problem in the academic literature. The formulation used here is called the “p-parameterization” where the pool composition represents a new decision variable <span class="math notranslate nohighlight">\(p\)</span>. The other additional decision variables are <span class="math notranslate nohighlight">\(x_r\)</span> referring to the amount of raw milk purchased from remote supplier <span class="math notranslate nohighlight">\(r\in R\)</span>, and <span class="math notranslate nohighlight">\(y_c\)</span> which is the amount of the pooled milk included in the blend delivered to customer <span class="math notranslate nohighlight">\(c\in C\)</span>.</p>
+<p>There are several mathematical formulations of pooling problem in the academic literature. The formulation used here is called the <strong><span class="math notranslate nohighlight">\(p\)</span>-parameterization</strong> where the pool composition represents a new decision variable <span class="math notranslate nohighlight">\(p\)</span>. The other additional decision variables are <span class="math notranslate nohighlight">\(x_r\)</span> referring to the amount of raw milk purchased from remote supplier <span class="math notranslate nohighlight">\(r\in R\)</span>, and <span class="math notranslate nohighlight">\(y_c\)</span> which is the amount of the pooled milk included in the blend delivered to customer <span class="math notranslate nohighlight">\(c\in C\)</span>.</p>
 <p>The profit objective is the difference between the income received for selling blended products and the cost of purchasing raw milk from local and remote suppliers.</p>
 <div class="math notranslate nohighlight">
 \[
@@ -1010,15 +1045,15 @@ <h3>Pooling problem<a class="headerlink" href="#pooling-problem" title="Permalin
 <div class="math notranslate nohighlight">
 \[
 \begin{align*}
-\sum_{r\in R}\text{fat}_{r}\ x_{r}  &amp; = \underbrace{p \sum_{c\in C} y_{c}}_{\text{bilinear}}
+\sum_{r\in R}\text{fat}_{r}\cdot x_{r}  &amp; = \underbrace{p \sum_{c\in C} y_{c}}_{\text{bilinear}}
 \end{align*}
 \]</div>
 <p>Finally, the milk fat required by each customer <span class="math notranslate nohighlight">\(c\in C\)</span> satisfies a blending constraint.</p>
 <div class="math notranslate nohighlight">
 \[
 \begin{align*}
-\underbrace{p y_{c}}_{\text{bilinear}}  + \sum_{(l,c)\ \in\ L \times C} \text{fat}_{l}\ z_{l,c}
-&amp; \geq \text{min_fat}_{c}\ (\sum_{l\in L} z_{l, c} + y_{c})
+\underbrace{p y_{c}}_{\text{bilinear}}  + \sum_{(l,c)\ \in\ L \times C} \text{fat}_{l}\cdot z_{l,c}
+&amp; \geq \text{fat}^{\min}_c \ (\sum_{l\in L} z_{l, c} + y_{c})
 &amp; \forall c \in C
 \end{align*}
 \]</div>
@@ -1027,30 +1062,31 @@ <h3>Pooling problem<a class="headerlink" href="#pooling-problem" title="Permalin
 <div class="math notranslate nohighlight">
 \[\begin{split}
 \begin{align*}
-\max\ \text{Profit} = &amp; \sum_{(l,c)\ \in\ L \times C} (\text{price}_c - \text{cost}_l)\ z_{l,c}
+\max \quad &amp; \sum_{(l,c)\ \in\ L \times C} (\text{price}_c - \text{cost}_l)\ z_{l,c}
 + \sum_{c\in C} \text{price}_c y_{c} - \sum_{r\in R} \text{cost}_r x_{r} \\
-\text{s.t.}\qquad &amp; \sum_{l\in L} z_{l, c} + y_{c} \leq \text{demand}_{c} &amp; \forall c\in C \\
+\text{s.t.}\quad 
+&amp; \sum_{l\in L} z_{l, c} + y_{c} \leq \text{demand}_{c} &amp; \forall c\in C \\
 &amp; \sum_{r\in R}x_{r} = \sum_{c\in C} y_{c} \\
-&amp; \underbrace{p y_{c}}_{\text{bilinear}}  + \sum_{(l,c)\ \in\ L \times C} \text{fat}_{l}\ z_{l,c} \geq \text{min_fat}_{c}\ (\sum_{l\in L} z_{l, c} + y_{c}) &amp; \forall c \in C \\
-&amp; \sum_{r\in R}\text{fat}_{r}\ x_{r} = \underbrace{p \sum_{c\in C} y_{c}}_{\text{bilinear}} \\
-&amp; p, x_r, y_c, z_{l, c} \geq 0 &amp; \forall r\in R, c\in C, l\in L 
+&amp; \underbrace{p y_{c}}_{\text{bilinear}}  + \sum_{(l,c)\ \in\ L \times C} \text{fat}_{l}\cdot z_{l,c} \geq \text{fat}^{\min}_c\ (\sum_{l\in L} z_{l, c} + y_{c}) &amp; \forall c \in C \\
+&amp; \sum_{r\in R}\text{fat}_{r}\cdot x_{r} = \underbrace{p \sum_{c\in C} y_{c}}_{\text{bilinear}} \\
+&amp; p, x_r, y_c, z_{l, c} \geq 0 &amp; \forall r\in R, c\in C, l\in L. 
 \end{align*}
 \end{split}\]</div>
-<p>Before attempting a solution to this problem, let’s first consider the implications of the bilinear terms.</p>
+<p>Before attempting a solution to this problem, let us first consider the implications of the bilinear terms.</p>
 </section>
 <section id="why-are-bilinear-problems-hard">
 <h3>Why are bilinear problems hard?<a class="headerlink" href="#why-are-bilinear-problems-hard" title="Permalink to this heading">#</a></h3>
-<p>Bilinearity has a profound consequence on the nature of the optimization problem. To demonstrate this point, we consider a function obtained by fixing <span class="math notranslate nohighlight">\(p\)</span> in the milk pooling problem and solving the resulting linear optimization problem for the maximum profit <span class="math notranslate nohighlight">\(f(p)\)</span>.</p>
+<p>Bilinearity has a profound consequence on the nature of the optimization problem. To explore this, we treat <span class="math notranslate nohighlight">\(p\)</span> as a parameter and repeatedly solve the resulting linear problem for different values of <span class="math notranslate nohighlight">\(p\)</span>.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># solve milk pooling problem for a fixed pool composition p</span>
-<span class="k">def</span> <span class="nf">f</span><span class="p">(</span><span class="n">p</span><span class="p">):</span>
-    
-    <span class="n">m</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">ConcreteModel</span><span class="p">(</span><span class="s1">&#39;Milk Pooling Model&#39;</span><span class="p">)</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">milk_pooling_bilinear</span><span class="p">(</span><span class="n">p</span><span class="p">):</span>
+    <span class="n">m</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">ConcreteModel</span><span class="p">(</span><span class="s2">&quot;Milk pooling v3 (Bilinear formulation)&quot;</span><span class="p">)</span>
 
-    <span class="c1"># define sources </span>
-    <span class="n">m</span><span class="o">.</span><span class="n">L</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Set</span><span class="p">(</span><span class="n">initialize</span><span class="o">=</span><span class="n">suppliers</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">suppliers</span><span class="p">[</span><span class="s2">&quot;location&quot;</span><span class="p">]</span><span class="o">==</span><span class="s2">&quot;local&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span>
-    <span class="n">m</span><span class="o">.</span><span class="n">R</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Set</span><span class="p">(</span><span class="n">initialize</span><span class="o">=</span><span class="n">suppliers</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">suppliers</span><span class="p">[</span><span class="s2">&quot;location&quot;</span><span class="p">]</span><span class="o">==</span><span class="s2">&quot;remote&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span>
+    <span class="c1"># define sources</span>
+    <span class="n">m</span><span class="o">.</span><span class="n">L</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Set</span><span class="p">(</span><span class="n">initialize</span><span class="o">=</span><span class="n">suppliers</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">suppliers</span><span class="p">[</span><span class="s2">&quot;location&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;local&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span>
+    <span class="n">m</span><span class="o">.</span><span class="n">R</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Set</span><span class="p">(</span>
+        <span class="n">initialize</span><span class="o">=</span><span class="n">suppliers</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">suppliers</span><span class="p">[</span><span class="s2">&quot;location&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;remote&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+    <span class="p">)</span>
 
     <span class="c1"># define customers</span>
     <span class="n">m</span><span class="o">.</span><span class="n">C</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Set</span><span class="p">(</span><span class="n">initialize</span><span class="o">=</span><span class="n">customers</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span>
@@ -1059,34 +1095,44 @@ <h3>Why are bilinear problems hard?<a class="headerlink" href="#why-are-bilinear
     <span class="n">m</span><span class="o">.</span><span class="n">x</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">R</span><span class="p">,</span> <span class="n">domain</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">NonNegativeReals</span><span class="p">)</span>
     <span class="n">m</span><span class="o">.</span><span class="n">y</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">C</span><span class="p">,</span> <span class="n">domain</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">NonNegativeReals</span><span class="p">)</span>
     <span class="n">m</span><span class="o">.</span><span class="n">z</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">L</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">C</span><span class="p">,</span> <span class="n">domain</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">NonNegativeReals</span><span class="p">)</span>
-    
+
     <span class="n">m</span><span class="o">.</span><span class="n">p</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Param</span><span class="p">(</span><span class="n">default</span><span class="o">=</span><span class="n">p</span><span class="p">)</span>
 
     <span class="nd">@m</span><span class="o">.</span><span class="n">Objective</span><span class="p">(</span><span class="n">sense</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">maximize</span><span class="p">)</span>
     <span class="k">def</span> <span class="nf">profit</span><span class="p">(</span><span class="n">m</span><span class="p">):</span>
-        <span class="k">return</span> <span class="o">+</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span><span class="o">*</span><span class="p">(</span><span class="n">customers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="s2">&quot;price&quot;</span><span class="p">]</span> <span class="o">-</span> <span class="n">suppliers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="s2">&quot;cost&quot;</span><span class="p">])</span> <span class="k">for</span> <span class="n">l</span><span class="p">,</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">L</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">C</span><span class="p">)</span> \
-               <span class="o">+</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">*</span><span class="n">customers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="s2">&quot;price&quot;</span><span class="p">]</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">C</span><span class="p">)</span> \
-               <span class="o">-</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">*</span><span class="n">suppliers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">r</span><span class="p">,</span> <span class="s2">&quot;cost&quot;</span><span class="p">]</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">R</span><span class="p">)</span>
+        <span class="k">return</span> <span class="p">(</span>
+            <span class="o">+</span><span class="nb">sum</span><span class="p">(</span>
+                <span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="n">customers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="s2">&quot;price&quot;</span><span class="p">]</span> <span class="o">-</span> <span class="n">suppliers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="s2">&quot;cost&quot;</span><span class="p">])</span>
+                <span class="k">for</span> <span class="n">l</span><span class="p">,</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">L</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">C</span>
+            <span class="p">)</span>
+            <span class="o">+</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">*</span> <span class="n">customers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="s2">&quot;price&quot;</span><span class="p">]</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">C</span><span class="p">)</span>
+            <span class="o">-</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">r</span><span class="p">]</span> <span class="o">*</span> <span class="n">suppliers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">r</span><span class="p">,</span> <span class="s2">&quot;cost&quot;</span><span class="p">]</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">R</span><span class="p">)</span>
+        <span class="p">)</span>
 
     <span class="nd">@m</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">C</span><span class="p">)</span>
     <span class="k">def</span> <span class="nf">customer_demand</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">c</span><span class="p">):</span>
         <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">L</span><span class="p">)</span> <span class="o">+</span> <span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">customers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="s2">&quot;demand&quot;</span><span class="p">]</span>
-    
+
     <span class="nd">@m</span><span class="o">.</span><span class="n">Constraint</span><span class="p">()</span>
-    <span class="k">def</span> <span class="nf">pool_balance</span><span class="p">(</span><span class="n">m</span><span class="p">,):</span>
+    <span class="k">def</span> <span class="nf">pool_balance</span><span class="p">(</span>
+        <span class="n">m</span><span class="p">,</span>
+    <span class="p">):</span>
         <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">r</span><span class="p">]</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">R</span><span class="p">)</span> <span class="o">==</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">C</span><span class="p">)</span>
-    
+
     <span class="nd">@m</span><span class="o">.</span><span class="n">Constraint</span><span class="p">()</span>
     <span class="k">def</span> <span class="nf">pool_quality</span><span class="p">(</span><span class="n">m</span><span class="p">):</span>
-        <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">suppliers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">r</span><span class="p">,</span> <span class="s2">&quot;fat&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">r</span><span class="p">]</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">R</span><span class="p">)</span> <span class="o">==</span> <span class="n">m</span><span class="o">.</span><span class="n">p</span> <span class="o">*</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">r</span><span class="p">]</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">R</span><span class="p">)</span>
-    
+        <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">suppliers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">r</span><span class="p">,</span> <span class="s2">&quot;fat&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">r</span><span class="p">]</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">R</span><span class="p">)</span> <span class="o">==</span> <span class="n">m</span><span class="o">.</span><span class="n">p</span> <span class="o">*</span> <span class="nb">sum</span><span class="p">(</span>
+            <span class="n">m</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">r</span><span class="p">]</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">R</span>
+        <span class="p">)</span>
+
     <span class="nd">@m</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">C</span><span class="p">)</span>
     <span class="k">def</span> <span class="nf">customer_quality</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">c</span><span class="p">):</span>
-        <span class="k">return</span> <span class="n">m</span><span class="o">.</span><span class="n">p</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">+</span> <span class="nb">sum</span><span class="p">(</span><span class="n">suppliers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="s2">&quot;fat&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">L</span><span class="p">)</span> \
-                 <span class="o">&gt;=</span> <span class="n">customers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="s2">&quot;min_fat&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">L</span><span class="p">)</span> <span class="o">+</span> <span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">])</span>
+        <span class="k">return</span> <span class="n">m</span><span class="o">.</span><span class="n">p</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">+</span> <span class="nb">sum</span><span class="p">(</span>
+            <span class="n">suppliers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="s2">&quot;fat&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">L</span>
+        <span class="p">)</span> <span class="o">&gt;=</span> <span class="n">customers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="s2">&quot;min_fat&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">L</span><span class="p">)</span> <span class="o">+</span> <span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">])</span>
+
+    <span class="n">SOLVER_LO</span><span class="o">.</span><span class="n">solve</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
 
-    <span class="n">pyo</span><span class="o">.</span><span class="n">SolverFactory</span><span class="p">(</span><span class="n">SOLVER_LO</span><span class="p">)</span><span class="o">.</span><span class="n">solve</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
-    
     <span class="k">return</span> <span class="n">m</span>
 </pre></div>
 </div>
@@ -1094,21 +1140,22 @@ <h3>Why are bilinear problems hard?<a class="headerlink" href="#why-are-bilinear
 </div>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">p_plot</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">0.025</span><span class="p">,</span> <span class="mf">0.055</span><span class="p">,</span> <span class="mi">200</span><span class="p">)</span>
-<span class="n">f_plot</span> <span class="o">=</span> <span class="p">[</span><span class="n">f</span><span class="p">(</span><span class="n">p</span><span class="p">)</span><span class="o">.</span><span class="n">profit</span><span class="p">()</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">p_plot</span><span class="p">]</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">p_plot</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">0.025</span><span class="p">,</span> <span class="mf">0.06</span><span class="p">,</span> <span class="mi">200</span><span class="p">)</span>
+<span class="n">f_plot</span> <span class="o">=</span> <span class="p">[</span><span class="n">milk_pooling_bilinear</span><span class="p">(</span><span class="n">p</span><span class="p">)</span><span class="o">.</span><span class="n">profit</span><span class="p">()</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">p_plot</span><span class="p">]</span>
 
-<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">p_plot</span><span class="p">,</span> <span class="n">f_plot</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Milk Pooling&quot;</span><span class="p">)</span>
+<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">p_plot</span><span class="p">,</span> <span class="n">f_plot</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
 <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Pool composition p&quot;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Profit f&quot;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Profit&quot;</span><span class="p">)</span>
 <span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="o">.</span><span class="n">update</span><span class="p">({</span><span class="s2">&quot;font.size&quot;</span><span class="p">:</span> <span class="mi">14</span><span class="p">})</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
 </div>
 </div>
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 <p>In contrast to linear or other convex optimization problems, the objective function for this <strong>bilinear optimization problem is a non-convex function of a decision variable</strong> <span class="math notranslate nohighlight">\(p\)</span> denoting the composition of the blending pool. In fact, when profit is plotted as a function of <span class="math notranslate nohighlight">\(p\)</span>, there are three local maxima separated by two local minima.</p>
@@ -1123,7 +1170,7 @@ <h3>Convex Approximation<a class="headerlink" href="#convex-approximation" title
 + \sum_{c\in C} \text{price}_c y_{c} - \sum_{r\in R} \text{cost}_r x_{r} \\
 \text{s.t.}\qquad &amp; \sum_{l\in L} z_{l, c} + y_{c} \leq \text{demand}_{c} &amp; \forall c\in C \\
 &amp; \sum_{r\in R}x_{r} = \sum_{c\in C} y_{c} \\
-&amp; w_c + \sum_{(l,c)\ \in\ L \times C} \text{fat}_{l}\ z_{l,c} \geq \text{min_fat}_{c}\ (\sum_{l\in L} z_{l, c} + y_{c}) &amp; \forall c \in C \\
+&amp; w_c + \sum_{(l,c)\ \in\ L \times C} \text{fat}_{l}\ z_{l,c} \geq \text{fat}^{\min}_c \ (\sum_{l\in L} z_{l, c} + y_{c}) &amp; \forall c \in C \\
 &amp; \sum_{r\in R}\text{fat}_{r}\ x_{r} = \underbrace{p \sum_{c\in C} y_{c}}_{\text{bilinear}} \\
 &amp; w_c, x_r, y_c, z_{l, c} \geq 0 &amp; \forall r\in R, c\in C, l\in L 
 \end{align*}
@@ -1166,12 +1213,13 @@ <h3>Convex Approximation<a class="headerlink" href="#convex-approximation" title
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">milk_pooling_convex</span><span class="p">():</span>
-    
-    <span class="n">m</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">ConcreteModel</span><span class="p">(</span><span class="s1">&#39;Milk Pooling Model - Convex Approximation&#39;</span><span class="p">)</span>
+    <span class="n">m</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">ConcreteModel</span><span class="p">(</span><span class="s2">&quot;Milk pooling v3 (Convex approximation)&quot;</span><span class="p">)</span>
 
-    <span class="c1"># define sources </span>
-    <span class="n">m</span><span class="o">.</span><span class="n">L</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Set</span><span class="p">(</span><span class="n">initialize</span><span class="o">=</span><span class="n">suppliers</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">suppliers</span><span class="p">[</span><span class="s2">&quot;location&quot;</span><span class="p">]</span><span class="o">==</span><span class="s2">&quot;local&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span>
-    <span class="n">m</span><span class="o">.</span><span class="n">R</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Set</span><span class="p">(</span><span class="n">initialize</span><span class="o">=</span><span class="n">suppliers</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">suppliers</span><span class="p">[</span><span class="s2">&quot;location&quot;</span><span class="p">]</span><span class="o">==</span><span class="s2">&quot;remote&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span>
+    <span class="c1"># define sources</span>
+    <span class="n">m</span><span class="o">.</span><span class="n">L</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Set</span><span class="p">(</span><span class="n">initialize</span><span class="o">=</span><span class="n">suppliers</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">suppliers</span><span class="p">[</span><span class="s2">&quot;location&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;local&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span>
+    <span class="n">m</span><span class="o">.</span><span class="n">R</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Set</span><span class="p">(</span>
+        <span class="n">initialize</span><span class="o">=</span><span class="n">suppliers</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">suppliers</span><span class="p">[</span><span class="s2">&quot;location&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;remote&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+    <span class="p">)</span>
 
     <span class="c1"># define customers</span>
     <span class="n">m</span><span class="o">.</span><span class="n">C</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Set</span><span class="p">(</span><span class="n">initialize</span><span class="o">=</span><span class="n">customers</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span>
@@ -1180,47 +1228,71 @@ <h3>Convex Approximation<a class="headerlink" href="#convex-approximation" title
     <span class="n">m</span><span class="o">.</span><span class="n">x</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">R</span><span class="p">,</span> <span class="n">domain</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">NonNegativeReals</span><span class="p">)</span>
     <span class="n">m</span><span class="o">.</span><span class="n">y</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">C</span><span class="p">,</span> <span class="n">bounds</span><span class="o">=</span><span class="k">lambda</span> <span class="n">m</span><span class="p">,</span> <span class="n">c</span><span class="p">:</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">customers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="s2">&quot;demand&quot;</span><span class="p">]))</span>
     <span class="n">m</span><span class="o">.</span><span class="n">z</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">L</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">C</span><span class="p">,</span> <span class="n">domain</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">NonNegativeReals</span><span class="p">)</span>
-    
+
     <span class="c1"># composition of the pool</span>
-    <span class="n">m</span><span class="o">.</span><span class="n">p</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">bounds</span><span class="o">=</span><span class="p">(</span><span class="n">suppliers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">m</span><span class="o">.</span><span class="n">R</span><span class="p">,</span> <span class="s2">&quot;fat&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">suppliers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">m</span><span class="o">.</span><span class="n">R</span><span class="p">,</span> <span class="s2">&quot;fat&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()))</span>
-    
+    <span class="n">m</span><span class="o">.</span><span class="n">p</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span>
+        <span class="n">bounds</span><span class="o">=</span><span class="p">(</span><span class="n">suppliers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">m</span><span class="o">.</span><span class="n">R</span><span class="p">,</span> <span class="s2">&quot;fat&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">suppliers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">m</span><span class="o">.</span><span class="n">R</span><span class="p">,</span> <span class="s2">&quot;fat&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
+    <span class="p">)</span>
+
     <span class="c1"># w[c] to replace bilinear terms p * y[c]</span>
     <span class="n">m</span><span class="o">.</span><span class="n">w</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">C</span><span class="p">,</span> <span class="n">domain</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">NonNegativeReals</span><span class="p">)</span>
 
     <span class="nd">@m</span><span class="o">.</span><span class="n">Objective</span><span class="p">(</span><span class="n">sense</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">maximize</span><span class="p">)</span>
     <span class="k">def</span> <span class="nf">profit</span><span class="p">(</span><span class="n">m</span><span class="p">):</span>
-        <span class="k">return</span> <span class="o">+</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span><span class="o">*</span><span class="p">(</span><span class="n">customers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="s2">&quot;price&quot;</span><span class="p">]</span> <span class="o">-</span> <span class="n">suppliers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="s2">&quot;cost&quot;</span><span class="p">])</span> <span class="k">for</span> <span class="n">l</span><span class="p">,</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">L</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">C</span><span class="p">)</span> \
-               <span class="o">+</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">*</span><span class="n">customers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="s2">&quot;price&quot;</span><span class="p">]</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">C</span><span class="p">)</span> \
-               <span class="o">-</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">*</span><span class="n">suppliers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">r</span><span class="p">,</span> <span class="s2">&quot;cost&quot;</span><span class="p">]</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">R</span><span class="p">)</span>
-    
+        <span class="k">return</span> <span class="p">(</span>
+            <span class="o">+</span><span class="nb">sum</span><span class="p">(</span>
+                <span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="n">customers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="s2">&quot;price&quot;</span><span class="p">]</span> <span class="o">-</span> <span class="n">suppliers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="s2">&quot;cost&quot;</span><span class="p">])</span>
+                <span class="k">for</span> <span class="n">l</span><span class="p">,</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">L</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">C</span>
+            <span class="p">)</span>
+            <span class="o">+</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">*</span> <span class="n">customers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="s2">&quot;price&quot;</span><span class="p">]</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">C</span><span class="p">)</span>
+            <span class="o">-</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">r</span><span class="p">]</span> <span class="o">*</span> <span class="n">suppliers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">r</span><span class="p">,</span> <span class="s2">&quot;cost&quot;</span><span class="p">]</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">R</span><span class="p">)</span>
+        <span class="p">)</span>
+
     <span class="c1"># create a block of constraints for every customer</span>
     <span class="nd">@m</span><span class="o">.</span><span class="n">Block</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">C</span><span class="p">)</span>
     <span class="k">def</span> <span class="nf">mccormick</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">):</span>
         <span class="n">m</span> <span class="o">=</span> <span class="n">b</span><span class="o">.</span><span class="n">model</span><span class="p">()</span>
-        <span class="n">b</span><span class="o">.</span><span class="n">ll</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">expr</span><span class="o">=</span><span class="n">m</span><span class="o">.</span><span class="n">w</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">p</span> <span class="o">+</span> <span class="n">m</span><span class="o">.</span><span class="n">p</span><span class="o">.</span><span class="n">lower</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">-</span> <span class="n">m</span><span class="o">.</span><span class="n">p</span><span class="o">.</span><span class="n">lower</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span><span class="p">)</span>
-        <span class="n">b</span><span class="o">.</span><span class="n">hh</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">expr</span><span class="o">=</span><span class="n">m</span><span class="o">.</span><span class="n">w</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">upper</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">p</span> <span class="o">+</span> <span class="n">m</span><span class="o">.</span><span class="n">p</span><span class="o">.</span><span class="n">upper</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">-</span> <span class="n">m</span><span class="o">.</span><span class="n">p</span><span class="o">.</span><span class="n">upper</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">upper</span><span class="p">)</span>
-        <span class="n">b</span><span class="o">.</span><span class="n">lh</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">expr</span><span class="o">=</span><span class="n">m</span><span class="o">.</span><span class="n">w</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">upper</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">p</span> <span class="o">+</span> <span class="n">m</span><span class="o">.</span><span class="n">p</span><span class="o">.</span><span class="n">lower</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">-</span> <span class="n">m</span><span class="o">.</span><span class="n">p</span><span class="o">.</span><span class="n">lower</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">upper</span><span class="p">)</span>
-        <span class="n">b</span><span class="o">.</span><span class="n">hl</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">expr</span><span class="o">=</span><span class="n">m</span><span class="o">.</span><span class="n">w</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">p</span> <span class="o">+</span> <span class="n">m</span><span class="o">.</span><span class="n">p</span><span class="o">.</span><span class="n">upper</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">-</span> <span class="n">m</span><span class="o">.</span><span class="n">p</span><span class="o">.</span><span class="n">upper</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span><span class="p">)</span>  
+        <span class="n">b</span><span class="o">.</span><span class="n">ll</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span>
+            <span class="n">expr</span><span class="o">=</span><span class="n">m</span><span class="o">.</span><span class="n">w</span><span class="p">[</span><span class="n">c</span><span class="p">]</span>
+            <span class="o">&gt;=</span> <span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">p</span> <span class="o">+</span> <span class="n">m</span><span class="o">.</span><span class="n">p</span><span class="o">.</span><span class="n">lower</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">-</span> <span class="n">m</span><span class="o">.</span><span class="n">p</span><span class="o">.</span><span class="n">lower</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span>
+        <span class="p">)</span>
+        <span class="n">b</span><span class="o">.</span><span class="n">hh</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span>
+            <span class="n">expr</span><span class="o">=</span><span class="n">m</span><span class="o">.</span><span class="n">w</span><span class="p">[</span><span class="n">c</span><span class="p">]</span>
+            <span class="o">&gt;=</span> <span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">upper</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">p</span> <span class="o">+</span> <span class="n">m</span><span class="o">.</span><span class="n">p</span><span class="o">.</span><span class="n">upper</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">-</span> <span class="n">m</span><span class="o">.</span><span class="n">p</span><span class="o">.</span><span class="n">upper</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">upper</span>
+        <span class="p">)</span>
+        <span class="n">b</span><span class="o">.</span><span class="n">lh</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span>
+            <span class="n">expr</span><span class="o">=</span><span class="n">m</span><span class="o">.</span><span class="n">w</span><span class="p">[</span><span class="n">c</span><span class="p">]</span>
+            <span class="o">&lt;=</span> <span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">upper</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">p</span> <span class="o">+</span> <span class="n">m</span><span class="o">.</span><span class="n">p</span><span class="o">.</span><span class="n">lower</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">-</span> <span class="n">m</span><span class="o">.</span><span class="n">p</span><span class="o">.</span><span class="n">lower</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">upper</span>
+        <span class="p">)</span>
+        <span class="n">b</span><span class="o">.</span><span class="n">hl</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span>
+            <span class="n">expr</span><span class="o">=</span><span class="n">m</span><span class="o">.</span><span class="n">w</span><span class="p">[</span><span class="n">c</span><span class="p">]</span>
+            <span class="o">&lt;=</span> <span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">p</span> <span class="o">+</span> <span class="n">m</span><span class="o">.</span><span class="n">p</span><span class="o">.</span><span class="n">upper</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">-</span> <span class="n">m</span><span class="o">.</span><span class="n">p</span><span class="o">.</span><span class="n">upper</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span>
+        <span class="p">)</span>
 
     <span class="nd">@m</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">C</span><span class="p">)</span>
     <span class="k">def</span> <span class="nf">customer_demand</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">c</span><span class="p">):</span>
         <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">L</span><span class="p">)</span> <span class="o">+</span> <span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">customers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="s2">&quot;demand&quot;</span><span class="p">]</span>
-    
+
     <span class="nd">@m</span><span class="o">.</span><span class="n">Constraint</span><span class="p">()</span>
-    <span class="k">def</span> <span class="nf">pool_balance</span><span class="p">(</span><span class="n">m</span><span class="p">,):</span>
+    <span class="k">def</span> <span class="nf">pool_balance</span><span class="p">(</span>
+        <span class="n">m</span><span class="p">,</span>
+    <span class="p">):</span>
         <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">r</span><span class="p">]</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">R</span><span class="p">)</span> <span class="o">==</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">C</span><span class="p">)</span>
-    
+
     <span class="nd">@m</span><span class="o">.</span><span class="n">Constraint</span><span class="p">()</span>
     <span class="k">def</span> <span class="nf">pool_quality</span><span class="p">(</span><span class="n">m</span><span class="p">):</span>
-        <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">suppliers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">r</span><span class="p">,</span> <span class="s2">&quot;fat&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">r</span><span class="p">]</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">R</span><span class="p">)</span> <span class="o">==</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">w</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">C</span><span class="p">)</span>
-    
+        <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">suppliers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">r</span><span class="p">,</span> <span class="s2">&quot;fat&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">r</span><span class="p">]</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">R</span><span class="p">)</span> <span class="o">==</span> <span class="nb">sum</span><span class="p">(</span>
+            <span class="n">m</span><span class="o">.</span><span class="n">w</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">C</span>
+        <span class="p">)</span>
+
     <span class="nd">@m</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">C</span><span class="p">)</span>
     <span class="k">def</span> <span class="nf">customer_quality</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">c</span><span class="p">):</span>
-        <span class="k">return</span> <span class="n">m</span><span class="o">.</span><span class="n">w</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">+</span> <span class="nb">sum</span><span class="p">(</span><span class="n">suppliers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="s2">&quot;fat&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">L</span><span class="p">)</span> \
-                 <span class="o">&gt;=</span> <span class="n">customers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="s2">&quot;min_fat&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">L</span><span class="p">)</span> <span class="o">+</span> <span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">])</span>
+        <span class="k">return</span> <span class="n">m</span><span class="o">.</span><span class="n">w</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">+</span> <span class="nb">sum</span><span class="p">(</span>
+            <span class="n">suppliers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="s2">&quot;fat&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">L</span>
+        <span class="p">)</span> <span class="o">&gt;=</span> <span class="n">customers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="s2">&quot;min_fat&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">L</span><span class="p">)</span> <span class="o">+</span> <span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">])</span>
+
+    <span class="n">SOLVER_LO</span><span class="o">.</span><span class="n">solve</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
 
-    <span class="n">pyo</span><span class="o">.</span><span class="n">SolverFactory</span><span class="p">(</span><span class="n">SOLVER_LO</span><span class="p">)</span><span class="o">.</span><span class="n">solve</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
-    
     <span class="k">return</span> <span class="n">m</span>
 </pre></div>
 </div>
@@ -1229,48 +1301,49 @@ <h3>Convex Approximation<a class="headerlink" href="#convex-approximation" title
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">report_solution</span><span class="p">(</span><span class="n">m</span><span class="p">):</span>
-    
     <span class="c1"># Supplier report</span>
     <span class="n">S</span> <span class="o">=</span> <span class="n">suppliers</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
     <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">L</span><span class="p">:</span>
         <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">C</span><span class="p">:</span>
-            <span class="n">S</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">]()</span> 
+            <span class="n">S</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">]()</span>
     <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">R</span><span class="p">:</span>
         <span class="n">S</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">r</span><span class="p">,</span> <span class="s2">&quot;Pool&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">m</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">r</span><span class="p">]()</span>
     <span class="n">S</span> <span class="o">=</span> <span class="n">S</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
     <span class="n">S</span><span class="p">[</span><span class="s2">&quot;Amount&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">S</span><span class="p">[</span><span class="n">m</span><span class="o">.</span><span class="n">C</span><span class="p">]</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="n">S</span><span class="p">[</span><span class="s2">&quot;Pool&quot;</span><span class="p">]</span>
-    <span class="n">S</span><span class="p">[</span><span class="s2">&quot;Expense&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">S</span><span class="p">[</span><span class="s2">&quot;Amount&quot;</span><span class="p">]</span><span class="o">*</span><span class="n">S</span><span class="p">[</span><span class="s2">&quot;cost&quot;</span><span class="p">]</span>
+    <span class="n">S</span><span class="p">[</span><span class="s2">&quot;Expense&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">S</span><span class="p">[</span><span class="s2">&quot;Amount&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="n">S</span><span class="p">[</span><span class="s2">&quot;cost&quot;</span><span class="p">]</span>
 
-    
     <span class="c1"># Customer report</span>
     <span class="n">C</span> <span class="o">=</span> <span class="n">customers</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
     <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">C</span><span class="p">:</span>
         <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">L</span><span class="p">:</span>
             <span class="n">C</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="n">l</span><span class="p">]</span> <span class="o">=</span> <span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">]()</span>
     <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">C</span><span class="p">:</span>
-        <span class="n">C</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="s2">&quot;Pool&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">]()</span>   
+        <span class="n">C</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="s2">&quot;Pool&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">]()</span>
     <span class="n">C</span> <span class="o">=</span> <span class="n">C</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
     <span class="n">C</span><span class="p">[</span><span class="s2">&quot;Amount&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">C</span><span class="p">[</span><span class="n">m</span><span class="o">.</span><span class="n">L</span><span class="p">]</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="n">C</span><span class="p">[</span><span class="s2">&quot;Pool&quot;</span><span class="p">]</span>
-    <span class="n">C</span><span class="p">[</span><span class="s2">&quot;fat delivered&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="nb">sum</span><span class="p">(</span><span class="n">C</span><span class="p">[</span><span class="n">l</span><span class="p">]</span><span class="o">*</span><span class="n">S</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="s2">&quot;fat&quot;</span><span class="p">]</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">L</span><span class="p">)</span> <span class="o">+</span> <span class="n">C</span><span class="p">[</span><span class="s2">&quot;Pool&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">p</span><span class="p">())</span><span class="o">/</span><span class="n">C</span><span class="p">[</span><span class="s2">&quot;Amount&quot;</span><span class="p">]</span>
+    <span class="n">C</span><span class="p">[</span><span class="s2">&quot;fat delivered&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="nb">sum</span><span class="p">(</span><span class="n">C</span><span class="p">[</span><span class="n">l</span><span class="p">]</span> <span class="o">*</span> <span class="n">S</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="s2">&quot;fat&quot;</span><span class="p">]</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">L</span><span class="p">)</span> <span class="o">+</span> <span class="n">C</span><span class="p">[</span><span class="s2">&quot;Pool&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">p</span><span class="p">()</span>
+    <span class="p">)</span> <span class="o">/</span> <span class="n">C</span><span class="p">[</span><span class="s2">&quot;Amount&quot;</span><span class="p">]</span>
     <span class="n">C</span><span class="p">[</span><span class="s2">&quot;Income&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">C</span><span class="p">[</span><span class="s2">&quot;Amount&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="n">C</span><span class="p">[</span><span class="s2">&quot;price&quot;</span><span class="p">]</span>
-    
+
     <span class="nb">print</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Pool composition = </span><span class="si">{</span><span class="n">m</span><span class="o">.</span><span class="n">p</span><span class="p">()</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Pool composition = </span><span class="si">{</span><span class="n">m</span><span class="o">.</span><span class="n">p</span><span class="p">()</span><span class="si">:</span><span class="s2">5.4f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
     <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Profit = </span><span class="si">{</span><span class="n">m</span><span class="o">.</span><span class="n">profit</span><span class="p">()</span><span class="si">:</span><span class="s2">0.2f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
     <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Supplier Report</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
     <span class="n">display</span><span class="p">(</span><span class="n">S</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">4</span><span class="p">))</span>
     <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Customer Report</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
     <span class="n">display</span><span class="p">(</span><span class="n">C</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">4</span><span class="p">))</span>
 
+
 <span class="n">m_convex</span> <span class="o">=</span> <span class="n">milk_pooling_convex</span><span class="p">()</span>
 <span class="n">report_solution</span><span class="p">(</span><span class="n">m_convex</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Milk Pooling Model - Convex Approximation
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&#39;Milk pooling v3 (Convex approximation)&#39;
 
-Pool composition = 0.04
+Pool composition = 0.0400
 Profit = 111411.76
 
 Supplier Report
@@ -1312,8 +1385,8 @@ <h3>Convex Approximation<a class="headerlink" href="#convex-approximation" title
       <td>45.0</td>
       <td>local</td>
       <td>2500.0</td>
-      <td>0.0000</td>
-      <td>0.0</td>
+      <td>-0.0000</td>
+      <td>-0.0</td>
       <td>0.0000</td>
       <td>2500.0000</td>
       <td>112500.0000</td>
@@ -1406,7 +1479,7 @@ <h3>Convex Approximation<a class="headerlink" href="#convex-approximation" title
       <td>0.030</td>
       <td>48.0</td>
       <td>2500.0</td>
-      <td>0.0</td>
+      <td>-0.0</td>
       <td>1029.4118</td>
       <td>1470.5882</td>
       <td>2500.0</td>
@@ -1418,7 +1491,7 @@ <h3>Convex Approximation<a class="headerlink" href="#convex-approximation" title
       <td>0.040</td>
       <td>50.0</td>
       <td>4000.0</td>
-      <td>0.0</td>
+      <td>-0.0</td>
       <td>0.0000</td>
       <td>4000.0000</td>
       <td>4000.0</td>
@@ -1432,74 +1505,78 @@ <h3>Convex Approximation<a class="headerlink" href="#convex-approximation" title
 <p>The convex approximation of the milk pooling model estimates an upper bound on profit of 111,412 for a pool composition <span class="math notranslate nohighlight">\(p = 0.040\)</span>. The plot below compares this solution to what was found by in an exhaustive search over values of <span class="math notranslate nohighlight">\(p\)</span>.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">p_plot</span><span class="p">,</span> <span class="n">f_plot</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Milk Pooling&quot;</span><span class="p">)</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">9</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">p_plot</span><span class="p">,</span> <span class="n">f_plot</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
 <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Pool composition p&quot;</span><span class="p">)</span>
 <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Profit&quot;</span><span class="p">)</span>
 <span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
 
-<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">m_convex</span><span class="o">.</span><span class="n">p</span><span class="p">(),</span> <span class="n">m_convex</span><span class="o">.</span><span class="n">profit</span><span class="p">(),</span> <span class="s1">&#39;ro&#39;</span><span class="p">,</span> <span class="n">ms</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="n">m_convex</span><span class="o">.</span><span class="n">profit</span><span class="p">(),</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;--&#39;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">m_convex</span><span class="o">.</span><span class="n">p</span><span class="p">(),</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;--&#39;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="s2">&quot;convex approximation&quot;</span><span class="p">,</span> <span class="n">xy</span><span class="o">=</span><span class="p">(</span><span class="n">m_convex</span><span class="o">.</span><span class="n">p</span><span class="p">(),</span> <span class="n">m_convex</span><span class="o">.</span><span class="n">profit</span><span class="p">()),</span>
-            <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mf">0.036</span><span class="p">,</span> <span class="mi">106000</span><span class="p">),</span> <span class="n">ha</span><span class="o">=</span><span class="s2">&quot;right&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">,</span>
-            <span class="n">arrowprops</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">shrink</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">headwidth</span><span class="o">=</span><span class="mi">5</span><span class="p">))</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">m_convex</span><span class="o">.</span><span class="n">p</span><span class="p">(),</span> <span class="n">m_convex</span><span class="o">.</span><span class="n">profit</span><span class="p">(),</span> <span class="s2">&quot;ro&quot;</span><span class="p">,</span> <span class="n">ms</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="n">m_convex</span><span class="o">.</span><span class="n">profit</span><span class="p">(),</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;--&quot;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">m_convex</span><span class="o">.</span><span class="n">p</span><span class="p">(),</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;--&quot;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span>
+    <span class="s2">&quot;convex approximation&quot;</span><span class="p">,</span>
+    <span class="n">xy</span><span class="o">=</span><span class="p">(</span><span class="n">m_convex</span><span class="o">.</span><span class="n">p</span><span class="p">(),</span> <span class="n">m_convex</span><span class="o">.</span><span class="n">profit</span><span class="p">()),</span>
+    <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mf">0.036</span><span class="p">,</span> <span class="mi">106000</span><span class="p">),</span>
+    <span class="n">ha</span><span class="o">=</span><span class="s2">&quot;right&quot;</span><span class="p">,</span>
+    <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">,</span>
+    <span class="n">arrowprops</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">shrink</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">headwidth</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">),</span>
+<span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="o">.</span><span class="n">update</span><span class="p">({</span><span class="s2">&quot;font.size&quot;</span><span class="p">:</span> <span class="mi">14</span><span class="p">})</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Text(0.036, 106000, &#39;convex approximation&#39;)
-</pre></div>
-</div>
-<img alt="../../_images/737b683159db34e95a6d04c26ae8d44d374a96d042c7de7cbe907c67db2b0265.png" src="../../_images/737b683159db34e95a6d04c26ae8d44d374a96d042c7de7cbe907c67db2b0265.png" />
+<img alt="../../_images/fe6a4740aa3c604082e16a4bf76b5cb9d4ec976ea9eea334b74f7774797f45d9.png" src="../../_images/fe6a4740aa3c604082e16a4bf76b5cb9d4ec976ea9eea334b74f7774797f45d9.png" />
 </div>
 </div>
 <p>As this stage the calculations find the maximum profit for a given value of <span class="math notranslate nohighlight">\(p\)</span>. The challenge, of course, is that the optimal value of <span class="math notranslate nohighlight">\(p\)</span> is unknown. The following cell computes profits over a range of <span class="math notranslate nohighlight">\(p\)</span>.</p>
 <p>The convex approximation is clearly misses the market in the estimate of profit and pool composition <span class="math notranslate nohighlight">\(p\)</span>. Without the benefit of the full scan of profit as a function of <span class="math notranslate nohighlight">\(p\)</span>, the only check on the profit estimate would be to compute the solution to model for the reported value of <span class="math notranslate nohighlight">\(p\)</span>. This is done below.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">m_est</span> <span class="o">=</span> <span class="n">f</span><span class="p">(</span><span class="n">m_convex</span><span class="o">.</span><span class="n">p</span><span class="p">())</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">m_est</span> <span class="o">=</span> <span class="n">milk_pooling_bilinear</span><span class="p">(</span><span class="n">m_convex</span><span class="o">.</span><span class="n">p</span><span class="p">())</span>
+<span class="n">report_solution</span><span class="p">(</span><span class="n">m_est</span><span class="p">)</span>
 
-<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">p_plot</span><span class="p">,</span> <span class="n">f_plot</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Milk Pooling&quot;</span><span class="p">)</span>
+<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">9</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">p_plot</span><span class="p">,</span> <span class="n">f_plot</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
 <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Pool composition p&quot;</span><span class="p">)</span>
 <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Profit&quot;</span><span class="p">)</span>
 <span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
 
-<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">m_convex</span><span class="o">.</span><span class="n">p</span><span class="p">(),</span> <span class="n">m_convex</span><span class="o">.</span><span class="n">profit</span><span class="p">(),</span> <span class="s1">&#39;ro&#39;</span><span class="p">,</span> <span class="n">ms</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="n">m_convex</span><span class="o">.</span><span class="n">profit</span><span class="p">(),</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;--&#39;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">m_convex</span><span class="o">.</span><span class="n">p</span><span class="p">(),</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;--&#39;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="s2">&quot;convex approximation&quot;</span><span class="p">,</span> <span class="n">xy</span><span class="o">=</span><span class="p">(</span><span class="n">m_convex</span><span class="o">.</span><span class="n">p</span><span class="p">(),</span> <span class="n">m_convex</span><span class="o">.</span><span class="n">profit</span><span class="p">()),</span>
-            <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mf">0.036</span><span class="p">,</span> <span class="mi">106000</span><span class="p">),</span> <span class="n">ha</span><span class="o">=</span><span class="s2">&quot;right&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">,</span>
-            <span class="n">arrowprops</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">shrink</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">headwidth</span><span class="o">=</span><span class="mi">5</span><span class="p">))</span>
-
-<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">m_est</span><span class="o">.</span><span class="n">p</span><span class="p">(),</span> <span class="n">m_est</span><span class="o">.</span><span class="n">profit</span><span class="p">(),</span> <span class="s1">&#39;go&#39;</span><span class="p">,</span> <span class="n">ms</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="n">m_est</span><span class="o">.</span><span class="n">profit</span><span class="p">(),</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;g&#39;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;--&#39;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="s2">&quot;local maxima&quot;</span><span class="p">,</span> <span class="n">xy</span><span class="o">=</span><span class="p">(</span><span class="n">m_convex</span><span class="o">.</span><span class="n">p</span><span class="p">(),</span> <span class="n">m_est</span><span class="o">.</span><span class="n">profit</span><span class="p">()),</span>
-            <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mf">0.045</span><span class="p">,</span> <span class="mi">105000</span><span class="p">),</span> <span class="n">ha</span><span class="o">=</span><span class="s2">&quot;left&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">,</span>
-            <span class="n">arrowprops</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">shrink</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">headwidth</span><span class="o">=</span><span class="mi">5</span><span class="p">))</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">m_convex</span><span class="o">.</span><span class="n">p</span><span class="p">(),</span> <span class="n">m_convex</span><span class="o">.</span><span class="n">profit</span><span class="p">(),</span> <span class="s2">&quot;ro&quot;</span><span class="p">,</span> <span class="n">ms</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="n">m_convex</span><span class="o">.</span><span class="n">profit</span><span class="p">(),</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;--&quot;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">m_convex</span><span class="o">.</span><span class="n">p</span><span class="p">(),</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;--&quot;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span>
+    <span class="s2">&quot;convex approximation&quot;</span><span class="p">,</span>
+    <span class="n">xy</span><span class="o">=</span><span class="p">(</span><span class="n">m_convex</span><span class="o">.</span><span class="n">p</span><span class="p">(),</span> <span class="n">m_convex</span><span class="o">.</span><span class="n">profit</span><span class="p">()),</span>
+    <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mf">0.036</span><span class="p">,</span> <span class="mi">106000</span><span class="p">),</span>
+    <span class="n">ha</span><span class="o">=</span><span class="s2">&quot;right&quot;</span><span class="p">,</span>
+    <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">,</span>
+    <span class="n">arrowprops</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">shrink</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">headwidth</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">),</span>
+<span class="p">)</span>
+
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">m_est</span><span class="o">.</span><span class="n">p</span><span class="p">(),</span> <span class="n">m_est</span><span class="o">.</span><span class="n">profit</span><span class="p">(),</span> <span class="s2">&quot;go&quot;</span><span class="p">,</span> <span class="n">ms</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="n">m_est</span><span class="o">.</span><span class="n">profit</span><span class="p">(),</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;g&quot;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;--&quot;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span>
+    <span class="s2">&quot;local max found&quot;</span><span class="p">,</span>
+    <span class="n">xy</span><span class="o">=</span><span class="p">(</span><span class="n">m_convex</span><span class="o">.</span><span class="n">p</span><span class="p">(),</span> <span class="n">m_est</span><span class="o">.</span><span class="n">profit</span><span class="p">()),</span>
+    <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mf">0.045</span><span class="p">,</span> <span class="mi">105000</span><span class="p">),</span>
+    <span class="n">ha</span><span class="o">=</span><span class="s2">&quot;left&quot;</span><span class="p">,</span>
+    <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">,</span>
+    <span class="n">arrowprops</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">shrink</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">headwidth</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">),</span>
+<span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="o">.</span><span class="n">update</span><span class="p">({</span><span class="s2">&quot;font.size&quot;</span><span class="p">:</span> <span class="mi">14</span><span class="p">})</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../../_images/d017e1b3499b5aded279aa32d081292d3dd5c589a694b990d824710eb2acb898.png" src="../../_images/d017e1b3499b5aded279aa32d081292d3dd5c589a694b990d824710eb2acb898.png" />
-</div>
-</div>
-<p>The result shows the profit if the pooled milk transported from the remote farms has a fat content <span class="math notranslate nohighlight">\(p = 0.04\)</span> them a profit of 100,088 is realized which is better than 81,000 earned for business as usual with just local suppliers, but falls short of the 122,441 earned if the remote milk supply could be transported without pooling.</p>
-<p>The following cell presents a full report of the solution.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">report_solution</span><span class="p">(</span><span class="n">m_est</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Milk Pooling Model
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&#39;Milk pooling v3 (Bilinear formulation)&#39;
 
-Pool composition = 0.04
+Pool composition = 0.0400
 Profit = 100088.24
 
 Supplier Report
@@ -1656,25 +1733,26 @@ <h3>Convex Approximation<a class="headerlink" href="#convex-approximation" title
     </tr>
   </tbody>
 </table>
-</div></div></div>
+</div></div><img alt="../../_images/67fa8136bb2857e049591d92d926a341c23feb07ab4e72ae2367082b6324c3af.png" src="../../_images/67fa8136bb2857e049591d92d926a341c23feb07ab4e72ae2367082b6324c3af.png" />
 </div>
-<p>With regard to the practical impact, the results of using this particular convex approximation are mixed. The approximation successfully produced a value for the pool composition <span class="math notranslate nohighlight">\(p\)</span> which would produce a profit of over 100,088. However, the reported value for <span class="math notranslate nohighlight">\(p\)</span> was actually the smallest of the three local maxima for this problem. This discrepancy may have large consequences regarding the choice of suppliers.</p>
+</div>
+<p>The result shows the profit if the pooled milk transported from the remote farms has a fat content <span class="math notranslate nohighlight">\(p = 0.04\)</span> them a profit of 100,088 is realized which is better than 81,000 earned for business as usual with just local suppliers, but falls short of the 122,441 earned if the remote milk supply could be transported without pooling.</p>
+<p>With regard to the practical impact, the results of using this particular convex approximation are mixed. The approximation successfully produced a value for the pool composition <span class="math notranslate nohighlight">\(p\)</span> which would produce a profit of over <span class="math notranslate nohighlight">\(100088\)</span>. However, the reported value for <span class="math notranslate nohighlight">\(p\)</span> was actually the smallest of the three local maxima for this problem. This discrepancy may have large consequences regarding the choice of suppliers.</p>
 </section>
 </section>
-<section id="global-nonlinear-optimization-gnlo-solution-with-couenne">
-<h2>Global Nonlinear Optimization (GNLO) solution with Couenne<a class="headerlink" href="#global-nonlinear-optimization-gnlo-solution-with-couenne" title="Permalink to this heading">#</a></h2>
-<p>The final version of this milk pooling model returns to the bilinear formulation with pool composition <span class="math notranslate nohighlight">\(p\)</span> as a decision variable. The following Pyomo implementation needs to specify a solver capable of solving the resulting problem. This has been tested with nonlinear solvers <a class="reference external" href="https://github.com/coin-or/Ipopt"><code class="docutils literal notranslate"><span class="pre">ipopt</span></code></a> and <a class="reference external" href="https://github.com/coin-or/Couenne"><code class="docutils literal notranslate"><span class="pre">couenne</span></code></a>. <a class="reference external" href="https://ampl.com/products/solvers/open-source/">Pre-compiled binaries for these solvers can be downloaded from AMPL</a>.</p>
+<section id="nonlinear-optimization-nlo-solution-with-ipopt">
+<h2>Nonlinear Optimization (NLO) solution with ipopt<a class="headerlink" href="#nonlinear-optimization-nlo-solution-with-ipopt" title="Permalink to this heading">#</a></h2>
+<p>The final version of this milk pooling model returns to the bilinear formulation with pool composition <span class="math notranslate nohighlight">\(p\)</span> as a decision variable. The following Pyomo implementation needs to specify a solver capable of solving the resulting problem, in this case we use the nonlinear solver <a class="reference external" href="https://github.com/coin-or/Ipopt"><code class="docutils literal notranslate"><span class="pre">ipopt</span></code></a>.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pyomo.environ</span> <span class="k">as</span> <span class="nn">pyo</span>
-
-<span class="k">def</span> <span class="nf">milk_pooling_bilinear</span><span class="p">():</span>
-    
-    <span class="n">m</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">ConcreteModel</span><span class="p">(</span><span class="s1">&#39;Milk Pooling Model - Bilinear&#39;</span><span class="p">)</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">milk_pooling_bilinear_NLO</span><span class="p">(</span><span class="n">localmin</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+    <span class="n">m</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">ConcreteModel</span><span class="p">(</span><span class="s2">&quot;Milk Pooling Model - Bilinear (NLO)&quot;</span><span class="p">)</span>
 
-    <span class="c1"># define sources </span>
-    <span class="n">m</span><span class="o">.</span><span class="n">L</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Set</span><span class="p">(</span><span class="n">initialize</span><span class="o">=</span><span class="n">suppliers</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">suppliers</span><span class="p">[</span><span class="s2">&quot;location&quot;</span><span class="p">]</span><span class="o">==</span><span class="s2">&quot;local&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span>
-    <span class="n">m</span><span class="o">.</span><span class="n">R</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Set</span><span class="p">(</span><span class="n">initialize</span><span class="o">=</span><span class="n">suppliers</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">suppliers</span><span class="p">[</span><span class="s2">&quot;location&quot;</span><span class="p">]</span><span class="o">==</span><span class="s2">&quot;remote&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span>
+    <span class="c1"># define sources</span>
+    <span class="n">m</span><span class="o">.</span><span class="n">L</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Set</span><span class="p">(</span><span class="n">initialize</span><span class="o">=</span><span class="n">suppliers</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">suppliers</span><span class="p">[</span><span class="s2">&quot;location&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;local&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span>
+    <span class="n">m</span><span class="o">.</span><span class="n">R</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Set</span><span class="p">(</span>
+        <span class="n">initialize</span><span class="o">=</span><span class="n">suppliers</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">suppliers</span><span class="p">[</span><span class="s2">&quot;location&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;remote&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+    <span class="p">)</span>
 
     <span class="c1"># define customers</span>
     <span class="n">m</span><span class="o">.</span><span class="n">C</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Set</span><span class="p">(</span><span class="n">initialize</span><span class="o">=</span><span class="n">customers</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span>
@@ -1683,34 +1761,49 @@ <h2>Global Nonlinear Optimization (GNLO) solution with Couenne<a class="headerli
     <span class="n">m</span><span class="o">.</span><span class="n">x</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">R</span><span class="p">,</span> <span class="n">domain</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">NonNegativeReals</span><span class="p">)</span>
     <span class="n">m</span><span class="o">.</span><span class="n">y</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">C</span><span class="p">,</span> <span class="n">bounds</span><span class="o">=</span><span class="k">lambda</span> <span class="n">m</span><span class="p">,</span> <span class="n">c</span><span class="p">:</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">customers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="s2">&quot;demand&quot;</span><span class="p">]))</span>
     <span class="n">m</span><span class="o">.</span><span class="n">z</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">L</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">C</span><span class="p">,</span> <span class="n">domain</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">NonNegativeReals</span><span class="p">)</span>
-    
     <span class="n">m</span><span class="o">.</span><span class="n">p</span> <span class="o">=</span> <span class="n">pyo</span><span class="o">.</span><span class="n">Var</span><span class="p">(</span><span class="n">bounds</span><span class="o">=</span><span class="p">(</span><span class="n">remote_suppliers</span><span class="p">[</span><span class="s2">&quot;fat&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">remote_suppliers</span><span class="p">[</span><span class="s2">&quot;fat&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()))</span>
 
     <span class="nd">@m</span><span class="o">.</span><span class="n">Objective</span><span class="p">(</span><span class="n">sense</span><span class="o">=</span><span class="n">pyo</span><span class="o">.</span><span class="n">maximize</span><span class="p">)</span>
     <span class="k">def</span> <span class="nf">profit</span><span class="p">(</span><span class="n">m</span><span class="p">):</span>
-        <span class="k">return</span> <span class="o">+</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span><span class="o">*</span><span class="p">(</span><span class="n">customers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="s2">&quot;price&quot;</span><span class="p">]</span> <span class="o">-</span> <span class="n">suppliers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="s2">&quot;cost&quot;</span><span class="p">])</span> <span class="k">for</span> <span class="n">l</span><span class="p">,</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">L</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">C</span><span class="p">)</span> \
-               <span class="o">+</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">*</span><span class="n">customers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="s2">&quot;price&quot;</span><span class="p">]</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">C</span><span class="p">)</span> \
-               <span class="o">-</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">*</span><span class="n">suppliers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">r</span><span class="p">,</span> <span class="s2">&quot;cost&quot;</span><span class="p">]</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">R</span><span class="p">)</span>
+        <span class="k">return</span> <span class="p">(</span>
+            <span class="o">+</span><span class="nb">sum</span><span class="p">(</span>
+                <span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="n">customers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="s2">&quot;price&quot;</span><span class="p">]</span> <span class="o">-</span> <span class="n">suppliers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="s2">&quot;cost&quot;</span><span class="p">])</span>
+                <span class="k">for</span> <span class="n">l</span><span class="p">,</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">L</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">C</span>
+            <span class="p">)</span>
+            <span class="o">+</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">*</span> <span class="n">customers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="s2">&quot;price&quot;</span><span class="p">]</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">C</span><span class="p">)</span>
+            <span class="o">-</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">r</span><span class="p">]</span> <span class="o">*</span> <span class="n">suppliers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">r</span><span class="p">,</span> <span class="s2">&quot;cost&quot;</span><span class="p">]</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">R</span><span class="p">)</span>
+        <span class="p">)</span>
 
     <span class="nd">@m</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">C</span><span class="p">)</span>
     <span class="k">def</span> <span class="nf">customer_demand</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">c</span><span class="p">):</span>
         <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">L</span><span class="p">)</span> <span class="o">+</span> <span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">customers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="s2">&quot;demand&quot;</span><span class="p">]</span>
-    
+
     <span class="nd">@m</span><span class="o">.</span><span class="n">Constraint</span><span class="p">()</span>
-    <span class="k">def</span> <span class="nf">pool_balance</span><span class="p">(</span><span class="n">m</span><span class="p">,):</span>
+    <span class="k">def</span> <span class="nf">pool_balance</span><span class="p">(</span>
+        <span class="n">m</span><span class="p">,</span>
+    <span class="p">):</span>
         <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">r</span><span class="p">]</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">R</span><span class="p">)</span> <span class="o">==</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">C</span><span class="p">)</span>
-    
+
     <span class="nd">@m</span><span class="o">.</span><span class="n">Constraint</span><span class="p">()</span>
     <span class="k">def</span> <span class="nf">pool_quality</span><span class="p">(</span><span class="n">m</span><span class="p">):</span>
-        <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">suppliers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">r</span><span class="p">,</span> <span class="s2">&quot;fat&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">r</span><span class="p">]</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">R</span><span class="p">)</span> <span class="o">==</span> <span class="n">m</span><span class="o">.</span><span class="n">p</span> <span class="o">*</span> <span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">r</span><span class="p">]</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">R</span><span class="p">)</span>
-    
+        <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">suppliers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">r</span><span class="p">,</span> <span class="s2">&quot;fat&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">r</span><span class="p">]</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">R</span><span class="p">)</span> <span class="o">==</span> <span class="n">m</span><span class="o">.</span><span class="n">p</span> <span class="o">*</span> <span class="nb">sum</span><span class="p">(</span>
+            <span class="n">m</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">r</span><span class="p">]</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">R</span>
+        <span class="p">)</span>
+
     <span class="nd">@m</span><span class="o">.</span><span class="n">Constraint</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">C</span><span class="p">)</span>
     <span class="k">def</span> <span class="nf">customer_quality</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">c</span><span class="p">):</span>
-        <span class="k">return</span> <span class="n">m</span><span class="o">.</span><span class="n">p</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">+</span> <span class="nb">sum</span><span class="p">(</span><span class="n">suppliers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="s2">&quot;fat&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">L</span><span class="p">)</span> \
-                 <span class="o">&gt;=</span> <span class="n">customers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="s2">&quot;min_fat&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">L</span><span class="p">)</span> <span class="o">+</span> <span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">])</span>
+        <span class="k">return</span> <span class="n">m</span><span class="o">.</span><span class="n">p</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">+</span> <span class="nb">sum</span><span class="p">(</span>
+            <span class="n">suppliers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="s2">&quot;fat&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">L</span>
+        <span class="p">)</span> <span class="o">&gt;=</span> <span class="n">customers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="s2">&quot;min_fat&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="nb">sum</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">z</span><span class="p">[</span><span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">L</span><span class="p">)</span> <span class="o">+</span> <span class="n">m</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">c</span><span class="p">])</span>
+
+    <span class="c1"># change ipopt options to find local minima instead of the global one</span>
+    <span class="k">if</span> <span class="n">localmin</span><span class="p">:</span>
+        <span class="n">SOLVER_NLO</span><span class="o">.</span><span class="n">options</span><span class="p">[</span><span class="s2">&quot;bound_frac&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.5</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">SOLVER_NLO</span><span class="o">.</span><span class="n">options</span><span class="p">[</span><span class="s2">&quot;bound_frac&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.01</span>
+
+    <span class="n">SOLVER_NLO</span><span class="o">.</span><span class="n">solve</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
 
-    <span class="n">pyo</span><span class="o">.</span><span class="n">SolverFactory</span><span class="p">(</span><span class="n">SOLVER_GNLO</span><span class="p">)</span><span class="o">.</span><span class="n">solve</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
-    
     <span class="k">return</span> <span class="n">m</span>
 </pre></div>
 </div>
@@ -1718,42 +1811,34 @@ <h2>Global Nonlinear Optimization (GNLO) solution with Couenne<a class="headerli
 </div>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">m_global</span> <span class="o">=</span> <span class="n">milk_pooling_bilinear</span><span class="p">()</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">m_global</span> <span class="o">=</span> <span class="n">milk_pooling_bilinear_NLO</span><span class="p">()</span>
+<span class="n">report_solution</span><span class="p">(</span><span class="n">m_global</span><span class="p">)</span>
 
-<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">p_plot</span><span class="p">,</span> <span class="n">f_plot</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Milk Pooling&quot;</span><span class="p">)</span>
+<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">9</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">p_plot</span><span class="p">,</span> <span class="n">f_plot</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
 <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Pool composition p&quot;</span><span class="p">)</span>
 <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Profit&quot;</span><span class="p">)</span>
 <span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
-
-<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">m_convex</span><span class="o">.</span><span class="n">p</span><span class="p">(),</span> <span class="n">m_convex</span><span class="o">.</span><span class="n">profit</span><span class="p">(),</span> <span class="s1">&#39;ro&#39;</span><span class="p">,</span> <span class="n">ms</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="n">m_convex</span><span class="o">.</span><span class="n">profit</span><span class="p">(),</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;--&#39;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">m_convex</span><span class="o">.</span><span class="n">p</span><span class="p">(),</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;--&#39;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="s2">&quot;convex approximation&quot;</span><span class="p">,</span> <span class="n">xy</span><span class="o">=</span><span class="p">(</span><span class="n">m_convex</span><span class="o">.</span><span class="n">p</span><span class="p">(),</span> <span class="n">m_convex</span><span class="o">.</span><span class="n">profit</span><span class="p">()),</span>
-            <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mf">0.036</span><span class="p">,</span> <span class="mi">106000</span><span class="p">),</span> <span class="n">ha</span><span class="o">=</span><span class="s2">&quot;right&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">,</span>
-            <span class="n">arrowprops</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">shrink</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">headwidth</span><span class="o">=</span><span class="mi">5</span><span class="p">))</span>
-
-<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">m_est</span><span class="o">.</span><span class="n">p</span><span class="p">(),</span> <span class="n">m_est</span><span class="o">.</span><span class="n">profit</span><span class="p">(),</span> <span class="s1">&#39;go&#39;</span><span class="p">,</span> <span class="n">ms</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="n">m_est</span><span class="o">.</span><span class="n">profit</span><span class="p">(),</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;g&#39;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;--&#39;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="s2">&quot;local maxima&quot;</span><span class="p">,</span> <span class="n">xy</span><span class="o">=</span><span class="p">(</span><span class="n">m_convex</span><span class="o">.</span><span class="n">p</span><span class="p">(),</span> <span class="n">m_est</span><span class="o">.</span><span class="n">profit</span><span class="p">()),</span>
-            <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mf">0.045</span><span class="p">,</span> <span class="mi">105000</span><span class="p">),</span> <span class="n">ha</span><span class="o">=</span><span class="s2">&quot;left&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">,</span>
-            <span class="n">arrowprops</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">shrink</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">headwidth</span><span class="o">=</span><span class="mi">5</span><span class="p">))</span>
-
-<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">m_global</span><span class="o">.</span><span class="n">p</span><span class="p">(),</span> <span class="n">m_global</span><span class="o">.</span><span class="n">profit</span><span class="p">(),</span> <span class="s1">&#39;bo&#39;</span><span class="p">,</span> <span class="n">ms</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="n">m_global</span><span class="o">.</span><span class="n">profit</span><span class="p">(),</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;--&#39;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="s2">&quot;global maxima&quot;</span><span class="p">,</span> <span class="n">xy</span><span class="o">=</span><span class="p">(</span><span class="n">m_global</span><span class="o">.</span><span class="n">p</span><span class="p">(),</span> <span class="n">m_global</span><span class="o">.</span><span class="n">profit</span><span class="p">()),</span>
-            <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mf">0.025</span><span class="p">,</span> <span class="mi">95000</span><span class="p">),</span> <span class="n">ha</span><span class="o">=</span><span class="s2">&quot;left&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">,</span>
-            <span class="n">arrowprops</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">shrink</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">headwidth</span><span class="o">=</span><span class="mi">5</span><span class="p">))</span>
-
-<span class="n">report_solution</span><span class="p">(</span><span class="n">m_global</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">m_global</span><span class="o">.</span><span class="n">p</span><span class="p">(),</span> <span class="n">m_global</span><span class="o">.</span><span class="n">profit</span><span class="p">(),</span> <span class="s2">&quot;go&quot;</span><span class="p">,</span> <span class="n">ms</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="n">m_global</span><span class="o">.</span><span class="n">profit</span><span class="p">(),</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;g&quot;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;--&quot;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span>
+    <span class="s2">&quot;global max found&quot;</span><span class="p">,</span>
+    <span class="n">xy</span><span class="o">=</span><span class="p">(</span><span class="n">m_global</span><span class="o">.</span><span class="n">p</span><span class="p">(),</span> <span class="n">m_global</span><span class="o">.</span><span class="n">profit</span><span class="p">()),</span>
+    <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mf">0.035</span><span class="p">,</span> <span class="mi">87000</span><span class="p">),</span>
+    <span class="n">ha</span><span class="o">=</span><span class="s2">&quot;left&quot;</span><span class="p">,</span>
+    <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">,</span>
+    <span class="n">arrowprops</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">shrink</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">headwidth</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">),</span>
+<span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="o">.</span><span class="n">update</span><span class="p">({</span><span class="s2">&quot;font.size&quot;</span><span class="p">:</span> <span class="mi">14</span><span class="p">})</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Milk Pooling Model - Bilinear
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&#39;Milk Pooling Model - Bilinear (NLO)&#39;
 
-Pool composition = 0.033
+Pool composition = 0.0330
 Profit = 102833.33
 
 Supplier Report
@@ -1794,19 +1879,19 @@ <h2>Global Nonlinear Optimization (GNLO) solution with Couenne<a class="headerli
       <td>0.045</td>
       <td>45.0</td>
       <td>local</td>
-      <td>6000.0</td>
-      <td>0.0</td>
-      <td>2333.3333</td>
+      <td>6000.0001</td>
+      <td>-0.0</td>
+      <td>2333.3334</td>
       <td>0.0000</td>
-      <td>8333.3333</td>
-      <td>375000.0000</td>
+      <td>8333.3334</td>
+      <td>375000.0037</td>
     </tr>
     <tr>
       <th>Farm B</th>
       <td>0.030</td>
       <td>42.0</td>
       <td>local</td>
-      <td>0.0</td>
+      <td>-0.0000</td>
       <td>0.0</td>
       <td>-0.0000</td>
       <td>0.0000</td>
@@ -1818,19 +1903,19 @@ <h2>Global Nonlinear Optimization (GNLO) solution with Couenne<a class="headerli
       <td>0.033</td>
       <td>37.0</td>
       <td>remote</td>
-      <td>0.0</td>
+      <td>0.0000</td>
       <td>0.0</td>
       <td>0.0000</td>
       <td>4166.6667</td>
       <td>4166.6667</td>
-      <td>154166.6667</td>
+      <td>154166.6683</td>
     </tr>
     <tr>
       <th>Farm D</th>
       <td>0.050</td>
       <td>45.0</td>
       <td>remote</td>
-      <td>0.0</td>
+      <td>0.0000</td>
       <td>0.0</td>
       <td>0.0000</td>
       <td>-0.0000</td>
@@ -1877,40 +1962,241 @@ <h2>Global Nonlinear Optimization (GNLO) solution with Couenne<a class="headerli
       <td>0.045</td>
       <td>52.0</td>
       <td>6000.0</td>
-      <td>6000.0000</td>
-      <td>0.0</td>
+      <td>6000.0001</td>
+      <td>-0.0</td>
       <td>0.0000</td>
-      <td>6000.0</td>
+      <td>6000.0001</td>
       <td>0.045</td>
-      <td>312000.0</td>
+      <td>312000.0031</td>
     </tr>
     <tr>
       <th>Customer 2</th>
       <td>0.030</td>
       <td>48.0</td>
       <td>2500.0</td>
-      <td>0.0000</td>
+      <td>-0.0000</td>
       <td>0.0</td>
       <td>2500.0000</td>
-      <td>2500.0</td>
+      <td>2500.0000</td>
       <td>0.033</td>
-      <td>120000.0</td>
+      <td>120000.0012</td>
     </tr>
     <tr>
       <th>Customer 3</th>
       <td>0.040</td>
       <td>50.0</td>
       <td>4000.0</td>
-      <td>2333.3333</td>
+      <td>2333.3334</td>
       <td>-0.0</td>
       <td>1666.6667</td>
+      <td>4000.0000</td>
+      <td>0.040</td>
+      <td>200000.0020</td>
+    </tr>
+  </tbody>
+</table>
+</div></div><img alt="../../_images/18c4ef7055cd03606fdb28a61dcf9e561a8854e890c47a2aab39c8a799392ef2.png" src="../../_images/18c4ef7055cd03606fdb28a61dcf9e561a8854e890c47a2aab39c8a799392ef2.png" />
+</div>
+</div>
+<p>In this case, the convergence to the global optimum was pure luck. The way general non-linear optimization solvers work is that they begin from an initial solution and improve from then on, stopping when a set of conditions, known as the <strong>KKT conditions</strong>, is met. If we change the <code class="docutils literal notranslate"><span class="pre">ipopt</span></code> settings for how to pick the initial point, we can end up in one of the suboptimal local minima.</p>
+<p>Setting the <code class="docutils literal notranslate"><span class="pre">localmin</span></code> optional argument to <code class="docutils literal notranslate"><span class="pre">True</span></code>, we can trigger this behavior and obtain a solution that is not the global optimum.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">m_global2</span> <span class="o">=</span> <span class="n">milk_pooling_bilinear_NLO</span><span class="p">(</span><span class="n">localmin</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">report_solution</span><span class="p">(</span><span class="n">m_global2</span><span class="p">)</span>
+
+<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">9</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">p_plot</span><span class="p">,</span> <span class="n">f_plot</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Pool composition p&quot;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Profit&quot;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">m_global</span><span class="o">.</span><span class="n">p</span><span class="p">(),</span> <span class="n">m_global</span><span class="o">.</span><span class="n">profit</span><span class="p">(),</span> <span class="s2">&quot;bo&quot;</span><span class="p">,</span> <span class="n">ms</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="n">m_global</span><span class="o">.</span><span class="n">profit</span><span class="p">(),</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;b&quot;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;--&quot;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span>
+    <span class="s2">&quot;global max&quot;</span><span class="p">,</span>
+    <span class="n">xy</span><span class="o">=</span><span class="p">(</span><span class="n">m_global</span><span class="o">.</span><span class="n">p</span><span class="p">(),</span> <span class="n">m_global</span><span class="o">.</span><span class="n">profit</span><span class="p">()),</span>
+    <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mf">0.0245</span><span class="p">,</span> <span class="mi">96000</span><span class="p">),</span>
+    <span class="n">ha</span><span class="o">=</span><span class="s2">&quot;left&quot;</span><span class="p">,</span>
+    <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">,</span>
+    <span class="n">arrowprops</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">shrink</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">headwidth</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">),</span>
+<span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">m_global2</span><span class="o">.</span><span class="n">p</span><span class="p">(),</span> <span class="n">m_global2</span><span class="o">.</span><span class="n">profit</span><span class="p">(),</span> <span class="s2">&quot;go&quot;</span><span class="p">,</span> <span class="n">ms</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="n">m_global2</span><span class="o">.</span><span class="n">profit</span><span class="p">(),</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;g&quot;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;--&quot;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span>
+    <span class="s2">&quot;local max found&quot;</span><span class="p">,</span>
+    <span class="n">xy</span><span class="o">=</span><span class="p">(</span><span class="n">m_global2</span><span class="o">.</span><span class="n">p</span><span class="p">(),</span> <span class="n">m_global2</span><span class="o">.</span><span class="n">profit</span><span class="p">()),</span>
+    <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mf">0.0495</span><span class="p">,</span> <span class="mi">93000</span><span class="p">),</span>
+    <span class="n">ha</span><span class="o">=</span><span class="s2">&quot;left&quot;</span><span class="p">,</span>
+    <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">,</span>
+    <span class="n">arrowprops</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">shrink</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">headwidth</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">),</span>
+<span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="o">.</span><span class="n">update</span><span class="p">({</span><span class="s2">&quot;font.size&quot;</span><span class="p">:</span> <span class="mi">14</span><span class="p">})</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&#39;Milk Pooling Model - Bilinear (NLO)&#39;
+
+Pool composition = 0.0450
+Profit = 101392.16
+
+Supplier Report
+</pre></div>
+</div>
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>fat</th>
+      <th>cost</th>
+      <th>location</th>
+      <th>Customer 1</th>
+      <th>Customer 2</th>
+      <th>Customer 3</th>
+      <th>Pool</th>
+      <th>Amount</th>
+      <th>Expense</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>Farm A</th>
+      <td>0.045</td>
+      <td>45.0</td>
+      <td>local</td>
+      <td>0.0</td>
+      <td>-0.0</td>
+      <td>-0.0000</td>
+      <td>0.0000</td>
+      <td>-0.0000</td>
+      <td>-0.0000</td>
+    </tr>
+    <tr>
+      <th>Farm B</th>
+      <td>0.030</td>
+      <td>42.0</td>
+      <td>local</td>
+      <td>-0.0</td>
+      <td>2500.0</td>
+      <td>1333.3333</td>
+      <td>0.0000</td>
+      <td>3833.3334</td>
+      <td>161000.0016</td>
+    </tr>
+    <tr>
+      <th>Farm C</th>
+      <td>0.033</td>
+      <td>37.0</td>
+      <td>remote</td>
+      <td>0.0</td>
+      <td>0.0</td>
+      <td>0.0000</td>
+      <td>2549.0196</td>
+      <td>2549.0196</td>
+      <td>94313.7265</td>
+    </tr>
+    <tr>
+      <th>Farm D</th>
+      <td>0.050</td>
+      <td>45.0</td>
+      <td>remote</td>
+      <td>0.0</td>
+      <td>0.0</td>
+      <td>0.0000</td>
+      <td>6117.6471</td>
+      <td>6117.6471</td>
+      <td>275294.1203</td>
+    </tr>
+  </tbody>
+</table>
+</div></div><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Customer Report
+</pre></div>
+</div>
+<div class="output text_html"><div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>min_fat</th>
+      <th>price</th>
+      <th>demand</th>
+      <th>Farm A</th>
+      <th>Farm B</th>
+      <th>Pool</th>
+      <th>Amount</th>
+      <th>fat delivered</th>
+      <th>Income</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>Customer 1</th>
+      <td>0.045</td>
+      <td>52.0</td>
+      <td>6000.0</td>
+      <td>0.0</td>
+      <td>-0.0000</td>
+      <td>6000.0001</td>
+      <td>6000.0001</td>
+      <td>0.045</td>
+      <td>312000.0031</td>
+    </tr>
+    <tr>
+      <th>Customer 2</th>
+      <td>0.030</td>
+      <td>48.0</td>
+      <td>2500.0</td>
+      <td>-0.0</td>
+      <td>2500.0000</td>
+      <td>-0.0000</td>
+      <td>2500.0000</td>
+      <td>0.030</td>
+      <td>120000.0012</td>
+    </tr>
+    <tr>
+      <th>Customer 3</th>
+      <td>0.040</td>
+      <td>50.0</td>
       <td>4000.0</td>
+      <td>-0.0</td>
+      <td>1333.3333</td>
+      <td>2666.6667</td>
+      <td>4000.0000</td>
       <td>0.040</td>
-      <td>200000.0</td>
+      <td>200000.0020</td>
     </tr>
   </tbody>
 </table>
-</div></div><img alt="../../_images/88d1a6809f23d9fe83fb9b6e29d2651e45f99d579ba02561430d3c8b6478af22.png" src="../../_images/88d1a6809f23d9fe83fb9b6e29d2651e45f99d579ba02561430d3c8b6478af22.png" />
+</div></div><img alt="../../_images/de0d1bc36bc029e0df68f6768aff1d1f468644f463345d86ad3d3a26131fc3cc.png" src="../../_images/de0d1bc36bc029e0df68f6768aff1d1f468644f463345d86ad3d3a26131fc3cc.png" />
 </div>
 </div>
 </section>
@@ -1922,16 +2208,6 @@ <h2>Concluding Remarks<a class="headerlink" href="#concluding-remarks" title="Pe
 <li><p>Customer 2 receives a blend that is 3.3% exceeding the requirement of 3%. This results in some “give away” of product quality in return for the economic benefits of pooling.</p></li>
 </ul>
 </section>
-<section id="suggested-exercises">
-<h2>Suggested Exercises<a class="headerlink" href="#suggested-exercises" title="Permalink to this heading">#</a></h2>
-<p>This simple model demonstrates practical issues that arise in modeling the non-convex problems. Take time explore the behavior of the model to parameter changes and the nature of the solution.</p>
-<ol class="arabic simple">
-<li><p>Examine the model data and explain why the enhanced profits are observed only for a particular range of values in <span class="math notranslate nohighlight">\(p\)</span>.</p></li>
-<li><p>Think carefully about the non-convex behavior observed in the plot of profit versus parameter <span class="math notranslate nohighlight">\(p\)</span>.  Why are the local maxima located where they are, and how are they related to problem data? Test your hypothesis by changing the problem data.  What happens when the product specification for Customer A is set equal to 0.045? What happens when it is set to 0.04?</p></li>
-<li><p>Revise the Pyomo model using ‘cbc’, ‘gurobi_direct’, ‘ipopt’, and ‘bonmin’ to find a solution. Did you find a solver that could solve this nonlinear problem?</p></li>
-<li><p>The above analysis assumed unlimited transport. If the truck turns out to have a limit of 4,000 units of milk, write the mathematical constraint necessary to introduce that limit into the model. Add that constraint to the Pyomo model and discuss the impact on profit.</p></li>
-</ol>
-</section>
 <section id="bibliographic-notes">
 <h2>Bibliographic Notes<a class="headerlink" href="#bibliographic-notes" title="Permalink to this heading">#</a></h2>
 <p>The pooling and blending is a large scale, high value, fundamental problem of logistics for the process and refining industries. The prototypical examples are the pooling and blending crude oils to meet the feed stock constraints of refineries, and for the pooling of refinery products for pipeline delivery to distribution terminals. Un</p>
@@ -2021,7 +2297,7 @@ <h2>Bibliographic Notes<a class="headerlink" href="#bibliographic-notes" title="
   </div>
   <nav class="bd-toc-nav page-toc">
     <ul class="visible nav section-nav flex-column">
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#problem-pooling-milk-for-wholesale-blending-and-distribution">Problem: Pooling milk for wholesale blending and distribution</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#problem-description-pooling-milk-for-wholesale-blending-and-distribution">Problem description: Pooling milk for wholesale blending and distribution</a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#option-1-business-as-usual">Option 1. Business as usual</a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#option-2-buy-an-additional-truck">Option 2. Buy an additional truck</a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#option-3-pool-delivery-from-remote-suppliers">Option 3. Pool delivery from remote suppliers</a><ul class="nav section-nav flex-column">
@@ -2030,9 +2306,8 @@ <h2>Bibliographic Notes<a class="headerlink" href="#bibliographic-notes" title="
 <li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#convex-approximation">Convex Approximation</a></li>
 </ul>
 </li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#global-nonlinear-optimization-gnlo-solution-with-couenne">Global Nonlinear Optimization (GNLO) solution with Couenne</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#nonlinear-optimization-nlo-solution-with-ipopt">Nonlinear Optimization (NLO) solution with ipopt</a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#concluding-remarks">Concluding Remarks</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#suggested-exercises">Suggested Exercises</a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#bibliographic-notes">Bibliographic Notes</a></li>
 </ul>
   </nav></div>
diff --git a/notebooks/05/ols-regression.html b/notebooks/05/ols-regression.html
index b76107e8..05f64f22 100644
--- a/notebooks/05/ols-regression.html
+++ b/notebooks/05/ols-regression.html
@@ -216,13 +216,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="05.00.html">5. Convex Optimization</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
diff --git a/notebooks/05/refinery-production.html b/notebooks/05/refinery-production.html
index 950f94aa..d13564fb 100644
--- a/notebooks/05/refinery-production.html
+++ b/notebooks/05/refinery-production.html
@@ -214,13 +214,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="05.00.html">5. Convex Optimization</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
diff --git a/notebooks/05/svm.html b/notebooks/05/svm.html
index e69814aa..0fea2be9 100644
--- a/notebooks/05/svm.html
+++ b/notebooks/05/svm.html
@@ -216,13 +216,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="reference internal" href="05.00.html">5. Convex Optimization</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
diff --git a/notebooks/06/06.00.html b/notebooks/06/06.00.html
index 68ca3f70..5a0e7d12 100644
--- a/notebooks/06/06.00.html
+++ b/notebooks/06/06.00.html
@@ -214,13 +214,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
diff --git a/notebooks/06/building-insulation.html b/notebooks/06/building-insulation.html
index 578f12f7..1e2f551b 100644
--- a/notebooks/06/building-insulation.html
+++ b/notebooks/06/building-insulation.html
@@ -216,13 +216,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
diff --git a/notebooks/06/economic-order-quantity.html b/notebooks/06/economic-order-quantity.html
index 0cfcb58a..753172ac 100644
--- a/notebooks/06/economic-order-quantity.html
+++ b/notebooks/06/economic-order-quantity.html
@@ -216,13 +216,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
diff --git a/notebooks/06/investment-wheel.html b/notebooks/06/investment-wheel.html
index 6b2335aa..7fe7cdee 100644
--- a/notebooks/06/investment-wheel.html
+++ b/notebooks/06/investment-wheel.html
@@ -216,13 +216,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
diff --git a/notebooks/06/kelly-criterion.html b/notebooks/06/kelly-criterion.html
index d81f2229..9352ba9d 100644
--- a/notebooks/06/kelly-criterion.html
+++ b/notebooks/06/kelly-criterion.html
@@ -216,13 +216,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
diff --git a/notebooks/06/markowitz_portfolio_revisited.html b/notebooks/06/markowitz_portfolio_revisited.html
index 8a2af21b..5a3cb730 100644
--- a/notebooks/06/markowitz_portfolio_revisited.html
+++ b/notebooks/06/markowitz_portfolio_revisited.html
@@ -216,13 +216,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
diff --git a/notebooks/06/optimal-growth-portfolios.html b/notebooks/06/optimal-growth-portfolios.html
index af17a221..df25db25 100644
--- a/notebooks/06/optimal-growth-portfolios.html
+++ b/notebooks/06/optimal-growth-portfolios.html
@@ -216,13 +216,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
diff --git a/notebooks/06/svm-conic.html b/notebooks/06/svm-conic.html
index ce1920ec..071da874 100644
--- a/notebooks/06/svm-conic.html
+++ b/notebooks/06/svm-conic.html
@@ -216,13 +216,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
diff --git a/notebooks/07/07.00.html b/notebooks/07/07.00.html
index 343f213f..9f464d67 100644
--- a/notebooks/07/07.00.html
+++ b/notebooks/07/07.00.html
@@ -214,13 +214,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
diff --git a/notebooks/07/bim-robustness-analysis.html b/notebooks/07/bim-robustness-analysis.html
index 120dfb03..adf50d23 100644
--- a/notebooks/07/bim-robustness-analysis.html
+++ b/notebooks/07/bim-robustness-analysis.html
@@ -216,13 +216,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
diff --git a/notebooks/07/fleet-assignment.html b/notebooks/07/fleet-assignment.html
index dfc28ec0..a7d7b1ba 100644
--- a/notebooks/07/fleet-assignment.html
+++ b/notebooks/07/fleet-assignment.html
@@ -216,13 +216,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
diff --git a/notebooks/08/08.00.html b/notebooks/08/08.00.html
index daecda5d..424ece65 100644
--- a/notebooks/08/08.00.html
+++ b/notebooks/08/08.00.html
@@ -214,13 +214,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
diff --git a/notebooks/08/bim-robust-optimization.html b/notebooks/08/bim-robust-optimization.html
index 11dc4dcb..e5c2ad2e 100644
--- a/notebooks/08/bim-robust-optimization.html
+++ b/notebooks/08/bim-robust-optimization.html
@@ -216,13 +216,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
diff --git a/notebooks/09/09.00.html b/notebooks/09/09.00.html
index f9dca5b1..b6032f21 100644
--- a/notebooks/09/09.00.html
+++ b/notebooks/09/09.00.html
@@ -214,13 +214,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
diff --git a/notebooks/09/economicdispatch.html b/notebooks/09/economicdispatch.html
index a7ed0166..18195e8d 100644
--- a/notebooks/09/economicdispatch.html
+++ b/notebooks/09/economicdispatch.html
@@ -216,13 +216,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
diff --git a/notebooks/09/markowitz_portfolio_with_chance_constraint.html b/notebooks/09/markowitz_portfolio_with_chance_constraint.html
index 30f31fc5..c4b83759 100644
--- a/notebooks/09/markowitz_portfolio_with_chance_constraint.html
+++ b/notebooks/09/markowitz_portfolio_with_chance_constraint.html
@@ -216,13 +216,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
diff --git a/notebooks/09/pop-up_shop.html b/notebooks/09/pop-up_shop.html
index 12287072..0a23d1eb 100644
--- a/notebooks/09/pop-up_shop.html
+++ b/notebooks/09/pop-up_shop.html
@@ -216,13 +216,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
diff --git a/notebooks/09/seafood.html b/notebooks/09/seafood.html
index 53aa28a0..33877baa 100644
--- a/notebooks/09/seafood.html
+++ b/notebooks/09/seafood.html
@@ -216,13 +216,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
diff --git a/notebooks/10/10.00.html b/notebooks/10/10.00.html
index 3af0e704..cea97b38 100644
--- a/notebooks/10/10.00.html
+++ b/notebooks/10/10.00.html
@@ -214,13 +214,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/power-network.html">Extra material: Energy dispatch problem</a></li>
 
 
 
-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../05/05.00.html">5. Convex Optimization</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-5"><i class="fa-solid fa-chevron-down"></i></label><ul>
diff --git a/notebooks/10/airline-seating.html b/notebooks/10/airline-seating.html
index 2299fd40..eb777aa6 100644
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@@ -216,13 +216,13 @@
 
 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Scheduling as a graph coloring problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/graph-coloring.html">Exam room scheduling</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/cryptocurrency-arbitrage.html">Cryptocurrency arbitrage search</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="../04/mincost-flow.html">Minimum-Cost Flow Problem</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../04/gasoline-distribution.html">Gasoline distribution</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
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diff --git a/notebooks/appendix/functional-programming-pyomo.html b/notebooks/appendix/functional-programming-pyomo.html
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-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Forex Arbitrage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../04/forex-arbitrage.html">Extra material: Forex Arbitrage</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="notebooks/04/forex-arbitrage.html">Forex Arbitrage</a></li>
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22, 25, 26, 27, 29, 30, 32, 33, 34, 36, 37, 38, 39, 40, 42, 44, 45, 46, 47, 48, 49, 53, 55, 57, 58, 60, 62, 63, 64, 65, 66, 67, 69], "evalu": [2, 5, 13, 42, 50, 63], "specif": [2, 3, 5, 6, 8, 10, 13, 14, 15, 17, 20, 25, 29, 30, 34, 35, 37, 38, 39, 41, 42, 45, 48, 53, 55, 57, 59, 60, 62, 63, 65, 67, 68, 69], "i": [2, 5, 6, 7, 8, 9, 12, 13, 14, 15, 16, 18, 20, 21, 22, 23, 24, 25, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 40, 42, 44, 45, 46, 47, 48, 49, 50, 52, 53, 55, 57, 58, 60, 62, 63, 65, 66, 69], "e": [2, 5, 7, 18, 19, 21, 22, 25, 27, 29, 30, 32, 33, 34, 35, 37, 38, 40, 41, 42, 44, 45, 46, 47, 49, 50, 53, 57, 58, 59, 60, 62, 63, 65, 66, 68, 69], "valu": [2, 3, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 52, 53, 55, 57, 58, 60, 62, 63, 65, 66, 68, 69], "earlier": [2, 5, 10, 21, 42, 45, 48, 52, 57, 59], "For": [2, 3, 5, 8, 10, 14, 16, 18, 20, 21, 22, 24, 27, 29, 30, 31, 34, 35, 36, 37, 39, 41, 42, 44, 45, 46, 47, 49, 50, 53, 55, 57, 59, 60, 62, 63, 64, 65, 66, 68, 69], "specifi": [2, 4, 5, 8, 13, 21, 24, 27, 29, 30, 31, 34, 37, 38, 39, 42, 45, 46, 48, 50, 58, 66, 68, 69], "either": [2, 3, 4, 5, 20, 21, 22, 23, 24, 26, 27, 29, 35, 50, 59, 63, 65, 69], "maxim": [2, 3, 4, 5, 6, 8, 9, 10, 17, 18, 20, 23, 26, 29, 30, 31, 35, 37, 38, 39, 41, 46, 47, 48, 49, 55, 58, 60, 62, 63, 64, 68, 69], "minim": [2, 3, 5, 6, 7, 8, 9, 11, 12, 13, 14, 18, 20, 21, 22, 24, 25, 27, 29, 33, 34, 35, 37, 40, 42, 44, 45, 50, 52, 53, 55, 57, 64, 65, 66, 69], "In": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 16, 17, 18, 20, 21, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 39, 40, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 53, 54, 55, 56, 57, 59, 60, 61, 62, 63, 64, 65, 66, 68, 69], "oper": [2, 3, 15, 21, 22, 27, 29, 35, 37, 39, 41, 42, 44, 45, 53, 59, 62, 63, 65, 66, 68], "cost": [2, 3, 4, 5, 6, 11, 12, 14, 15, 18, 20, 26, 28, 29, 30, 31, 33, 35, 37, 39, 41, 44, 45, 52, 53, 57, 59, 60, 63, 64, 65, 66, 68], "number": [2, 3, 4, 5, 8, 9, 10, 13, 16, 17, 18, 19, 20, 22, 24, 25, 27, 29, 31, 33, 35, 37, 38, 40, 42, 43, 44, 46, 47, 48, 49, 50, 51, 53, 54, 55, 56, 58, 59, 60, 61, 62, 63, 64, 65, 66, 69], "satisfi": [2, 3, 9, 15, 16, 18, 19, 20, 27, 29, 30, 31, 32, 34, 39, 42, 46, 47, 49, 57, 59, 62, 65], "custom": [2, 20, 21, 29, 32, 37, 39, 42, 51, 57, 62, 69], "restrict": [2, 16, 25, 30, 33, 35, 46, 49, 53, 55, 68, 69], "possibl": [2, 4, 5, 6, 9, 13, 15, 16, 17, 18, 19, 20, 21, 24, 27, 29, 30, 32, 33, 34, 35, 38, 39, 40, 42, 47, 50, 53, 55, 57, 58, 60, 62, 63, 64, 65, 66, 68, 69], "condit": [2, 3, 8, 15, 18, 23, 24, 26, 27, 29, 30, 42, 44, 50, 55, 59, 64, 68, 69], "must": [2, 3, 5, 19, 21, 23, 26, 27, 29, 31, 32, 35, 38, 39, 44, 53, 63, 64, 65, 66, 68], "requir": [2, 3, 4, 8, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 37, 39, 45, 47, 52, 53, 57, 59, 62, 63, 64, 66, 68, 69], "maximum": [2, 3, 8, 11, 15, 18, 23, 24, 25, 29, 33, 34, 35, 39, 44, 47, 48, 49, 53, 55, 57, 63, 66, 68], "allow": [2, 4, 8, 18, 23, 24, 27, 29, 31, 32, 35, 41, 45, 48, 49, 53, 55, 57, 65, 68], "budget": [2, 12, 37, 52, 63], "demand": [2, 3, 4, 6, 18, 20, 26, 30, 32, 34, 35, 37, 39, 45, 51, 52, 57, 59, 63, 64, 65, 66, 68, 69], "import": [2, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 30, 31, 32, 33, 34, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 55, 58, 59, 60, 63, 64, 69], "met": [2, 35, 57], "capac": [2, 12, 18, 23, 24, 30, 34, 35, 41, 52, 57, 62, 66, 69], "warehous": [2, 18, 45, 51, 60, 69], "exceed": [2, 29, 30, 39, 57], "both": [2, 4, 5, 8, 12, 13, 17, 20, 23, 24, 26, 27, 29, 33, 35, 39, 42, 45, 47, 50, 55, 61, 62, 63], "express": [2, 3, 4, 7, 8, 9, 10, 11, 12, 16, 18, 19, 22, 26, 29, 30, 32, 34, 35, 39, 41, 42, 44, 45, 46, 49, 50, 52, 55, 57, 60, 64, 65, 66, 68, 69], "defin": [2, 4, 5, 8, 9, 12, 13, 14, 16, 20, 22, 25, 26, 27, 32, 33, 35, 37, 39, 40, 41, 42, 45, 46, 50, 53, 55, 57, 62, 63, 65, 66, 68, 69], "feasibl": [2, 5, 8, 9, 17, 20, 30, 35, 37, 41, 44, 46, 47, 49, 53, 60, 63, 65, 66], "region": [2, 8, 20, 32, 35], "set": [2, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 44, 45, 46, 47, 48, 49, 52, 53, 57, 58, 59, 60, 62, 63, 64, 65, 66, 68], "candid": [2, 3, 7, 21, 37], "meet": [2, 3, 5, 12, 18, 22, 25, 29, 30, 32, 35, 37, 39, 43, 52, 57, 59, 65, 66], "best": [2, 5, 7, 8, 29, 30, 35, 44, 57, 63, 66, 69], "global": [2, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 38, 46, 48, 52, 55, 57, 60, 62, 66, 69], "optimum": [2, 41, 44], "among": [2, 3, 7, 11, 14, 15, 21, 22, 26, 31, 32, 33, 47, 48, 49, 62, 64, 65, 69], "appli": [2, 4, 5, 18, 24, 35, 37, 39, 42, 44, 45, 49, 65, 66, 68], "type": [2, 3, 4, 5, 6, 8, 12, 16, 17, 19, 21, 25, 29, 30, 35, 37, 42, 45, 50, 52, 55, 57, 65, 66, 68], "relat": [2, 5, 18, 23, 26, 29, 33, 39, 50, 55, 57, 62, 63, 65], "fundament": [2, 14, 39, 68], "question": [2, 8, 15, 22, 29, 42, 45, 51, 59, 64, 65], "translat": [2, 4, 5, 18, 22, 29, 32, 37, 44], "real": [2, 4, 5, 7, 13, 16, 21, 30, 31, 35, 36, 37, 40, 46, 47, 49, 51, 53, 55, 57, 59, 62, 63, 65], "abstract": [2, 4, 8, 68], "represent": [2, 21, 42, 44, 50], "Not": [2, 29], "aspect": [2, 6, 8, 14, 16, 28, 36, 43, 54, 56, 61], "taken": [2, 29, 33, 63, 65, 69], "account": [2, 10, 18, 23, 24, 29, 31, 35, 43, 46, 47, 55, 59, 60, 63, 65, 66, 68], "so": [2, 3, 4, 5, 8, 9, 13, 18, 21, 23, 24, 25, 27, 29, 30, 35, 36, 37, 38, 39, 40, 42, 44, 48, 50, 51, 53, 55, 57, 62, 63, 66, 68], "mani": [2, 3, 4, 8, 16, 19, 20, 22, 23, 27, 29, 30, 31, 33, 37, 42, 45, 46, 47, 49, 50, 52, 53, 55, 59, 62, 63, 65, 66, 69], "made": [2, 3, 10, 12, 18, 20, 24, 29, 52, 59, 63, 66], "step": [2, 3, 4, 8, 9, 12, 14, 15, 18, 21, 29, 45, 46, 52, 53, 62, 63, 68, 69], "typic": [2, 21, 35, 57, 59, 63, 68, 69], "signific": [2, 5, 8, 41, 45, 46, 47, 49, 50], "impact": [2, 7, 39, 51, 64], "approach": [2, 5, 7, 8, 10, 13, 14, 17, 19, 20, 22, 26, 27, 30, 33, 37, 39, 44, 46, 49, 57, 63, 65, 68], "There": [2, 5, 18, 19, 22, 25, 26, 29, 30, 35, 37, 39, 44, 45, 59, 63, 68, 69], "multipl": [2, 3, 12, 15, 18, 27, 29, 31, 32, 34, 37, 39, 69], "equival": [2, 5, 8, 18, 20, 22, 33, 42, 44, 45, 53, 55, 58, 62, 69], "formul": [2, 3, 4, 6, 8, 9, 11, 13, 14, 15, 16, 18, 22, 23, 26, 27, 28, 29, 30, 32, 35, 36, 39, 40, 44, 49, 51, 55, 57, 59, 60, 62, 65, 66, 69], "conceptu": 2, "same": [2, 3, 4, 5, 10, 11, 13, 14, 15, 18, 19, 21, 22, 25, 27, 29, 30, 31, 32, 33, 34, 40, 42, 45, 46, 50, 53, 55, 57, 60, 63, 65, 66, 68, 69], "comput": [2, 4, 5, 7, 8, 14, 15, 18, 22, 23, 29, 30, 31, 35, 37, 39, 42, 45, 46, 49, 50, 52, 59, 62, 64, 65, 66, 68, 69], "complex": [2, 4, 5, 15, 21, 22, 23, 29, 31, 37, 68, 69], "mai": [2, 3, 4, 5, 10, 11, 12, 15, 16, 17, 18, 20, 21, 23, 27, 29, 31, 32, 35, 37, 39, 41, 42, 45, 46, 50, 52, 55, 57, 58, 64, 68], "differ": [2, 3, 5, 8, 10, 11, 12, 13, 14, 15, 17, 18, 19, 27, 29, 30, 32, 33, 34, 35, 37, 39, 40, 41, 42, 44, 45, 48, 49, 50, 51, 53, 55, 57, 59, 60, 63, 64, 65, 66, 69], "interpret": [2, 4, 15, 33, 41], "s": [2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 42, 43, 44, 45, 48, 49, 50, 51, 52, 53, 55, 57, 58, 59, 60, 62, 63, 65, 66, 68, 69], "after": [2, 5, 13, 23, 29, 30, 31, 40, 47, 53, 55, 59, 63, 64, 65, 66, 68], "ha": [2, 3, 4, 5, 7, 10, 12, 14, 18, 21, 22, 24, 25, 26, 27, 29, 30, 31, 33, 35, 36, 37, 38, 39, 41, 42, 44, 45, 46, 47, 49, 50, 52, 53, 55, 57, 59, 60, 62, 63, 65, 66, 68], "back": [2, 5, 13, 23, 29, 31, 33, 42, 50, 63, 68], "origin": [2, 3, 4, 8, 9, 10, 13, 14, 17, 25, 27, 30, 35, 37, 39, 45, 48], "These": [2, 4, 15, 25, 27, 29, 34, 35, 39, 42, 45, 47, 49, 53, 57, 59, 62, 68, 69], "treat": [2, 21, 30, 51, 66], "continu": [2, 12, 18, 22, 23, 39, 42, 44, 50, 52, 60, 66], "process": [2, 4, 23, 29, 39, 41, 42, 45, 50, 53, 55, 68, 69], "sequenti": 2, "final": [2, 4, 5, 12, 14, 21, 22, 23, 29, 39, 45, 62, 65], "turn": [2, 3, 16, 21, 30, 36, 39, 42, 50, 53, 57, 66], "out": [2, 3, 23, 24, 27, 29, 30, 32, 36, 37, 39, 53, 57, 65, 66], "impract": [2, 29], "adjust": [2, 4, 14, 29, 35, 41, 57, 62, 65, 66], "certain": [2, 16, 29, 30, 32, 42, 45, 50, 55, 57, 62, 66], "desir": [2, 3, 12, 18, 25, 30, 36, 52, 68], "properti": [2, 8, 30, 36, 44, 46, 48, 49, 57], "cannot": [2, 8, 12, 16, 21, 22, 25, 29, 33, 35, 44, 52, 53, 60, 62, 63, 66], "effici": [2, 10, 14, 21, 27, 29, 30, 48, 49, 50, 55, 69], "perhap": [2, 19, 32, 37], "re": [2, 69], "includ": [2, 5, 6, 14, 16, 18, 19, 21, 22, 23, 24, 27, 28, 29, 31, 32, 36, 37, 39, 41, 42, 44, 45, 46, 47, 49, 50, 53, 57, 58, 62, 63, 64, 65, 66, 67, 68, 69], "well": [2, 5, 7, 13, 14, 19, 22, 27, 36, 37, 44, 46, 47, 60, 64, 69], "except": [2, 20, 25, 33, 66, 68, 69], "mathematician": 2, "studi": [2, 15, 21, 27, 29, 37, 39, 44, 57, 60, 69], "alwai": [2, 4, 10, 12, 17, 24, 35, 38, 40, 42, 46, 47, 48, 50, 51, 55, 58, 62, 68], "flaw": 2, "challeng": [2, 8, 19, 27, 31, 37, 39, 42], "follow": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 45, 46, 47, 49, 50, 51, 52, 53, 55, 57, 59, 60, 62, 63, 64, 65, 66, 68, 69], "given": [2, 3, 4, 5, 7, 8, 9, 11, 13, 14, 15, 17, 18, 20, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 35, 37, 39, 42, 44, 45, 46, 47, 49, 50, 51, 53, 55, 57, 58, 59, 60, 62, 63, 64, 65, 66, 68, 69], "f": [2, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 52, 53, 55, 57, 58, 59, 60, 62, 63, 64, 65, 66, 68, 69], "x": [2, 4, 6, 7, 8, 10, 11, 12, 13, 15, 16, 17, 18, 20, 23, 24, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44, 45, 47, 48, 50, 52, 53, 55, 57, 58, 59, 60, 63, 64, 65, 66, 68, 69], "mathbb": [2, 6, 8, 13, 16, 17, 18, 26, 35, 37, 38, 40, 42, 45, 46, 47, 49, 50, 55, 57, 58, 59, 60, 62, 65, 66], "r": [2, 4, 6, 8, 13, 14, 16, 18, 19, 21, 22, 24, 25, 27, 29, 31, 32, 35, 37, 38, 39, 40, 42, 44, 45, 46, 47, 48, 49, 50, 53, 55, 58, 59, 60, 62, 63, 64, 65, 66, 68], "subseteq": [2, 16, 33, 35, 53], "n": [2, 6, 7, 8, 9, 13, 16, 17, 18, 19, 20, 21, 23, 24, 25, 27, 29, 30, 31, 32, 33, 35, 37, 38, 39, 40, 42, 45, 46, 47, 48, 49, 50, 52, 53, 55, 57, 58, 60, 62, 63, 69], "being": [2, 5, 6, 8, 33, 35, 38, 39, 50, 53, 66], "y": [2, 4, 7, 8, 9, 13, 14, 18, 19, 20, 23, 24, 26, 27, 29, 30, 31, 32, 34, 35, 38, 39, 40, 41, 42, 44, 45, 47, 48, 49, 50, 55, 57, 59, 60, 63, 64, 66, 68, 69], "geq": [2, 6, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 37, 38, 39, 40, 42, 44, 45, 46, 47, 48, 49, 50, 55, 57, 58, 60, 62, 63, 65, 66, 68], "foral": [2, 4, 7, 11, 12, 13, 14, 15, 18, 19, 20, 22, 23, 24, 25, 27, 29, 30, 32, 33, 34, 35, 37, 38, 39, 40, 42, 45, 46, 48, 49, 50, 53, 55, 57, 59, 62, 63, 65, 66], "least": [2, 6, 7, 12, 18, 22, 24, 33, 35, 36, 37, 39, 52, 53, 63, 64], "good": [2, 4, 5, 7, 15, 20, 27, 29, 33, 34, 38, 46, 49, 59, 63, 64, 69], "similarli": [2, 8, 13, 18, 35, 40], "chang": [2, 4, 5, 10, 14, 15, 21, 23, 24, 27, 29, 30, 32, 33, 35, 38, 39, 40, 41, 48, 51, 53, 55, 57, 58, 62, 63, 65, 68, 69], "last": [2, 12, 18, 22, 23, 24, 38, 39, 52, 53, 55, 64, 66], "leq": [2, 3, 4, 8, 9, 10, 11, 12, 14, 15, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 32, 33, 34, 35, 37, 38, 39, 42, 44, 45, 46, 47, 48, 49, 50, 53, 55, 57, 58, 59, 62, 63, 65, 66, 69], "case": [2, 3, 4, 5, 7, 8, 13, 16, 20, 21, 25, 26, 27, 29, 30, 32, 37, 40, 42, 46, 47, 49, 50, 55, 57, 59, 60, 66, 68, 69], "refer": [2, 3, 4, 6, 8, 13, 16, 20, 24, 27, 29, 32, 34, 39, 40, 42, 47, 50, 65, 69], "begin": [2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 32, 33, 34, 35, 37, 38, 39, 40, 42, 44, 45, 46, 47, 48, 49, 50, 52, 53, 55, 57, 58, 59, 60, 62, 63, 65, 66, 68, 69], "align": [2, 3, 4, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 32, 33, 34, 35, 37, 38, 39, 40, 42, 44, 45, 46, 47, 48, 49, 50, 53, 55, 57, 58, 59, 60, 62, 63, 65, 66, 68, 69], "min": [2, 6, 7, 8, 9, 11, 13, 14, 16, 18, 20, 22, 24, 25, 27, 29, 30, 32, 33, 34, 35, 37, 39, 40, 42, 44, 45, 46, 50, 53, 55, 57, 59, 60, 63, 65, 66, 69], "quad": [2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 20, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 37, 38, 40, 42, 44, 45, 46, 47, 48, 49, 50, 53, 58, 59, 60, 62, 63, 65, 68, 69], "text": [2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 42, 44, 45, 46, 47, 48, 49, 50, 53, 55, 57, 58, 59, 60, 62, 63, 65, 66, 68, 69], "t": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 52, 53, 55, 57, 58, 59, 60, 62, 63, 64, 65, 66, 68, 69], "end": [2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 32, 33, 34, 35, 37, 38, 39, 40, 42, 44, 45, 46, 47, 48, 49, 50, 52, 53, 55, 57, 58, 59, 60, 62, 63, 65, 66, 68, 69], "lead": [2, 14, 17, 18, 19, 20, 21, 22, 26, 29, 36, 39, 42, 44, 50, 55, 57, 63, 66, 69], "techniqu": [2, 7, 11, 13, 21, 22, 23, 26, 32, 42, 45, 49, 57, 63, 65], "introductori": [2, 61], "present": [2, 4, 5, 7, 8, 13, 14, 24, 29, 31, 32, 35, 37, 39, 40, 41, 42, 46, 47, 48, 49, 50, 53, 62, 64, 66, 68, 69], "simpl": [2, 3, 4, 5, 12, 21, 26, 30, 31, 36, 37, 39, 41, 42, 45, 50, 55, 68, 69], "context": [2, 15, 25, 33, 34, 58, 63, 66], "plan": [2, 4, 5, 10, 11, 15, 16, 18, 24, 25, 27, 39, 45, 51, 59, 61], "explain": [2, 7, 35, 39, 50, 57, 66], "structur": [2, 4, 8, 17, 26, 28, 29, 30, 37, 44, 62, 63, 65, 66, 68, 69], "formal": [2, 16], "implement": [2, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 19, 22, 24, 27, 28, 29, 32, 35, 36, 37, 38, 39, 43, 44, 45, 46, 51, 54, 56, 57, 58, 59, 61, 62, 63, 68, 69], "second": [2, 4, 5, 10, 13, 18, 21, 27, 29, 31, 32, 33, 35, 37, 38, 39, 42, 50, 60, 61, 62, 63, 64, 65, 66], "also": [2, 3, 4, 5, 8, 11, 12, 14, 15, 18, 20, 22, 23, 25, 29, 30, 35, 36, 38, 39, 41, 42, 45, 53, 55, 58, 62, 63, 65, 66, 69], "serv": [2, 4, 5, 20, 34, 53, 63], "tutori": [2, 25, 37, 46, 47, 69], "lastli": [2, 3, 62, 63, 66], "discuss": [2, 32, 39, 42, 45, 50, 51, 63], "some": [2, 3, 4, 5, 7, 8, 13, 16, 21, 23, 27, 29, 30, 32, 33, 35, 39, 40, 41, 42, 44, 46, 47, 49, 50, 55, 57, 63, 65, 66, 68, 69], "advanc": [2, 3, 5, 23, 39, 45, 51, 63, 65, 66], "featur": [2, 4, 5, 6, 8, 13, 16, 20, 21, 22, 23, 27, 34, 39, 40, 47, 49, 60, 62, 68, 69], "third": [2, 4, 5, 32], "go": [2, 6, 8, 13, 14, 16, 21, 28, 30, 35, 36, 39, 40, 43, 51, 53, 54, 56, 63, 64, 65, 68], "next": [2, 3, 4, 5, 6, 7, 8, 12, 15, 16, 18, 21, 22, 23, 25, 28, 29, 30, 32, 34, 36, 38, 43, 46, 50, 51, 53, 54, 56, 57, 60, 64, 65, 66], "about": [2, 6, 8, 16, 20, 24, 28, 29, 30, 36, 39, 43, 51, 54, 56, 57, 59, 62, 63, 65], "linear": [2, 3, 5, 7, 8, 10, 11, 12, 13, 14, 15, 17, 19, 20, 22, 23, 26, 29, 31, 35, 36, 39, 40, 41, 55, 57, 60, 61, 62, 63, 65], "compani": [3, 6, 8, 12, 17, 18, 34, 39, 45, 51, 52, 53, 55, 63], "produc": [3, 4, 7, 8, 10, 12, 15, 17, 21, 23, 26, 29, 31, 32, 35, 37, 39, 42, 46, 47, 52, 53, 55, 57, 63, 65, 66, 68], "two": [3, 4, 5, 6, 8, 12, 13, 14, 16, 17, 19, 20, 21, 23, 25, 26, 29, 30, 31, 32, 33, 37, 39, 42, 45, 47, 50, 53, 55, 56, 57, 59, 60, 65, 68, 69], "version": [3, 4, 5, 10, 18, 20, 30, 35, 39, 45, 53, 54, 55, 63, 65, 66, 69], "each": [3, 4, 5, 8, 9, 12, 13, 14, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 42, 46, 47, 50, 52, 53, 55, 57, 58, 59, 60, 62, 63, 64, 65, 66, 68, 69], "raw": [3, 5, 8, 12, 18, 26, 29, 35, 39, 42, 50, 52, 57, 63, 65, 66], "10": [3, 4, 5, 7, 10, 11, 12, 13, 14, 15, 18, 19, 20, 21, 22, 23, 24, 25, 27, 29, 30, 31, 32, 33, 34, 35, 37, 39, 41, 42, 44, 45, 46, 47, 49, 50, 52, 53, 57, 59, 60, 62, 63, 64, 65, 66, 68, 69], "per": [3, 8, 10, 12, 14, 18, 23, 24, 25, 26, 29, 32, 34, 37, 44, 45, 52, 60, 63, 64, 65], "gram": [3, 18, 42, 50], "special": [3, 4, 11, 12, 13, 16, 28, 30, 37, 40, 45, 62, 69], "labor": [3, 4, 26, 63, 68], "finish": [3, 12, 18, 21, 22, 23, 26, 37, 52, 53, 69], "u": [3, 4, 5, 14, 18, 23, 29, 34, 42, 44, 45, 46, 49, 50, 53, 55, 57, 63, 68, 69], "higher": [3, 14, 26, 29, 39, 42, 47, 50, 52, 57, 59, 63, 65], "270": [3, 4, 5, 26, 29, 63, 66, 68], "unit": [3, 4, 5, 10, 12, 14, 15, 18, 23, 24, 26, 29, 31, 32, 34, 35, 39, 44, 45, 47, 52, 57, 58, 59, 60, 62, 63, 64, 65, 68], "one": [3, 4, 5, 6, 8, 9, 13, 14, 15, 17, 18, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 37, 38, 39, 40, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 55, 57, 58, 59, 62, 63, 64, 65, 66], "hour": [3, 4, 21, 24, 25, 26, 53, 63], "b": [3, 4, 5, 6, 7, 8, 13, 15, 16, 18, 19, 21, 22, 25, 26, 29, 32, 34, 35, 37, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 53, 60, 62, 63, 65, 66, 68, 69], "due": [3, 4, 12, 14, 18, 29, 35, 38, 39, 51, 52, 55, 59, 62, 64, 65, 69], "market": [3, 4, 14, 15, 29, 31, 39, 46, 47, 49, 62, 63, 64], "limit": [3, 5, 12, 18, 20, 21, 24, 25, 26, 29, 32, 35, 37, 39, 41, 44, 46, 49, 52, 57, 63, 65, 66, 69], "40": [3, 4, 5, 7, 24, 25, 26, 29, 32, 33, 37, 44, 45, 46, 59, 62, 63, 64, 68, 69], "week": [3, 25, 29, 33, 62], "v": [3, 4, 5, 14, 20, 21, 24, 29, 30, 33, 34, 35, 39, 42, 45, 46, 50, 53, 57, 63, 65, 66, 68, 69], "lower": [3, 4, 5, 12, 14, 21, 29, 31, 32, 37, 39, 40, 55, 59, 62, 64, 65, 69], "unlimit": [3, 4, 26, 37, 39, 63], "210": [3, 4, 5, 26, 62, 63, 64, 68], "9": [3, 4, 5, 7, 8, 9, 10, 11, 12, 14, 15, 17, 19, 20, 21, 22, 23, 24, 25, 27, 29, 30, 31, 32, 34, 35, 37, 38, 39, 41, 42, 45, 50, 51, 52, 53, 55, 62, 63, 66, 68], "1": [3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 33, 34, 37, 38, 40, 41, 42, 45, 46, 47, 48, 49, 50, 52, 53, 55, 57, 58, 59, 60, 61, 63, 65, 66, 68, 69], "summar": [3, 4, 25, 32, 35, 39, 42, 50, 59, 62, 63, 64], "tabl": [3, 4, 18, 21, 22, 24, 26, 27, 31, 42, 46, 52, 53, 55, 59, 60, 62, 64, 69], "g": [3, 4, 5, 8, 14, 18, 20, 22, 27, 29, 30, 31, 32, 33, 34, 35, 39, 40, 42, 44, 45, 50, 53, 55, 60, 65, 66, 68], "hr": [3, 4, 24, 53], "2": [3, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 33, 34, 37, 38, 40, 41, 42, 45, 46, 47, 48, 49, 50, 52, 53, 55, 57, 58, 59, 60, 63, 65, 66, 68, 69], "weekli": [3, 25], "avail": [3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 30, 31, 32, 33, 34, 35, 37, 38, 39, 41, 42, 45, 46, 48, 49, 50, 52, 53, 55, 57, 59, 60, 62, 63, 66, 68], "inventori": [3, 12, 18, 29, 45, 52, 59, 60], "shelf": 3, "life": [3, 22, 30, 36, 44, 53], "order": [3, 4, 10, 12, 18, 21, 22, 25, 30, 32, 35, 37, 41, 42, 43, 50, 52, 53, 59, 63], "left": [3, 7, 8, 9, 10, 12, 13, 21, 22, 23, 24, 25, 27, 30, 32, 37, 39, 40, 42, 44, 45, 47, 49, 50, 52, 53, 57, 60, 62, 63, 65], "over": [3, 4, 10, 11, 14, 18, 21, 23, 25, 29, 30, 35, 37, 39, 41, 42, 45, 47, 50, 52, 59, 62, 63, 64, 68, 69], "discard": 3, "detail": [3, 8, 20, 25, 42, 44, 50, 59], "resourc": [3, 4, 15, 21, 25, 27, 29, 30, 37, 52, 64, 65, 68], "amount": [3, 4, 12, 15, 16, 17, 18, 21, 22, 25, 29, 31, 32, 34, 38, 39, 51, 52, 55, 57, 58, 60, 63, 64, 65, 69], "80": [3, 4, 5, 26, 29, 32, 35, 41, 47, 49, 50, 63, 64, 66, 68], "50": [3, 4, 5, 9, 14, 18, 24, 26, 29, 31, 32, 35, 37, 39, 42, 44, 45, 52, 55, 60, 62, 63, 64, 66, 68, 69], "100": [3, 4, 5, 10, 18, 19, 20, 22, 24, 26, 29, 31, 32, 35, 37, 39, 41, 42, 45, 47, 49, 50, 52, 55, 57, 60, 62, 63, 64, 65, 66, 68, 69], "want": [3, 14, 17, 18, 20, 29, 32, 33, 38, 40, 42, 48, 55, 58, 62, 63, 69], "its": [3, 5, 7, 8, 9, 13, 14, 20, 22, 30, 34, 40, 42, 45, 50, 55, 63, 65, 68, 69], "gross": [3, 46, 47, 49], "profit": [3, 4, 5, 6, 8, 9, 10, 17, 23, 26, 29, 32, 37, 39, 41, 55, 62, 63, 64, 68, 69], "much": [3, 8, 12, 13, 17, 18, 23, 24, 27, 30, 33, 37, 44, 47, 51, 63, 65, 66], "abov": [3, 4, 6, 8, 11, 12, 13, 14, 17, 18, 22, 23, 24, 29, 30, 32, 33, 35, 37, 39, 40, 44, 45, 46, 48, 49, 50, 52, 55, 57, 60, 62, 63, 65, 69], "optim": [3, 4, 5, 9, 10, 11, 13, 17, 20, 21, 23, 24, 25, 27, 29, 30, 31, 32, 33, 34, 40, 41, 45, 46, 52, 53, 55, 57, 59, 61, 67, 69], "reformul": [3, 5, 10, 13, 14, 44, 46, 47, 49, 50, 55, 62], "involv": [3, 5, 6, 8, 11, 21, 31, 39, 42, 45, 46, 47, 49], "few": [3, 4, 5, 29, 32, 33, 42, 62, 68], "crucial": [3, 35], "element": [3, 4, 5, 8, 25, 30, 32, 42, 44, 47, 50, 65, 68, 69], "start": [3, 5, 7, 8, 13, 14, 18, 21, 22, 23, 24, 27, 29, 31, 33, 37, 49, 55, 62, 63], "point": [3, 4, 7, 8, 13, 16, 20, 23, 27, 29, 34, 39, 40, 41, 42, 45, 47, 50, 55, 60, 62, 66, 69], "list": [3, 4, 8, 14, 19, 21, 24, 25, 26, 29, 30, 31, 33, 35, 37, 42, 44, 46, 49, 50, 52, 53, 57, 60, 62, 63, 65, 68, 69], "relev": [3, 4, 18, 20, 27, 29, 66, 68, 69], "hand": [3, 4, 8, 9, 17, 20, 24, 25, 29, 30, 31, 42, 50, 55, 57, 59], "quantiti": [3, 8, 11, 12, 18, 34, 35, 37, 39, 43, 51, 52, 55, 63], "modifi": [3, 5, 17, 20, 21, 23, 29, 31, 42, 55, 57, 62, 65, 66, 68, 69], "achiev": [3, 11, 14, 23, 25, 38, 42, 50, 65, 68], "outcom": [3, 15, 32, 42, 46, 50, 57, 59, 63], "while": [3, 5, 8, 9, 12, 14, 17, 18, 20, 21, 24, 25, 26, 29, 33, 34, 35, 37, 42, 48, 49, 50, 51, 52, 53, 55, 57, 59, 62, 63, 65, 66], "stage": [3, 5, 27, 39, 46, 47, 60, 64, 65, 68, 69], "prove": [3, 8], "redund": [3, 69], "later": [3, 4, 5, 15, 21, 24, 27, 45, 47, 66, 69], "creat": [3, 12, 13, 14, 15, 19, 21, 22, 23, 27, 29, 30, 31, 33, 37, 39, 42, 45, 46, 49, 50, 57, 59, 62, 63, 64, 68, 69], "comprehens": [3, 7, 42, 50], "below": [3, 5, 9, 11, 12, 14, 17, 20, 21, 24, 29, 30, 35, 37, 39, 42, 44, 45, 46, 50, 55, 57, 60, 62, 63, 66, 69], "symbol": [3, 14, 22, 29, 31, 32, 49, 59, 68, 69], "descript": [3, 15, 21, 32, 42, 51, 55, 66], "upper": [3, 4, 5, 9, 12, 14, 21, 22, 23, 24, 29, 32, 33, 37, 38, 39, 40, 44, 45, 52, 55, 60, 68, 69], "bound": [3, 4, 5, 9, 14, 19, 21, 22, 23, 24, 26, 29, 30, 31, 33, 37, 38, 39, 42, 44, 53, 55, 57, 63, 64, 66, 68], "known": [3, 4, 5, 7, 13, 14, 19, 21, 22, 23, 27, 33, 38, 39, 40, 42, 44, 45, 47, 49, 50, 53, 57, 59, 63, 64, 65, 66], "x_m": [3, 5, 60], "0": [3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 52, 53, 55, 57, 58, 59, 60, 62, 63, 64, 65, 66, 68, 69], "x_a": [3, 5], "x_b": [3, 5], "y_u": [3, 5], "y_v": [3, 5], "measur": [3, 7, 10, 14, 23, 24, 25, 29, 39, 42, 50, 51], "max": [3, 4, 7, 8, 9, 10, 11, 14, 15, 17, 21, 22, 23, 24, 25, 26, 27, 29, 35, 37, 38, 39, 41, 42, 44, 46, 47, 48, 50, 55, 57, 58, 59, 60, 62, 63, 64, 65, 66, 68], "equal": [3, 5, 6, 7, 8, 10, 12, 15, 16, 23, 25, 26, 27, 29, 30, 32, 33, 34, 38, 39, 40, 41, 44, 45, 46, 47, 49, 53, 55, 57, 59, 60, 62, 63, 65, 66], "between": [3, 4, 5, 8, 10, 13, 14, 20, 21, 24, 25, 27, 29, 31, 32, 34, 35, 36, 38, 39, 40, 42, 44, 46, 47, 48, 50, 53, 57, 64, 65, 66, 68, 69], "revenu": [3, 4, 5, 10, 11, 41, 59, 62, 64, 68], "written": [3, 4, 5, 15, 42, 45, 58, 59, 62, 68, 69], "As": [3, 4, 5, 8, 14, 18, 21, 25, 29, 30, 31, 39, 42, 45, 46, 50, 53, 59, 60, 63, 66, 69], "shown": [3, 5, 39, 42, 45, 46, 69], "here": [3, 4, 5, 10, 12, 13, 21, 22, 24, 25, 26, 29, 31, 39, 40, 42, 45, 46, 47, 49, 52, 53, 55, 57, 58, 59, 62, 63, 66, 68, 69], "algebra": [3, 5, 19, 41, 42, 45], "combin": [3, 4, 5, 18, 19, 29, 39, 44, 53, 57, 63, 65, 68, 69], "name": [3, 4, 5, 9, 10, 12, 13, 14, 15, 17, 18, 20, 21, 22, 24, 25, 29, 30, 32, 37, 40, 41, 42, 48, 50, 59, 60, 62, 65, 66], "when": [3, 4, 5, 7, 8, 12, 13, 16, 20, 21, 22, 23, 24, 25, 29, 31, 32, 35, 39, 41, 42, 43, 45, 46, 47, 50, 57, 59, 60, 63, 64, 65, 66, 67, 68], "place": [3, 4, 29, 30, 31, 35, 39, 45, 46, 59, 68, 69], "break": [3, 4, 29, 33, 53, 57, 63, 66], "up": [3, 4, 5, 12, 16, 17, 18, 19, 21, 23, 29, 37, 42, 46, 47, 50, 52, 56, 57, 62, 63, 64], "longer": [3, 20, 21, 30, 45, 50, 53, 57, 62, 68], "smaller": [3, 4, 10, 16, 27, 51, 57], "simplifi": [3, 5, 24, 27, 50, 62, 68, 69], "relationship": [3, 5, 13, 22, 24, 25, 27, 29, 31, 44, 45, 46, 69], "inequ": [3, 6, 8, 9, 39, 47, 55, 65], "overal": [3, 8, 15, 25, 39, 44], "now": [3, 4, 5, 7, 8, 12, 14, 18, 20, 21, 24, 25, 27, 29, 30, 31, 32, 33, 34, 35, 37, 42, 45, 46, 47, 48, 50, 52, 53, 55, 57, 58, 59, 60, 62, 63, 65, 66, 68], "readi": [3, 29, 31, 45, 50, 63], "full": [3, 7, 13, 20, 23, 24, 32, 37, 39, 40, 42, 44, 49, 50, 55, 62, 63, 69], "canon": [3, 6, 38], "nonumb": [3, 4, 30], "textbook": [3, 20, 63, 64], "customari": [3, 8, 50], "under": [3, 14, 15, 18, 20, 25, 26, 52, 57, 59, 62, 63, 64, 68], "clearli": [3, 4, 7, 18, 20, 23, 25, 39, 53, 63, 66], "distinguish": [3, 29, 66, 69], "paramet": [3, 4, 5, 18, 21, 23, 32, 33, 35, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 50, 51, 55, 57, 59, 60, 62, 63, 65, 66, 68], "might": [3, 15, 16, 27, 29, 31, 33, 35, 36, 39, 42, 51, 59, 65, 68], "letter": [3, 19, 45, 63], "throughout": [3, 8, 29, 63, 69], "websit": 3, "howev": [3, 4, 8, 13, 15, 16, 17, 18, 20, 28, 29, 30, 31, 33, 35, 36, 39, 42, 44, 45, 48, 49, 50, 53, 55, 57, 59, 62, 63, 65, 66, 69], "stick": 3, "convent": [3, 5, 6, 8, 57, 65, 66], "onli": [3, 4, 7, 8, 9, 16, 18, 20, 21, 24, 25, 26, 27, 29, 30, 32, 33, 35, 37, 39, 42, 45, 49, 53, 55, 57, 59, 62, 63, 64, 65, 69], "explicitli": [3, 8, 17, 29, 30, 31, 50, 53, 62, 64, 69], "domain": [3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44, 45, 48, 52, 53, 55, 57, 58, 59, 60, 62, 63, 65, 66, 68], "vector": [3, 6, 8, 11, 12, 13, 35, 36, 38, 40, 41, 43, 58, 62, 63], "minimum": [3, 11, 12, 14, 16, 21, 24, 25, 28, 29, 33, 35, 37, 39, 44, 53, 62, 64], "even": [3, 5, 20, 25, 29, 30, 39, 42, 48, 50, 53, 62, 63, 65, 66], "like": [3, 4, 8, 9, 11, 12, 14, 18, 29, 30, 31, 32, 37, 39, 42, 50, 52, 62, 63, 65, 66], "immedi": [3, 21, 29], "clear": [3, 9, 17, 36, 57, 63, 69], "what": [3, 8, 15, 21, 22, 25, 29, 31, 39, 41, 43, 46, 47, 51, 55, 57, 59, 62, 63, 64, 66, 68], "exactli": [3, 14, 33, 34, 35, 45, 50, 51, 53, 55, 62, 63, 66], "where": [3, 4, 5, 6, 7, 8, 9, 12, 13, 14, 15, 16, 18, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 35, 37, 38, 39, 41, 42, 44, 45, 46, 47, 48, 49, 50, 53, 55, 57, 59, 60, 62, 63, 65, 66, 68, 69], "come": [3, 4, 14, 15, 30, 36, 42, 50, 63], "plai": [3, 8, 36, 46, 50, 53, 63], "thei": [3, 5, 8, 16, 18, 22, 24, 29, 30, 31, 32, 36, 37, 39, 42, 49, 51, 53, 57, 62, 63, 65, 66, 68, 69], "procedur": [3, 37, 55, 63], "long": [3, 4, 14, 16, 22, 25, 29, 46, 47, 60, 63, 69], "standard": [3, 5, 6, 8, 13, 14, 22, 35, 39, 41, 42, 50, 58, 62, 69], "fashion": [3, 4, 53], "practition": [3, 49], "often": [3, 4, 16, 21, 28, 32, 35, 51, 63, 66, 69], "boil": 3, "down": [3, 17, 23, 30, 35, 47, 62, 63, 64], "pass": [3, 4, 5, 18, 20, 21, 24, 28, 42, 50, 68], "softwar": [3, 26], "regardless": [3, 4, 8, 12, 18, 29, 46, 52, 60], "wa": [3, 7, 14, 25, 29, 30, 32, 35, 36, 37, 39, 45, 46, 47, 48, 49, 53, 55, 59, 63, 65], "stori": [3, 46, 47], "behind": 3, "To": [3, 5, 8, 18, 21, 22, 23, 24, 25, 27, 29, 30, 35, 37, 39, 42, 44, 45, 49, 50, 53, 55, 57, 59, 60, 62, 63, 65, 66, 68], "do": [3, 4, 8, 20, 23, 25, 29, 30, 31, 33, 35, 39, 41, 45, 53, 55, 57, 62, 63, 65, 66, 68, 69], "interfac": [3, 4, 5, 14, 45, 69], "commun": [3, 5, 7, 20, 25, 27, 64], "adopt": [3, 8, 26, 32, 42, 44, 47, 55], "python": [3, 5, 8, 13, 17, 20, 21, 25, 29, 30, 32, 37, 40, 41, 42, 45, 50, 53, 57, 66, 68, 69], "base": [3, 5, 7, 12, 13, 19, 21, 26, 29, 32, 35, 40, 41, 42, 47, 49, 50, 51, 52, 57, 63, 65, 68, 69], "carri": [3, 24, 35], "ll": [4, 31, 39], "revisit": [4, 16, 25, 43, 61], "time": [4, 5, 6, 8, 14, 18, 20, 23, 24, 25, 26, 27, 30, 31, 33, 35, 36, 37, 39, 40, 42, 45, 46, 49, 50, 51, 52, 53, 57, 59, 62, 63, 65, 66, 68, 69], "capabl": [4, 7, 26, 39, 55], "scale": [4, 7, 29, 30, 35, 37, 39, 42, 57, 60, 62, 63, 66], "enabl": [4, 5, 28, 29, 45, 50, 68], "vari": [4, 12, 38, 50, 52, 55, 57, 64], "addit": [4, 6, 7, 12, 15, 18, 21, 23, 25, 27, 37, 42, 45, 47, 49, 50, 59, 62, 63, 66, 68, 69], "compon": [4, 5, 8, 13, 14, 18, 22, 26, 38, 45, 50, 58, 69], "index": [4, 5, 8, 12, 13, 14, 15, 18, 19, 20, 21, 22, 23, 24, 25, 27, 29, 30, 31, 32, 35, 37, 39, 40, 41, 42, 44, 45, 49, 50, 52, 53, 57, 59, 62, 64, 65, 68], "essenti": [4, 22, 29, 30, 37, 42, 50, 53, 55, 63], "scalabl": [4, 6], "maintain": [4, 5, 8, 27, 29, 45, 66, 69], "more": [4, 5, 8, 10, 11, 13, 14, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 31, 32, 35, 37, 38, 39, 42, 44, 45, 46, 47, 48, 49, 50, 52, 53, 55, 57, 59, 60, 62, 63, 64, 65, 66, 68, 69], "analysi": [4, 7, 8, 12, 13, 21, 29, 32, 39, 42, 46, 47, 50, 51, 53, 55, 62, 69], "examin": [4, 7, 17, 39, 49], "identifi": [4, 5, 16, 18, 24, 27, 29, 31, 34, 37, 39, 42, 44, 45, 47, 50, 62, 66, 69], "underli": [4, 40, 63], "verifi": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 38, 48, 50, 52, 53, 55, 57, 59, 60, 62, 66], "high": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 39, 42, 44, 49, 50, 52, 55, 57, 59, 60, 63, 65, 66, 69], "via": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 38, 48, 50, 51, 55, 57, 60, 66], "appsi": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 35, 52, 55, 60, 66], "modul": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 52, 53, 55, 57, 58, 59, 60, 62, 63, 64, 65, 66, 68], "elsewher": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 35, 38, 48, 52, 55, 57, 60, 66, 69], "assum": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 35, 37, 38, 39, 42, 44, 45, 48, 49, 50, 51, 52, 53, 55, 57, 58, 59, 60, 62, 63, 65, 66, 68, 69], "cbc": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 35, 37, 39, 41, 42, 52, 53, 55, 59, 60, 62, 63, 64, 65, 66, 68], "been": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 35, 36, 37, 38, 39, 42, 45, 46, 47, 48, 49, 50, 52, 53, 55, 57, 60, 62, 64, 66, 68, 69], "previous": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 35, 38, 47, 48, 50, 52, 53, 55, 57, 60, 62, 65, 66, 68, 69], "solverfactori": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 52, 53, 55, 57, 58, 59, 60, 62, 63, 64, 65, 66, 68, 69], "It": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 38, 45, 48, 52, 55, 57, 60, 63, 65, 66], "sy": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 52, 53, 55, 57, 58, 59, 60, 62, 63, 64, 65, 66, 68], "pip": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 52, 53, 55, 57, 58, 59, 60, 62, 63, 64, 65, 66, 68], "dev": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 52, 53, 55, 57, 58, 59, 60, 62, 63, 64, 65, 66], "null": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 52, 53, 55, 57, 58, 59, 60, 62, 63, 64, 65, 66], "highspi": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 41, 52, 53, 55, 59, 60, 62, 63, 64, 65, 66], "environ": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 52, 53, 57, 58, 59, 60, 62, 63, 64, 65, 66, 68, 69], "appsi_high": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 41, 53, 55, 59, 60, 62, 63, 64, 65, 66], "els": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 29, 31, 33, 34, 38, 42, 44, 45, 47, 48, 49, 50, 52, 53, 55, 60, 62, 63, 65, 66, 68, 69], "assert": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 38, 48, 52, 55, 57, 60], "basic": [4, 16, 17, 21, 27, 42, 55, 65, 68, 69], "previou": [4, 9, 12, 15, 18, 28, 29, 35, 42, 45, 50, 51, 63], "were": [4, 18, 27, 30, 35, 42, 49, 62, 65], "distinct": [4, 22, 29], "evid": [4, 20], "compris": [4, 21, 23, 25, 31, 37, 40, 59, 68, 69], "abbrevi": [4, 29, 68], "m": [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 52, 53, 55, 57, 58, 59, 60, 62, 63, 64, 65, 66, 68, 69], "factor": [4, 7, 20, 42, 44, 48, 50, 55, 62, 65, 69], "attribut": [4, 5, 7, 14, 29, 35, 37, 45, 65, 68, 69], "built": [4, 20, 57], "librari": [4, 5, 8, 13, 21, 30, 32, 33, 40, 41, 44, 45, 47, 49, 50, 69], "handl": [4, 23, 26, 45, 68], "tabular": [4, 5], "sever": [4, 5, 6, 7, 11, 16, 19, 24, 26, 28, 29, 36, 37, 39, 42, 49, 57, 69], "option": [4, 5, 14, 21, 27, 29, 33, 41, 42, 50, 63, 65, 68, 69], "would": [4, 7, 8, 12, 15, 17, 18, 19, 21, 25, 26, 29, 31, 32, 33, 35, 39, 42, 45, 48, 49, 50, 52, 53, 55, 57, 59, 62, 63, 64, 65, 66, 68], "appropri": [4, 5, 42, 50], "task": [4, 16, 20, 24, 29, 30, 32, 37, 42, 53], "nest": [4, 37, 63, 68, 69], "dictionari": [4, 18, 21, 29, 34, 35, 37, 42, 45, 50, 53, 57, 63, 69], "column": [4, 5, 7, 12, 14, 15, 18, 20, 22, 24, 25, 29, 30, 31, 32, 35, 39, 42, 45, 49, 50, 52, 53, 62, 64, 66, 69], "show": [4, 5, 7, 9, 12, 13, 14, 18, 20, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 33, 35, 37, 38, 39, 40, 42, 44, 45, 47, 48, 49, 51, 52, 53, 55, 57, 59, 60, 62, 63, 66, 68, 69], "repres": [4, 6, 8, 18, 19, 22, 23, 25, 31, 33, 34, 35, 37, 39, 42, 44, 45, 46, 47, 49, 50, 57, 59, 68, 69], "kei": [4, 5, 8, 14, 20, 21, 29, 32, 33, 34, 35, 37, 40, 46, 49, 52, 53, 57, 59, 60, 63, 65, 66, 68, 69], "outermost": 4, "inner": [4, 42, 50], "float": [4, 30, 42, 50, 52, 60, 69], "none": [4, 5, 7, 20, 21, 27, 29, 30, 32, 34, 35, 40, 42, 45, 47, 50, 52, 57, 66, 68], "print": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 22, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 41, 42, 44, 45, 46, 47, 48, 49, 50, 52, 53, 55, 57, 59, 60, 62, 63, 64, 65, 66, 68], "item": [4, 21, 30, 32, 34, 35, 37, 50, 53, 57, 59, 60, 65, 66, 69], "consum": [4, 21, 35, 57, 65, 69], "8s": [4, 29, 49], "row": [4, 5, 7, 8, 22, 29, 31, 35, 42, 53, 63, 66, 69], "label": [4, 7, 13, 15, 24, 27, 29, 31, 32, 33, 34, 35, 37, 39, 40, 41, 42, 44, 45, 50, 52, 53, 57, 60, 66, 69], "4s": 4, "By": [4, 5, 7, 14, 30, 31, 32, 55, 69], "rearrang": [4, 42], "straightforward": [4, 18, 55, 69], "becom": [4, 8, 9, 14, 19, 20, 22, 25, 29, 38, 39, 41, 42, 45, 47, 49, 50, 57, 58, 59, 60, 65, 66], "collect": [4, 5, 6, 7, 11, 13, 15, 20, 21, 27, 31, 32, 33, 35, 40, 42, 50, 62, 63, 69], "compar": [4, 7, 8, 13, 14, 19, 20, 22, 29, 30, 37, 39, 40, 42, 45, 47, 50, 59, 60, 62, 64, 66], "adapt": [4, 15, 49, 57, 62, 63, 65], "let": [4, 7, 8, 9, 11, 12, 15, 19, 20, 21, 25, 27, 29, 30, 33, 34, 35, 37, 38, 39, 44, 45, 47, 50, 52, 55, 57, 58, 59, 62, 63, 65, 68], "cal": [4, 50], "p": [4, 11, 12, 18, 23, 24, 26, 33, 35, 37, 39, 41, 42, 44, 46, 47, 49, 50, 52, 53, 57, 58, 60, 62, 65, 66, 68], "respect": [4, 5, 8, 14, 18, 21, 26, 27, 29, 30, 32, 34, 35, 37, 39, 42, 46, 55, 57, 62, 63, 64, 66, 68], "x_r": [4, 39], "y_p": 4, "denot": [4, 8, 12, 15, 18, 19, 21, 23, 26, 27, 29, 30, 31, 35, 37, 39, 40, 49, 53, 57, 62, 65, 69], "constrain": [4, 12, 15, 16, 29, 35, 39, 45, 46, 49], "zero": [4, 13, 19, 29, 35, 37, 40, 41, 42, 44, 45, 50, 52, 60, 63, 65, 66], "b_r": 4, "b_p": 4, "b_q": 4, "don": [4, 8, 29, 57, 66, 69], "insert": [4, 5, 32, 45, 47], "larger": [4, 8, 20, 27, 30, 33, 37, 42, 47, 50, 53, 59], "than": [4, 5, 8, 10, 12, 15, 16, 17, 20, 21, 23, 24, 25, 27, 29, 30, 32, 33, 35, 37, 39, 42, 46, 47, 49, 50, 52, 53, 55, 57, 59, 60, 62, 63, 65], "ever": [4, 42, 50], "design": [4, 5, 14, 19, 22, 27, 32, 39, 40, 42, 43, 66, 68], "caus": [4, 5, 39, 68], "ignor": [4, 35, 42, 50, 57], "befor": [4, 13, 20, 21, 22, 30, 33, 39, 40, 42, 46, 53, 57, 59, 60, 62, 63, 65, 66], "sum_": [4, 7, 12, 13, 14, 15, 18, 20, 23, 25, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 42, 44, 45, 46, 47, 48, 49, 50, 53, 57, 58, 59, 62, 63, 65, 66, 69], "c_p": 4, "c_r": 4, "a_": [4, 15, 25, 29, 31, 37], "put": [4, 42, 59, 63, 65], "piec": [4, 21, 37], "flexibl": [4, 32, 35, 36, 63, 68], "particular": [4, 14, 16, 21, 25, 30, 33, 35, 37, 39, 40, 45, 55, 62, 63, 65, 66, 69], "hold": [4, 5, 8, 9, 12, 14, 16, 18, 24, 27, 29, 35, 45, 52, 57, 59, 60, 62, 63, 65, 66], "scienc": [4, 5, 25, 32, 44, 45, 46, 62, 64], "see": [4, 5, 20, 21, 25, 26, 30, 31, 40, 44, 45, 46, 48, 50, 55, 57, 60, 63, 64, 65, 66, 68], "facilit": [4, 8, 14, 69], "construct": [4, 5, 7, 9, 14, 18, 21, 29, 32, 37, 42, 44, 45, 50, 53, 65, 66, 68, 69], "pyo": [4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44, 45, 47, 48, 49, 50, 52, 53, 55, 57, 58, 59, 60, 62, 63, 64, 65, 66, 68], "initi": [4, 7, 8, 12, 13, 14, 15, 18, 19, 20, 21, 22, 23, 24, 25, 27, 29, 30, 31, 32, 33, 35, 37, 38, 39, 40, 41, 42, 46, 48, 50, 52, 53, 55, 57, 58, 59, 60, 62, 63, 64, 65, 66, 68, 69], "assign": [4, 5, 15, 16, 19, 22, 24, 25, 29, 30, 31, 32, 33, 37, 41, 42, 50, 51, 66], "decor": [4, 5, 8, 9, 25, 32, 62], "declar": [4, 5, 35, 65, 66, 69], "return": [4, 5, 7, 8, 9, 10, 11, 12, 13, 15, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 52, 53, 55, 57, 58, 59, 60, 62, 63, 65, 66, 68, 69], "form": [4, 6, 22, 23, 36, 44, 45, 51, 58, 60, 63, 65, 68], "param": [4, 7, 12, 14, 15, 18, 21, 22, 23, 24, 25, 27, 29, 30, 32, 39, 42, 52, 57, 60, 65, 68, 69], "omit": [4, 69], "emb": 4, "extern": [4, 21, 44, 55], "directli": [4, 5, 7, 14, 22, 23, 32, 35, 44, 45, 50, 59], "effect": [4, 5, 21, 23, 34, 44, 46, 47, 53, 68], "doe": [4, 5, 15, 21, 23, 25, 27, 29, 30, 35, 37, 40, 42, 45, 50, 55, 62, 66], "keep": [4, 12, 18, 25, 33, 35, 39, 50, 53, 63, 65, 69], "shorter": 4, "remov": [4, 6, 8, 15, 29, 37, 42, 53], "overhead": 4, "blur": 4, "boundari": [4, 27, 69], "statement": [4, 30, 32, 62], "edit": [4, 37, 46], "locat": [4, 16, 24, 29, 32, 34, 39, 41, 42, 57, 59, 69], "portion": [4, 49], "therebi": 4, "significantli": [4, 8, 27, 30, 39, 42, 50], "improv": [4, 5, 25, 42, 44, 45, 47, 64], "maintainabi": 4, "concern": [4, 30], "overkil": 4, "small": [4, 5, 8, 12, 25, 26, 29, 30, 31, 33, 34, 37, 39, 41, 63, 65, 69], "consider": [4, 14, 15, 21, 25, 32, 42, 59, 69], "note": [4, 5, 9, 11, 12, 13, 14, 15, 17, 18, 20, 24, 25, 32, 33, 34, 35, 37, 40, 41, 42, 48, 55, 57, 60, 62, 66, 68, 69], "becaus": [4, 8, 11, 18, 21, 25, 27, 29, 30, 32, 35, 37, 39, 42, 45, 50, 53, 55, 57, 63, 65, 68, 69], "take": [4, 5, 7, 8, 9, 11, 16, 17, 18, 21, 22, 23, 24, 27, 29, 31, 33, 35, 38, 39, 42, 45, 47, 48, 50, 51, 53, 55, 57, 58, 59, 60, 62, 63, 65, 66, 68], "logic": [4, 5, 8, 12, 17, 18, 19, 22, 26, 29, 52, 55, 69], "def": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 52, 53, 55, 57, 58, 59, 60, 62, 63, 64, 65, 66, 68, 69], "coeffici": [4, 6, 7, 8, 13, 15, 17, 18, 29, 40, 42, 44, 50, 55, 62, 63], "cp": [4, 41], "cr": 4, "argumentn": 4, "var": [4, 5, 7, 8, 9, 10, 11, 12, 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60, 62, 63, 66], "util": [8, 27, 29, 32, 46, 47, 49, 53, 68], "encompass": 8, "advantag": [8, 18, 28, 30, 31, 42, 50, 68], "concis": [8, 20], "space": [8, 16, 44, 45, 53, 62, 68], "clariti": 8, "reveal": [8, 39, 50], "across": [8, 29, 34, 35, 63, 65], "uniform": [8, 23, 32, 39, 45, 46, 60, 63, 65, 69], "furthermor": [8, 18, 23, 44, 69], "unnecessari": [8, 69], "theoret": [8, 52, 60], "streamlin": [8, 21, 69], "Is": [8, 15, 30, 39, 46], "answer": [8, 42, 59], "textit": 8, "sign": [8, 42, 50], "onc": [8, 22, 23, 37, 42, 50, 62, 66], "commit": [8, 31, 65], "deriv": [8, 22, 27, 35, 41, 42, 45, 50, 62], "sort": [8, 14, 20, 21, 25, 29, 33, 42, 46, 50], "consid": [8, 11, 13, 14, 15, 18, 20, 21, 22, 24, 27, 28, 29, 30, 31, 32, 33, 37, 38, 39, 40, 42, 44, 46, 47, 48, 49, 53, 55, 57, 58, 60, 62, 63, 64, 65, 66, 68], "anywai": [8, 30, 63], "without": [8, 15, 23, 27, 32, 33, 37, 39, 42, 46, 47, 50, 53, 59, 63, 68, 69], "Of": [8, 16, 63], "cours": [8, 16, 17, 22, 33, 39, 63, 69], "illustr": [8, 12, 18, 20, 21, 26, 27, 29, 30, 37, 44, 45, 52, 55, 60, 65, 66], "shall": [8, 18, 45, 50, 65], "upon": [8, 21, 57], "had": [8, 17, 18, 29, 42, 50, 59, 65], "mention": [8, 11], "neg": [8, 9, 17, 18, 21, 29, 34, 35, 37, 42, 45, 47, 50, 51, 57, 59, 62, 69], "therefor": [8, 14, 27, 32, 35, 38, 42, 45, 50, 62, 63, 66], "overlin": 8, "side": [8, 9, 14, 25, 29, 30, 42, 55, 57, 68], "One": [8, 9, 15, 19, 21, 22, 27, 29, 30, 35, 37, 38, 63, 65, 68], "inde": [8, 20, 25, 44, 50, 57, 63, 65], "yield": [8, 10, 17, 18, 27, 29, 39, 41, 45, 50, 52, 53, 55, 57, 63, 64, 68], "n_var": 8, "n_constraint": 8, "constraintlist": [8, 11, 60], "optimal_x": 8, "round": [8, 12, 14, 17, 18, 24, 25, 30, 31, 32, 33, 34, 37, 38, 39, 41, 44, 45, 48, 52, 55, 62, 64, 65], "addition": [8, 58, 63], "avoid": [8, 12, 18, 19, 21, 22, 27, 32, 33, 42, 52, 66, 68, 69], "hard": [8, 27, 29, 42, 50, 63], "doc": [8, 37, 45, 48, 69], "lambda_1": 9, "lambda_2": 9, "lambda_3": 9, "lambda_4": 9, "four": [9, 24, 39, 41, 42, 50, 63], "rh": 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41, 42, 44, 45, 46, 47, 49, 50, 53, 55, 57, 59, 60, 62, 63, 64, 65, 66, 68, 69], "specifi": [2, 4, 5, 8, 13, 21, 24, 27, 29, 30, 31, 34, 37, 38, 39, 42, 45, 46, 48, 50, 58, 66, 68, 69], "either": [2, 3, 4, 5, 20, 21, 22, 23, 24, 26, 27, 29, 35, 50, 59, 63, 65, 69], "maxim": [2, 3, 4, 5, 6, 8, 9, 10, 17, 18, 20, 23, 26, 29, 30, 31, 35, 37, 38, 39, 41, 46, 47, 48, 49, 55, 58, 60, 62, 63, 64, 68, 69], "minim": [2, 3, 5, 6, 7, 8, 9, 11, 12, 13, 14, 18, 20, 21, 22, 24, 25, 27, 29, 33, 34, 35, 37, 40, 42, 44, 45, 50, 52, 53, 55, 57, 64, 65, 66, 69], "In": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 16, 17, 18, 20, 21, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 39, 40, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 53, 54, 55, 56, 57, 59, 60, 61, 62, 63, 64, 65, 66, 68, 69], "oper": [2, 3, 15, 21, 22, 27, 29, 35, 37, 39, 41, 42, 44, 45, 53, 59, 62, 63, 65, 66, 68], "cost": [2, 3, 4, 5, 6, 11, 12, 14, 15, 18, 20, 26, 28, 29, 30, 31, 33, 35, 37, 39, 41, 44, 45, 52, 53, 57, 59, 60, 63, 64, 65, 66, 68], "number": [2, 3, 4, 5, 8, 9, 10, 13, 16, 17, 18, 19, 20, 22, 24, 25, 27, 29, 31, 33, 35, 37, 38, 40, 42, 43, 44, 46, 47, 48, 49, 50, 51, 53, 54, 55, 56, 58, 59, 60, 61, 62, 63, 64, 65, 66, 69], "satisfi": [2, 3, 9, 15, 16, 18, 19, 20, 27, 29, 30, 31, 32, 34, 39, 42, 46, 47, 49, 57, 59, 62, 65], "custom": [2, 20, 21, 29, 32, 37, 39, 42, 51, 57, 62, 69], "restrict": [2, 16, 25, 30, 33, 35, 46, 49, 53, 55, 68, 69], "possibl": [2, 4, 5, 6, 9, 13, 15, 16, 17, 18, 19, 20, 21, 24, 27, 29, 30, 32, 33, 34, 35, 38, 39, 40, 42, 47, 50, 53, 55, 57, 58, 60, 62, 63, 64, 65, 66, 68, 69], "condit": [2, 3, 8, 15, 18, 23, 24, 26, 27, 29, 30, 39, 42, 44, 50, 55, 59, 64, 68, 69], "must": [2, 3, 5, 19, 21, 23, 26, 27, 29, 31, 32, 35, 38, 39, 44, 53, 63, 64, 65, 66, 68], "requir": [2, 3, 4, 8, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 32, 37, 39, 45, 47, 52, 53, 57, 59, 62, 63, 64, 66, 68, 69], "maximum": [2, 3, 8, 11, 15, 18, 23, 24, 25, 29, 33, 34, 35, 39, 44, 47, 48, 49, 53, 55, 57, 63, 66, 68], "allow": [2, 4, 8, 18, 23, 24, 27, 29, 31, 32, 35, 41, 45, 48, 49, 53, 55, 57, 65, 68], "budget": [2, 12, 37, 52, 63], "demand": [2, 3, 4, 6, 18, 20, 26, 30, 32, 34, 35, 37, 39, 45, 51, 52, 57, 59, 63, 64, 65, 66, 68, 69], "import": [2, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 30, 31, 32, 33, 34, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 55, 58, 59, 60, 63, 64, 69], "met": [2, 35, 39, 57], "capac": [2, 12, 18, 23, 24, 30, 34, 35, 41, 52, 57, 62, 66, 69], "warehous": [2, 18, 45, 51, 60, 69], "exceed": [2, 29, 30, 39, 57], "both": [2, 4, 5, 8, 12, 13, 17, 20, 23, 24, 26, 27, 29, 33, 35, 39, 42, 45, 47, 50, 55, 61, 62, 63], "express": [2, 3, 4, 7, 8, 9, 10, 11, 12, 16, 18, 19, 22, 26, 29, 30, 32, 34, 35, 39, 41, 42, 44, 45, 46, 49, 50, 52, 55, 57, 60, 64, 65, 66, 68, 69], "defin": [2, 4, 5, 8, 9, 12, 13, 14, 16, 20, 22, 25, 26, 27, 32, 33, 35, 37, 39, 40, 41, 42, 45, 46, 50, 53, 55, 57, 62, 63, 65, 66, 68, 69], "feasibl": [2, 5, 8, 9, 17, 20, 30, 35, 37, 41, 44, 46, 47, 49, 53, 60, 63, 65, 66], "region": [2, 8, 20, 32, 35], "set": [2, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 44, 45, 46, 47, 48, 49, 52, 53, 57, 58, 59, 60, 62, 63, 64, 65, 66, 68], "candid": [2, 3, 7, 21, 37], "meet": [2, 3, 5, 12, 18, 22, 25, 29, 30, 32, 35, 37, 39, 43, 52, 57, 59, 65, 66], "best": [2, 5, 7, 8, 29, 30, 35, 44, 57, 63, 66, 69], "global": [2, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 38, 39, 46, 48, 52, 55, 57, 60, 62, 66, 69], "optimum": [2, 39, 41, 44], "among": [2, 3, 7, 11, 14, 15, 21, 22, 26, 31, 32, 33, 47, 48, 49, 62, 64, 65, 69], "appli": [2, 4, 5, 18, 24, 35, 37, 39, 42, 44, 45, 49, 65, 66, 68], "type": [2, 3, 4, 5, 6, 8, 12, 16, 17, 19, 21, 25, 29, 30, 35, 37, 42, 45, 50, 52, 55, 57, 65, 66, 68], "relat": [2, 5, 18, 23, 26, 29, 33, 50, 55, 57, 62, 63, 65], "fundament": [2, 14, 39, 68], "question": [2, 8, 15, 22, 29, 42, 45, 51, 59, 64, 65], "translat": [2, 4, 5, 18, 22, 29, 32, 37, 44], "real": [2, 4, 5, 7, 13, 16, 21, 30, 31, 35, 36, 37, 40, 46, 47, 49, 51, 53, 55, 57, 59, 62, 63, 65], "abstract": [2, 4, 8, 68], "represent": [2, 21, 42, 44, 50], "Not": [2, 29], "aspect": [2, 6, 8, 14, 16, 28, 36, 43, 54, 56, 61], "taken": [2, 29, 33, 63, 65, 69], "account": [2, 10, 18, 23, 24, 29, 31, 35, 43, 46, 47, 55, 59, 60, 63, 65, 66, 68], "so": [2, 3, 4, 5, 8, 9, 13, 18, 21, 23, 24, 25, 27, 29, 30, 35, 36, 37, 38, 39, 40, 42, 44, 48, 50, 51, 53, 55, 57, 62, 63, 66, 68], "mani": [2, 3, 4, 8, 16, 19, 20, 22, 23, 27, 29, 30, 31, 33, 37, 42, 45, 46, 47, 49, 50, 52, 53, 55, 59, 62, 63, 65, 66, 69], "made": [2, 3, 10, 12, 18, 20, 24, 29, 52, 59, 63, 66], "step": [2, 3, 4, 8, 9, 12, 14, 15, 18, 21, 29, 45, 46, 52, 53, 62, 63, 68, 69], "typic": [2, 21, 35, 57, 59, 63, 68, 69], "signific": [2, 5, 8, 41, 45, 46, 47, 49, 50], "impact": [2, 7, 39, 51, 64], "approach": [2, 5, 7, 8, 10, 13, 14, 17, 19, 20, 22, 26, 27, 30, 33, 37, 39, 44, 46, 49, 57, 63, 65, 68], "There": [2, 5, 18, 19, 22, 25, 26, 29, 30, 35, 37, 39, 44, 45, 59, 63, 68, 69], "multipl": [2, 3, 12, 15, 18, 27, 29, 31, 32, 34, 37, 39, 69], "equival": [2, 5, 8, 18, 20, 22, 33, 42, 44, 45, 53, 55, 58, 62, 69], "formul": [2, 3, 4, 6, 8, 9, 11, 13, 14, 15, 16, 18, 22, 23, 26, 27, 28, 29, 30, 32, 35, 36, 39, 40, 44, 49, 51, 55, 57, 59, 60, 62, 65, 66, 69], "conceptu": 2, "same": [2, 3, 4, 5, 10, 11, 13, 14, 15, 18, 19, 21, 22, 25, 27, 29, 30, 31, 32, 33, 34, 40, 42, 45, 46, 50, 53, 55, 57, 60, 63, 65, 66, 68, 69], "comput": [2, 4, 5, 7, 8, 14, 15, 18, 22, 23, 29, 30, 31, 35, 37, 39, 42, 45, 46, 49, 50, 52, 59, 62, 64, 65, 66, 68, 69], "complex": [2, 4, 5, 15, 21, 22, 23, 29, 31, 37, 68, 69], "mai": [2, 3, 4, 5, 10, 11, 12, 15, 16, 17, 18, 20, 21, 23, 27, 29, 31, 32, 35, 37, 39, 41, 42, 45, 46, 50, 52, 55, 57, 58, 64, 68], "differ": [2, 3, 5, 8, 10, 11, 12, 13, 14, 15, 17, 18, 19, 27, 29, 30, 32, 33, 34, 35, 37, 39, 40, 41, 42, 44, 45, 48, 49, 50, 51, 53, 55, 57, 59, 60, 63, 64, 65, 66, 69], "interpret": [2, 4, 15, 33, 41], "s": [2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 42, 43, 44, 45, 48, 49, 50, 51, 52, 53, 55, 57, 58, 59, 60, 62, 63, 65, 66, 68, 69], "after": [2, 5, 13, 23, 29, 30, 31, 40, 47, 53, 55, 59, 63, 64, 65, 66, 68], "ha": [2, 3, 4, 5, 7, 10, 12, 14, 18, 21, 22, 24, 25, 26, 27, 29, 30, 31, 33, 35, 36, 37, 38, 39, 41, 42, 44, 45, 46, 47, 49, 50, 52, 53, 55, 57, 59, 60, 62, 63, 65, 66, 68], "back": [2, 5, 13, 23, 29, 31, 33, 42, 50, 63, 68], "origin": [2, 3, 4, 8, 9, 10, 13, 14, 17, 25, 27, 30, 35, 37, 39, 45, 48], "These": [2, 4, 15, 25, 27, 29, 34, 35, 39, 42, 45, 47, 49, 53, 57, 59, 62, 68, 69], "treat": [2, 21, 30, 39, 51, 66], "continu": [2, 12, 18, 22, 23, 39, 42, 44, 50, 52, 60, 66], "process": [2, 4, 23, 29, 39, 41, 42, 45, 50, 53, 55, 68, 69], "sequenti": 2, "final": [2, 4, 5, 12, 14, 21, 22, 23, 29, 31, 39, 45, 62, 65], "turn": [2, 3, 16, 21, 30, 36, 42, 50, 53, 57, 66], "out": [2, 3, 23, 24, 27, 29, 30, 32, 36, 37, 53, 57, 65, 66], "impract": [2, 29], "adjust": [2, 4, 14, 29, 35, 41, 57, 62, 65, 66], "certain": [2, 16, 29, 30, 32, 42, 45, 50, 55, 57, 62, 66], "desir": [2, 3, 12, 18, 25, 30, 36, 52, 68], "properti": [2, 8, 30, 36, 44, 46, 48, 49, 57], "cannot": [2, 8, 12, 16, 21, 22, 25, 29, 33, 35, 44, 52, 53, 60, 62, 63, 66], "effici": [2, 10, 14, 21, 27, 29, 30, 48, 49, 50, 55, 69], "perhap": [2, 19, 32, 37], "re": [2, 69], "includ": [2, 5, 6, 14, 16, 18, 19, 21, 22, 23, 24, 27, 28, 29, 31, 32, 36, 37, 39, 41, 42, 44, 45, 46, 47, 49, 50, 53, 57, 58, 62, 63, 64, 65, 66, 67, 68, 69], "well": [2, 5, 7, 13, 14, 19, 22, 27, 36, 37, 44, 46, 47, 60, 64, 69], "except": [2, 20, 25, 33, 66, 68, 69], "mathematician": 2, "studi": [2, 15, 21, 27, 29, 37, 39, 44, 57, 60, 69], "alwai": [2, 4, 10, 12, 17, 24, 35, 38, 40, 42, 46, 47, 48, 50, 51, 55, 58, 62, 68], "flaw": 2, "challeng": [2, 8, 19, 27, 31, 37, 39, 42], "follow": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 45, 46, 47, 49, 50, 51, 52, 53, 55, 57, 59, 60, 62, 63, 64, 65, 66, 68, 69], "given": [2, 3, 4, 5, 7, 8, 9, 11, 13, 14, 15, 17, 18, 20, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 35, 37, 39, 42, 44, 45, 46, 47, 49, 50, 51, 53, 55, 57, 58, 59, 60, 62, 63, 64, 65, 66, 68, 69], "f": [2, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 52, 53, 55, 57, 58, 59, 60, 62, 63, 64, 65, 66, 68, 69], "x": [2, 4, 6, 7, 8, 10, 11, 12, 13, 15, 16, 17, 18, 20, 23, 24, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44, 45, 47, 48, 50, 52, 53, 55, 57, 58, 59, 60, 63, 64, 65, 66, 68, 69], "mathbb": [2, 6, 8, 13, 16, 17, 18, 26, 35, 37, 38, 40, 42, 45, 46, 47, 49, 50, 55, 57, 58, 59, 60, 62, 65, 66], "r": [2, 4, 6, 8, 13, 14, 16, 18, 19, 21, 22, 24, 25, 27, 29, 31, 32, 35, 37, 38, 39, 40, 42, 44, 45, 46, 47, 48, 49, 50, 53, 55, 58, 59, 60, 62, 63, 64, 65, 66, 68], "subseteq": [2, 16, 33, 35, 53], "n": [2, 6, 7, 8, 9, 13, 16, 17, 18, 19, 20, 21, 23, 24, 25, 27, 29, 30, 31, 32, 33, 35, 37, 38, 39, 40, 42, 45, 46, 47, 48, 49, 50, 52, 53, 55, 57, 58, 60, 62, 63, 69], "being": [2, 5, 6, 8, 33, 35, 38, 39, 50, 53, 66], "y": [2, 4, 7, 8, 9, 13, 14, 18, 19, 20, 23, 24, 26, 27, 29, 30, 31, 32, 34, 35, 38, 39, 40, 41, 42, 44, 45, 47, 48, 49, 50, 55, 57, 59, 60, 63, 64, 66, 68, 69], "geq": [2, 6, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 37, 38, 39, 40, 42, 44, 45, 46, 47, 48, 49, 50, 55, 57, 58, 60, 62, 63, 65, 66, 68], "foral": [2, 4, 7, 11, 12, 13, 14, 15, 18, 19, 20, 22, 23, 24, 25, 27, 29, 30, 32, 33, 34, 35, 37, 38, 39, 40, 42, 45, 46, 48, 49, 50, 53, 55, 57, 59, 62, 63, 65, 66], "least": [2, 6, 7, 12, 18, 22, 24, 33, 35, 36, 37, 39, 52, 53, 63, 64], "good": [2, 4, 5, 7, 15, 20, 27, 29, 33, 34, 38, 46, 49, 59, 63, 64, 69], "similarli": [2, 8, 13, 18, 35, 40], "chang": [2, 4, 5, 10, 14, 15, 21, 23, 24, 27, 29, 30, 31, 32, 33, 35, 38, 39, 40, 41, 48, 51, 53, 55, 57, 58, 62, 63, 65, 68, 69], "last": [2, 12, 18, 22, 23, 24, 38, 39, 52, 53, 55, 64, 66], "leq": [2, 3, 4, 8, 9, 10, 11, 12, 14, 15, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 32, 33, 34, 35, 37, 38, 39, 42, 44, 45, 46, 47, 48, 49, 50, 53, 55, 57, 58, 59, 62, 63, 65, 66, 69], "case": [2, 3, 4, 5, 7, 8, 13, 16, 20, 21, 25, 26, 27, 29, 30, 32, 37, 39, 40, 42, 46, 47, 49, 50, 55, 57, 59, 60, 66, 68, 69], "refer": [2, 3, 4, 6, 8, 13, 16, 20, 24, 27, 29, 32, 34, 39, 40, 42, 47, 50, 65, 69], "begin": [2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 32, 33, 34, 35, 37, 38, 39, 40, 42, 44, 45, 46, 47, 48, 49, 50, 52, 53, 55, 57, 58, 59, 60, 62, 63, 65, 66, 68, 69], "align": [2, 3, 4, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 32, 33, 34, 35, 37, 38, 39, 40, 42, 44, 45, 46, 47, 48, 49, 50, 53, 55, 57, 58, 59, 60, 62, 63, 65, 66, 68, 69], "min": [2, 6, 7, 8, 9, 11, 13, 14, 16, 18, 20, 22, 24, 25, 27, 29, 30, 32, 33, 34, 35, 37, 39, 40, 42, 44, 45, 46, 50, 53, 55, 57, 59, 60, 63, 65, 66, 69], "quad": [2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 20, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 37, 38, 39, 40, 42, 44, 45, 46, 47, 48, 49, 50, 53, 58, 59, 60, 62, 63, 65, 68, 69], "text": [2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 42, 44, 45, 46, 47, 48, 49, 50, 53, 55, 57, 58, 59, 60, 62, 63, 65, 66, 68, 69], "t": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 52, 53, 55, 57, 58, 59, 60, 62, 63, 64, 65, 66, 68, 69], "end": [2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 32, 33, 34, 35, 37, 38, 39, 40, 42, 44, 45, 46, 47, 48, 49, 50, 52, 53, 55, 57, 58, 59, 60, 62, 63, 65, 66, 68, 69], "lead": [2, 14, 17, 18, 19, 20, 21, 22, 26, 29, 36, 39, 42, 44, 50, 55, 57, 63, 66, 69], "techniqu": [2, 7, 11, 13, 21, 22, 23, 26, 32, 42, 45, 49, 57, 63, 65], "introductori": [2, 61], "present": [2, 4, 5, 7, 8, 13, 14, 24, 29, 31, 32, 35, 37, 39, 40, 41, 42, 46, 47, 48, 49, 50, 53, 62, 64, 66, 68, 69], "simpl": [2, 3, 4, 5, 12, 21, 26, 30, 31, 36, 37, 39, 41, 42, 45, 50, 55, 68, 69], "context": [2, 15, 25, 33, 34, 58, 63, 66], "plan": [2, 4, 5, 10, 11, 15, 16, 18, 24, 25, 27, 45, 51, 59, 61], "explain": [2, 7, 35, 39, 50, 57, 66], "structur": [2, 4, 8, 17, 26, 28, 29, 30, 37, 44, 62, 63, 65, 66, 68, 69], "formal": [2, 16], "implement": [2, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 19, 22, 24, 27, 28, 29, 32, 35, 36, 37, 38, 39, 43, 44, 45, 46, 51, 54, 56, 57, 58, 59, 61, 62, 63, 68, 69], "second": [2, 4, 5, 10, 13, 18, 21, 27, 29, 31, 32, 33, 35, 37, 38, 39, 42, 50, 60, 61, 62, 63, 64, 65, 66], "also": [2, 3, 4, 5, 8, 11, 12, 14, 15, 18, 20, 22, 23, 25, 29, 30, 35, 36, 38, 39, 41, 42, 45, 53, 55, 58, 62, 63, 65, 66, 69], "serv": [2, 4, 5, 20, 34, 53, 63], "tutori": [2, 25, 37, 46, 47, 69], "lastli": [2, 3, 62, 63, 66], "discuss": [2, 32, 42, 45, 50, 51, 63], "some": [2, 3, 4, 5, 7, 8, 13, 16, 21, 23, 27, 29, 30, 32, 33, 35, 39, 40, 41, 42, 44, 46, 47, 49, 50, 55, 57, 63, 65, 66, 68, 69], "advanc": [2, 3, 5, 23, 39, 45, 51, 63, 65, 66], "featur": [2, 4, 5, 6, 8, 13, 16, 20, 21, 22, 23, 27, 34, 39, 40, 47, 49, 60, 62, 68, 69], "third": [2, 4, 5, 32], "go": [2, 6, 8, 13, 14, 16, 21, 28, 30, 35, 36, 39, 40, 43, 51, 53, 54, 56, 63, 64, 65, 68], "next": [2, 3, 4, 5, 6, 7, 8, 12, 15, 16, 18, 21, 22, 23, 25, 28, 29, 30, 32, 34, 36, 38, 43, 46, 50, 51, 53, 54, 56, 57, 60, 64, 65, 66], "about": [2, 6, 8, 16, 20, 24, 28, 29, 30, 36, 43, 51, 54, 56, 57, 59, 62, 63, 65], "linear": [2, 3, 5, 7, 8, 10, 11, 12, 13, 14, 15, 17, 19, 20, 22, 23, 26, 29, 31, 35, 36, 39, 40, 41, 55, 57, 60, 61, 62, 63, 65], "compani": [3, 6, 8, 12, 17, 18, 34, 39, 45, 51, 52, 53, 55, 63], "produc": [3, 4, 7, 8, 10, 12, 15, 17, 21, 23, 26, 29, 31, 32, 35, 37, 39, 42, 46, 47, 52, 53, 55, 57, 63, 65, 66, 68], "two": [3, 4, 5, 6, 8, 12, 13, 14, 16, 17, 19, 20, 21, 23, 25, 26, 29, 30, 31, 32, 33, 37, 39, 42, 45, 47, 50, 53, 55, 56, 57, 59, 60, 65, 68, 69], "version": [3, 4, 5, 10, 18, 20, 30, 35, 39, 45, 53, 54, 55, 63, 65, 66, 69], "each": [3, 4, 5, 8, 9, 12, 13, 14, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 42, 46, 47, 50, 52, 53, 55, 57, 58, 59, 60, 62, 63, 64, 65, 66, 68, 69], "raw": [3, 5, 8, 12, 18, 26, 29, 35, 39, 42, 50, 52, 57, 63, 65, 66], "10": [3, 4, 5, 7, 10, 11, 12, 13, 14, 15, 18, 19, 20, 21, 22, 23, 24, 25, 27, 29, 30, 31, 32, 33, 34, 35, 37, 39, 41, 42, 44, 45, 46, 47, 49, 50, 52, 53, 57, 59, 60, 62, 63, 64, 65, 66, 68, 69], "per": [3, 8, 10, 12, 14, 18, 23, 24, 25, 26, 29, 32, 34, 37, 44, 45, 52, 60, 63, 64, 65], "gram": [3, 18, 42, 50], "special": [3, 4, 11, 12, 13, 16, 28, 30, 37, 40, 45, 62, 69], "labor": [3, 4, 26, 63, 68], "finish": [3, 12, 18, 21, 22, 23, 26, 37, 52, 53, 69], "u": [3, 4, 5, 14, 18, 23, 29, 34, 42, 44, 45, 46, 49, 50, 53, 55, 57, 63, 68, 69], "higher": [3, 14, 26, 29, 39, 42, 47, 50, 52, 57, 59, 63, 65], "270": [3, 4, 5, 26, 29, 63, 66, 68], "unit": [3, 4, 5, 10, 12, 14, 15, 18, 23, 24, 26, 29, 31, 32, 34, 35, 39, 44, 45, 47, 52, 57, 58, 59, 60, 62, 63, 64, 65, 68], "one": [3, 4, 5, 6, 8, 9, 13, 14, 15, 17, 18, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 37, 38, 39, 40, 42, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 55, 57, 58, 59, 62, 63, 64, 65, 66], "hour": [3, 4, 21, 24, 25, 26, 53, 63], "b": [3, 4, 5, 6, 7, 8, 13, 15, 16, 18, 19, 21, 22, 25, 26, 29, 32, 34, 35, 37, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 53, 60, 62, 63, 65, 66, 68, 69], "due": [3, 4, 12, 14, 18, 29, 35, 38, 39, 51, 52, 55, 59, 62, 64, 65, 69], "market": [3, 4, 14, 15, 29, 31, 39, 46, 47, 49, 62, 63, 64], "limit": [3, 5, 12, 18, 20, 21, 24, 25, 26, 29, 32, 35, 37, 39, 41, 44, 46, 49, 52, 57, 63, 65, 66, 69], "40": [3, 4, 5, 7, 24, 25, 26, 29, 32, 33, 37, 44, 45, 46, 59, 62, 63, 64, 68, 69], "week": [3, 25, 29, 33, 62], "v": [3, 4, 5, 14, 20, 21, 24, 29, 30, 33, 34, 35, 39, 42, 45, 46, 50, 53, 57, 63, 65, 66, 68, 69], "lower": [3, 4, 5, 12, 14, 21, 29, 31, 32, 37, 39, 40, 55, 59, 62, 64, 65, 69], "unlimit": [3, 4, 26, 37, 63], "210": [3, 4, 5, 26, 62, 63, 64, 68], "9": [3, 4, 5, 7, 8, 9, 10, 11, 12, 14, 15, 17, 19, 20, 21, 22, 23, 24, 25, 27, 29, 30, 32, 34, 35, 37, 38, 39, 41, 42, 45, 50, 51, 52, 53, 55, 62, 63, 66, 68], "1": [3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 33, 34, 37, 38, 40, 41, 42, 45, 46, 47, 48, 49, 50, 52, 53, 55, 57, 58, 59, 60, 61, 63, 65, 66, 68, 69], "summar": [3, 4, 25, 32, 35, 39, 42, 50, 59, 62, 63, 64], "tabl": [3, 4, 18, 21, 22, 24, 26, 27, 31, 42, 46, 52, 53, 55, 59, 60, 62, 64, 69], "g": [3, 4, 5, 8, 14, 18, 20, 22, 27, 29, 30, 31, 32, 33, 34, 35, 39, 40, 42, 44, 45, 50, 53, 55, 60, 65, 66, 68], "hr": [3, 4, 24, 53], "2": [3, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 33, 34, 37, 38, 40, 41, 42, 45, 46, 47, 48, 49, 50, 52, 53, 55, 57, 58, 59, 60, 63, 65, 66, 68, 69], "weekli": [3, 25], "avail": [3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 30, 31, 32, 33, 34, 35, 37, 38, 39, 41, 42, 45, 46, 48, 49, 50, 52, 53, 55, 57, 59, 60, 62, 63, 66, 68], "inventori": [3, 12, 18, 29, 45, 52, 59, 60], "shelf": 3, "life": [3, 22, 30, 36, 44, 53], "order": [3, 4, 10, 12, 18, 21, 22, 25, 30, 32, 35, 37, 41, 42, 43, 50, 52, 53, 59, 63], "left": [3, 7, 8, 9, 10, 12, 13, 21, 22, 23, 24, 25, 27, 30, 32, 37, 39, 40, 42, 44, 45, 47, 49, 50, 52, 53, 57, 60, 62, 63, 65], "over": [3, 4, 10, 11, 14, 18, 21, 23, 25, 29, 30, 35, 37, 39, 41, 42, 45, 47, 50, 52, 59, 62, 63, 64, 68, 69], "discard": 3, "detail": [3, 8, 20, 25, 42, 44, 50, 59], "resourc": [3, 4, 15, 21, 25, 27, 29, 30, 37, 52, 64, 65, 68], "amount": [3, 4, 12, 15, 16, 17, 18, 21, 22, 25, 29, 31, 32, 34, 38, 39, 51, 52, 55, 57, 58, 60, 63, 64, 65, 69], "80": [3, 4, 5, 26, 29, 32, 35, 41, 47, 49, 50, 63, 64, 66, 68], "50": [3, 4, 5, 9, 14, 18, 24, 26, 29, 31, 32, 35, 37, 39, 42, 44, 45, 52, 55, 60, 62, 63, 64, 66, 68, 69], "100": [3, 4, 5, 10, 18, 19, 20, 22, 24, 26, 29, 31, 32, 35, 37, 39, 41, 42, 45, 47, 49, 50, 52, 55, 57, 60, 62, 63, 64, 65, 66, 68, 69], "want": [3, 14, 17, 18, 20, 29, 32, 33, 38, 40, 42, 48, 55, 58, 62, 63, 69], "its": [3, 5, 7, 8, 9, 13, 14, 20, 22, 30, 34, 40, 42, 45, 50, 55, 63, 65, 68, 69], "gross": [3, 46, 47, 49], "profit": [3, 4, 5, 6, 8, 9, 10, 17, 23, 26, 29, 32, 37, 39, 41, 55, 62, 63, 64, 68, 69], "much": [3, 8, 12, 13, 17, 18, 23, 24, 27, 30, 33, 37, 44, 47, 51, 63, 65, 66], "abov": [3, 4, 6, 8, 11, 12, 13, 14, 17, 18, 22, 23, 24, 29, 30, 32, 33, 35, 37, 40, 44, 45, 46, 48, 49, 50, 52, 55, 57, 60, 62, 63, 65, 69], "optim": [3, 4, 5, 9, 10, 11, 13, 17, 20, 21, 23, 24, 25, 27, 29, 30, 31, 32, 33, 34, 40, 41, 45, 46, 52, 53, 55, 57, 59, 61, 67, 69], "reformul": [3, 5, 10, 13, 14, 44, 46, 47, 49, 50, 55, 62], "involv": [3, 5, 6, 8, 11, 21, 31, 39, 42, 45, 46, 47, 49], "few": [3, 4, 5, 29, 32, 33, 42, 62, 68], "crucial": [3, 35], "element": [3, 4, 5, 8, 25, 30, 32, 42, 44, 47, 50, 65, 68, 69], "start": [3, 5, 7, 8, 13, 14, 18, 21, 22, 23, 24, 27, 29, 31, 33, 37, 49, 55, 62, 63], "point": [3, 4, 7, 8, 13, 16, 20, 23, 27, 29, 34, 39, 40, 41, 42, 45, 47, 50, 55, 60, 62, 66, 69], "list": [3, 4, 8, 14, 19, 21, 24, 25, 26, 29, 30, 31, 33, 35, 37, 42, 44, 46, 49, 50, 52, 53, 57, 60, 62, 63, 65, 68, 69], "relev": [3, 4, 18, 20, 27, 29, 66, 68, 69], "hand": [3, 4, 8, 9, 17, 20, 24, 25, 29, 30, 31, 42, 50, 55, 57, 59], "quantiti": [3, 8, 11, 12, 18, 34, 35, 37, 39, 43, 51, 52, 55, 63], "modifi": [3, 5, 17, 20, 21, 23, 29, 31, 42, 55, 57, 62, 65, 66, 68, 69], "achiev": [3, 11, 14, 23, 25, 38, 42, 50, 65, 68], "outcom": [3, 15, 32, 42, 46, 50, 57, 59, 63], "while": [3, 5, 8, 9, 12, 14, 17, 18, 20, 21, 24, 25, 26, 29, 33, 34, 35, 37, 42, 48, 49, 50, 51, 52, 53, 55, 57, 59, 62, 63, 65, 66], "stage": [3, 5, 27, 39, 46, 47, 60, 64, 65, 68, 69], "prove": [3, 8], "redund": [3, 69], "later": [3, 4, 5, 15, 21, 24, 27, 45, 47, 66, 69], "creat": [3, 12, 13, 14, 15, 19, 21, 22, 23, 27, 29, 30, 31, 33, 37, 39, 42, 45, 46, 49, 50, 57, 59, 62, 63, 64, 68, 69], "comprehens": [3, 7, 42, 50], "below": [3, 5, 9, 11, 12, 14, 17, 20, 21, 24, 29, 30, 35, 37, 39, 42, 44, 45, 46, 50, 55, 57, 60, 62, 63, 66, 69], "symbol": [3, 14, 22, 29, 31, 32, 49, 59, 68, 69], "descript": [3, 15, 21, 32, 42, 51, 55, 66], "upper": [3, 4, 5, 9, 12, 14, 21, 22, 23, 24, 29, 32, 33, 37, 38, 39, 40, 44, 45, 52, 55, 60, 68, 69], "bound": [3, 4, 5, 9, 14, 19, 21, 22, 23, 24, 26, 29, 30, 31, 33, 37, 38, 39, 42, 44, 53, 55, 57, 63, 64, 66, 68], "known": [3, 4, 5, 7, 13, 14, 19, 21, 22, 23, 27, 33, 38, 39, 40, 42, 44, 45, 47, 49, 50, 53, 57, 59, 63, 64, 65, 66], "x_m": [3, 5, 60], "0": [3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 52, 53, 55, 57, 58, 59, 60, 62, 63, 64, 65, 66, 68, 69], "x_a": [3, 5], "x_b": [3, 5], "y_u": [3, 5], "y_v": [3, 5], "measur": [3, 7, 10, 14, 23, 24, 25, 29, 39, 42, 50, 51], "max": [3, 4, 7, 8, 9, 10, 11, 14, 15, 17, 21, 22, 23, 24, 25, 26, 27, 29, 35, 37, 38, 39, 41, 42, 44, 46, 47, 48, 50, 55, 57, 58, 59, 60, 62, 63, 64, 65, 66, 68], "equal": [3, 5, 6, 7, 8, 10, 12, 15, 16, 23, 25, 26, 27, 29, 30, 32, 33, 34, 38, 39, 40, 41, 44, 45, 46, 47, 49, 53, 55, 57, 59, 60, 62, 63, 65, 66], "between": [3, 4, 5, 8, 10, 13, 14, 20, 21, 24, 25, 27, 29, 31, 32, 34, 35, 36, 38, 39, 40, 42, 44, 46, 47, 48, 50, 53, 57, 64, 65, 66, 68, 69], "revenu": [3, 4, 5, 10, 11, 41, 59, 62, 64, 68], "written": [3, 4, 5, 15, 42, 45, 58, 59, 62, 68, 69], "As": [3, 4, 5, 8, 14, 18, 21, 25, 29, 30, 31, 39, 42, 45, 46, 50, 53, 59, 60, 63, 66, 69], "shown": [3, 5, 39, 42, 45, 46, 69], "here": [3, 4, 5, 10, 12, 13, 21, 22, 24, 25, 26, 29, 31, 39, 40, 42, 45, 46, 47, 49, 52, 53, 55, 57, 58, 59, 62, 63, 66, 68, 69], "algebra": [3, 5, 19, 41, 42, 45], "combin": [3, 4, 5, 18, 19, 29, 39, 44, 53, 57, 63, 65, 68, 69], "name": [3, 4, 5, 9, 10, 12, 13, 14, 15, 17, 18, 20, 21, 22, 24, 25, 29, 30, 32, 37, 40, 41, 42, 48, 50, 59, 60, 62, 65, 66], "when": [3, 4, 5, 7, 8, 12, 13, 16, 20, 21, 22, 23, 24, 25, 29, 31, 32, 35, 39, 41, 42, 43, 45, 46, 47, 50, 57, 59, 60, 63, 64, 65, 66, 67, 68], "place": [3, 4, 29, 30, 31, 35, 39, 45, 46, 59, 68, 69], "break": [3, 4, 29, 33, 53, 57, 63, 66], "up": [3, 4, 5, 12, 16, 17, 18, 19, 21, 23, 29, 37, 39, 42, 46, 47, 50, 52, 56, 57, 62, 63, 64], "longer": [3, 20, 21, 30, 45, 50, 53, 57, 62, 68], "smaller": [3, 4, 10, 16, 27, 51, 57], "simplifi": [3, 5, 24, 27, 50, 62, 68, 69], "relationship": [3, 5, 13, 22, 24, 25, 27, 29, 31, 44, 45, 46, 69], "inequ": [3, 6, 8, 9, 39, 47, 55, 65], "overal": [3, 8, 15, 25, 39, 44], "now": [3, 4, 5, 7, 8, 12, 14, 18, 20, 21, 24, 25, 27, 29, 30, 31, 32, 33, 34, 35, 37, 42, 45, 46, 47, 48, 50, 52, 53, 55, 57, 58, 59, 60, 62, 63, 65, 66, 68], "readi": [3, 29, 31, 45, 50, 63], "full": [3, 7, 13, 20, 23, 24, 32, 37, 39, 40, 42, 44, 49, 50, 55, 62, 63, 69], "canon": [3, 6, 38], "nonumb": [3, 4, 30], "textbook": [3, 20, 63, 64], "customari": [3, 8, 50], "under": [3, 14, 15, 18, 20, 25, 26, 31, 52, 57, 59, 62, 63, 64, 68], "clearli": [3, 4, 7, 18, 20, 23, 25, 39, 53, 63, 66], "distinguish": [3, 29, 66, 69], "paramet": [3, 4, 5, 18, 21, 23, 32, 33, 35, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 50, 51, 55, 57, 59, 60, 62, 63, 65, 66, 68], "might": [3, 15, 16, 27, 29, 31, 33, 35, 36, 39, 42, 51, 59, 65, 68], "letter": [3, 19, 45, 63], "throughout": [3, 8, 29, 63, 69], "websit": 3, "howev": [3, 4, 8, 13, 15, 16, 17, 18, 20, 28, 29, 30, 31, 33, 35, 36, 39, 42, 44, 45, 48, 49, 50, 53, 55, 57, 59, 62, 63, 65, 66, 69], "stick": 3, "convent": [3, 5, 6, 8, 57, 65, 66], "onli": [3, 4, 7, 8, 9, 16, 18, 20, 21, 24, 25, 26, 27, 29, 30, 32, 33, 35, 37, 39, 42, 45, 49, 53, 55, 57, 59, 62, 63, 64, 65, 69], "explicitli": [3, 8, 17, 29, 30, 31, 50, 53, 62, 64, 69], "domain": [3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44, 45, 48, 52, 53, 55, 57, 58, 59, 60, 62, 63, 65, 66, 68], "vector": [3, 6, 8, 11, 12, 13, 35, 36, 38, 40, 41, 43, 58, 62, 63], "minimum": [3, 11, 12, 14, 16, 21, 24, 25, 28, 29, 33, 35, 37, 39, 44, 53, 62, 64], "even": [3, 5, 20, 25, 29, 30, 39, 42, 48, 50, 53, 62, 63, 65, 66], "like": [3, 4, 8, 9, 11, 12, 14, 18, 29, 30, 31, 32, 37, 39, 42, 50, 52, 62, 63, 65, 66], "immedi": [3, 21, 29], "clear": [3, 9, 17, 36, 57, 63, 69], "what": [3, 8, 15, 21, 22, 25, 29, 31, 39, 41, 43, 46, 47, 51, 55, 57, 59, 62, 63, 64, 66, 68], "exactli": [3, 14, 33, 34, 35, 45, 50, 51, 53, 55, 62, 63, 66], "where": [3, 4, 5, 6, 7, 8, 9, 12, 13, 14, 15, 16, 18, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 35, 37, 38, 39, 41, 42, 44, 45, 46, 47, 48, 49, 50, 53, 55, 57, 59, 60, 62, 63, 65, 66, 68, 69], "come": [3, 4, 14, 15, 30, 36, 42, 50, 63], "plai": [3, 8, 36, 46, 50, 53, 63], "thei": [3, 5, 8, 16, 18, 22, 24, 29, 30, 31, 32, 36, 37, 39, 42, 49, 51, 53, 57, 62, 63, 65, 66, 68, 69], "procedur": [3, 37, 55, 63], "long": [3, 4, 14, 16, 22, 25, 29, 46, 47, 60, 63, 69], "standard": [3, 5, 6, 8, 13, 14, 22, 35, 39, 41, 42, 50, 58, 62, 69], "fashion": [3, 4, 53], "practition": [3, 49], "often": [3, 4, 16, 21, 28, 32, 35, 51, 63, 66, 69], "boil": 3, "down": [3, 17, 23, 30, 35, 47, 62, 63, 64], "pass": [3, 4, 5, 18, 20, 21, 24, 28, 42, 50, 68], "softwar": [3, 26], "regardless": [3, 4, 8, 12, 18, 29, 46, 52, 60], "wa": [3, 7, 14, 25, 29, 30, 32, 35, 36, 37, 39, 45, 46, 47, 48, 49, 53, 55, 59, 63, 65], "stori": [3, 46, 47], "behind": 3, "To": [3, 5, 8, 18, 21, 22, 23, 24, 25, 27, 29, 30, 35, 37, 39, 42, 44, 45, 49, 50, 53, 55, 57, 59, 60, 62, 63, 65, 66, 68], "do": [3, 4, 8, 20, 23, 25, 29, 30, 31, 33, 35, 39, 41, 45, 53, 55, 57, 62, 63, 65, 66, 68, 69], "interfac": [3, 4, 5, 14, 45, 69], "commun": [3, 5, 7, 20, 25, 27, 64], "adopt": [3, 8, 26, 32, 42, 44, 47, 55], "python": [3, 5, 8, 13, 17, 20, 21, 25, 29, 30, 32, 37, 40, 41, 42, 45, 50, 53, 57, 66, 68, 69], "base": [3, 5, 7, 12, 13, 19, 21, 26, 29, 32, 35, 40, 41, 42, 47, 49, 50, 51, 52, 57, 63, 65, 68, 69], "carri": [3, 24, 35], "ll": [4, 39], "revisit": [4, 16, 25, 43, 61], "time": [4, 5, 6, 8, 14, 18, 20, 23, 24, 25, 26, 27, 30, 31, 33, 35, 36, 37, 39, 40, 42, 45, 46, 49, 50, 51, 52, 53, 57, 59, 62, 63, 65, 66, 68, 69], "capabl": [4, 7, 26, 39, 55], "scale": [4, 7, 29, 30, 35, 37, 39, 42, 57, 60, 62, 63, 66], "enabl": [4, 5, 28, 29, 45, 50, 68], "vari": [4, 12, 38, 50, 52, 55, 57, 64], "addit": [4, 6, 7, 12, 15, 18, 21, 23, 25, 27, 37, 42, 45, 47, 49, 50, 59, 62, 63, 66, 68, 69], "compon": [4, 5, 8, 13, 14, 18, 22, 26, 38, 45, 50, 58, 69], "index": [4, 5, 8, 12, 13, 14, 15, 18, 19, 20, 21, 22, 23, 24, 25, 27, 29, 30, 31, 32, 35, 37, 39, 40, 41, 42, 44, 45, 49, 50, 52, 53, 57, 59, 62, 64, 65, 68], "essenti": [4, 22, 29, 30, 37, 42, 50, 53, 55, 63], "scalabl": [4, 6], "maintain": [4, 5, 8, 27, 29, 45, 66, 69], "more": [4, 5, 8, 10, 11, 13, 14, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 31, 32, 35, 37, 38, 39, 42, 44, 45, 46, 47, 48, 49, 50, 52, 53, 55, 57, 59, 60, 62, 63, 64, 65, 66, 68, 69], "analysi": [4, 7, 8, 12, 13, 21, 29, 32, 42, 46, 47, 50, 51, 53, 55, 62, 69], "examin": [4, 7, 17, 49], "identifi": [4, 5, 16, 18, 24, 27, 29, 31, 34, 37, 39, 42, 44, 45, 47, 50, 62, 66, 69], "underli": [4, 40, 63], "verifi": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 38, 48, 50, 52, 53, 55, 57, 59, 60, 62, 66], "high": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 39, 42, 44, 49, 50, 52, 55, 57, 59, 60, 63, 65, 66, 69], "via": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 38, 48, 50, 51, 55, 57, 60, 66], "appsi": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 29, 30, 32, 35, 52, 55, 60, 66], "modul": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 52, 53, 55, 57, 58, 59, 60, 62, 63, 64, 65, 66, 68], "elsewher": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 29, 30, 32, 35, 38, 48, 52, 55, 57, 60, 66, 69], "assum": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 35, 37, 38, 39, 42, 44, 45, 48, 49, 50, 51, 52, 53, 55, 57, 58, 59, 60, 62, 63, 65, 66, 68, 69], "cbc": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 32, 35, 37, 41, 42, 52, 53, 55, 59, 60, 62, 63, 64, 65, 66, 68], "been": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 35, 36, 37, 38, 42, 45, 46, 47, 48, 49, 50, 52, 53, 55, 57, 60, 62, 64, 66, 68, 69], "previous": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 29, 30, 32, 35, 38, 47, 48, 50, 52, 53, 55, 57, 60, 62, 65, 66, 68, 69], "solverfactori": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 52, 53, 55, 57, 58, 59, 60, 62, 63, 64, 65, 66, 68, 69], "It": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 38, 39, 45, 48, 52, 55, 57, 60, 63, 65, 66], "sy": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 52, 53, 55, 57, 58, 59, 60, 62, 63, 64, 65, 66, 68], "pip": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 52, 53, 55, 57, 58, 59, 60, 62, 63, 64, 65, 66, 68], "dev": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 52, 53, 55, 57, 58, 59, 60, 62, 63, 64, 65, 66], "null": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 52, 53, 55, 57, 58, 59, 60, 62, 63, 64, 65, 66], "highspi": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 41, 52, 53, 55, 59, 60, 62, 63, 64, 65, 66], "environ": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 52, 53, 57, 58, 59, 60, 62, 63, 64, 65, 66, 68, 69], "appsi_high": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 39, 41, 53, 55, 59, 60, 62, 63, 64, 65, 66], "els": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 29, 33, 34, 38, 39, 42, 44, 45, 47, 48, 49, 50, 52, 53, 55, 60, 62, 63, 65, 66, 68, 69], "assert": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 38, 39, 48, 52, 55, 57, 60], "basic": [4, 16, 17, 21, 27, 42, 55, 65, 68, 69], "previou": [4, 9, 12, 15, 18, 28, 29, 35, 42, 45, 50, 51, 63], "were": [4, 18, 27, 30, 35, 42, 49, 62, 65], "distinct": [4, 22, 29], "evid": [4, 20], "compris": [4, 21, 23, 25, 31, 37, 40, 59, 68, 69], "abbrevi": [4, 29, 68], "m": [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 52, 53, 55, 57, 58, 59, 60, 62, 63, 64, 65, 66, 68, 69], "factor": [4, 7, 20, 31, 42, 44, 48, 50, 55, 62, 65, 69], "attribut": [4, 5, 7, 14, 29, 35, 37, 45, 65, 68, 69], "built": [4, 20, 57], "librari": [4, 5, 8, 13, 21, 30, 32, 33, 40, 41, 44, 45, 47, 49, 50, 69], "handl": [4, 23, 26, 45, 68], "tabular": [4, 5], "sever": [4, 5, 6, 7, 11, 16, 19, 24, 26, 28, 29, 36, 37, 39, 42, 49, 57, 69], "option": [4, 5, 14, 21, 27, 29, 33, 41, 42, 50, 63, 65, 68, 69], "would": [4, 7, 8, 12, 15, 17, 18, 19, 21, 25, 26, 29, 31, 32, 33, 35, 39, 42, 45, 48, 49, 50, 52, 53, 55, 57, 59, 62, 63, 64, 65, 66, 68], "appropri": [4, 5, 42, 50], "task": [4, 16, 20, 24, 29, 30, 32, 37, 42, 53], "nest": [4, 37, 63, 68, 69], "dictionari": [4, 18, 21, 29, 34, 35, 37, 42, 45, 50, 53, 57, 63, 69], "column": [4, 5, 7, 12, 14, 15, 18, 20, 22, 24, 25, 29, 30, 31, 32, 35, 39, 42, 45, 49, 50, 52, 53, 62, 64, 66, 69], "show": [4, 5, 7, 9, 12, 13, 14, 18, 20, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 33, 35, 37, 38, 39, 40, 42, 44, 45, 47, 48, 49, 51, 52, 53, 55, 57, 59, 60, 62, 63, 66, 68, 69], "repres": [4, 6, 8, 18, 19, 22, 23, 25, 31, 33, 34, 35, 37, 39, 42, 44, 45, 46, 47, 49, 50, 57, 59, 68, 69], "kei": [4, 5, 8, 14, 20, 21, 29, 32, 33, 34, 35, 37, 40, 46, 49, 52, 53, 57, 59, 60, 63, 65, 66, 68, 69], "outermost": 4, "inner": [4, 42, 50], "float": [4, 30, 42, 50, 52, 60, 69], "none": [4, 5, 7, 20, 21, 27, 29, 30, 32, 34, 35, 40, 42, 45, 47, 50, 52, 57, 66, 68], "print": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 22, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 41, 42, 44, 45, 46, 47, 48, 49, 50, 52, 53, 55, 57, 59, 60, 62, 63, 64, 65, 66, 68], "item": [4, 21, 30, 32, 34, 35, 37, 50, 53, 57, 59, 60, 65, 66, 69], "consum": [4, 21, 35, 57, 65, 69], "8s": [4, 29, 49], "row": [4, 5, 7, 8, 22, 29, 31, 35, 42, 53, 63, 66, 69], "label": [4, 7, 13, 15, 24, 27, 29, 31, 32, 33, 34, 35, 37, 39, 40, 41, 42, 44, 45, 50, 52, 53, 57, 60, 66, 69], "4s": 4, "By": [4, 5, 7, 14, 30, 31, 32, 55, 69], "rearrang": [4, 42], "straightforward": [4, 18, 55, 69], "becom": [4, 8, 9, 14, 19, 20, 22, 25, 29, 38, 39, 41, 42, 45, 47, 49, 50, 57, 58, 59, 60, 65, 66], "collect": [4, 5, 6, 7, 11, 13, 15, 20, 21, 27, 31, 32, 33, 35, 40, 42, 50, 62, 63, 69], "compar": [4, 7, 8, 13, 14, 19, 20, 22, 29, 30, 37, 39, 40, 42, 45, 47, 50, 59, 60, 62, 64, 66], "adapt": [4, 15, 49, 57, 62, 63, 65], "let": [4, 7, 8, 9, 11, 12, 15, 19, 20, 21, 25, 27, 29, 30, 33, 34, 35, 37, 38, 39, 44, 45, 47, 50, 52, 55, 57, 58, 59, 62, 63, 65, 68], "cal": [4, 50], "p": [4, 11, 12, 18, 23, 24, 26, 33, 35, 37, 39, 41, 42, 44, 46, 47, 49, 50, 52, 53, 57, 58, 60, 62, 65, 66, 68], "respect": [4, 5, 8, 14, 18, 21, 26, 27, 29, 30, 32, 34, 35, 37, 39, 42, 46, 55, 57, 62, 63, 64, 66, 68], "x_r": [4, 39], "y_p": 4, "denot": [4, 8, 12, 15, 18, 19, 21, 23, 26, 27, 29, 30, 31, 35, 37, 39, 40, 49, 53, 57, 62, 65, 69], "constrain": [4, 12, 15, 16, 29, 35, 39, 45, 46, 49], "zero": [4, 13, 19, 29, 35, 37, 40, 41, 42, 44, 45, 50, 52, 60, 63, 65, 66], "b_r": 4, "b_p": 4, "b_q": 4, "don": [4, 8, 29, 57, 66, 69], "insert": [4, 5, 32, 45, 47], "larger": [4, 8, 20, 27, 30, 33, 37, 42, 47, 50, 53, 59], "than": [4, 5, 8, 10, 12, 15, 16, 17, 20, 21, 23, 24, 25, 27, 29, 30, 32, 33, 35, 37, 39, 42, 46, 47, 49, 50, 52, 53, 55, 57, 59, 60, 62, 63, 65], "ever": [4, 42, 50], "design": [4, 5, 14, 19, 22, 27, 32, 39, 40, 42, 43, 66, 68], "caus": [4, 5, 39, 68], "ignor": [4, 35, 42, 50, 57], "befor": [4, 13, 20, 21, 22, 30, 33, 39, 40, 42, 46, 53, 57, 59, 60, 62, 63, 65, 66], "sum_": [4, 7, 12, 13, 14, 15, 18, 20, 23, 25, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 42, 44, 45, 46, 47, 48, 49, 50, 53, 57, 58, 59, 62, 63, 65, 66, 69], "c_p": 4, "c_r": 4, "a_": [4, 15, 25, 29, 31, 37], "put": [4, 42, 59, 63, 65], "piec": [4, 21, 37], "flexibl": [4, 32, 35, 36, 63, 68], "particular": [4, 14, 16, 21, 25, 30, 33, 35, 37, 39, 40, 45, 55, 62, 63, 65, 66, 69], "hold": [4, 5, 8, 9, 12, 14, 16, 18, 24, 27, 29, 35, 45, 52, 57, 59, 60, 62, 63, 65, 66], "scienc": [4, 5, 25, 32, 44, 45, 46, 62, 64], "see": [4, 5, 20, 21, 25, 26, 30, 31, 40, 44, 45, 46, 48, 50, 55, 57, 60, 63, 64, 65, 66, 68], "facilit": [4, 8, 14, 69], "construct": [4, 5, 7, 9, 14, 18, 21, 29, 32, 37, 42, 44, 45, 50, 53, 65, 66, 68, 69], "pyo": [4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44, 45, 47, 48, 49, 50, 52, 53, 55, 57, 58, 59, 60, 62, 63, 64, 65, 66, 68], "initi": [4, 7, 8, 12, 13, 14, 15, 18, 19, 20, 21, 22, 23, 24, 25, 27, 29, 30, 31, 32, 33, 35, 37, 38, 39, 40, 41, 42, 46, 48, 50, 52, 53, 55, 57, 58, 59, 60, 62, 63, 64, 65, 66, 68, 69], "assign": [4, 5, 15, 16, 19, 22, 24, 25, 29, 30, 31, 32, 33, 37, 41, 42, 50, 51, 66], "decor": [4, 5, 8, 9, 25, 32, 62], "declar": [4, 5, 35, 65, 66, 69], "return": [4, 5, 7, 8, 9, 10, 11, 12, 13, 15, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 52, 53, 55, 57, 58, 59, 60, 62, 63, 65, 66, 68, 69], "form": [4, 6, 22, 23, 36, 44, 45, 51, 58, 60, 63, 65, 68], "param": [4, 7, 12, 14, 15, 18, 21, 22, 23, 24, 25, 27, 29, 30, 32, 39, 42, 52, 57, 60, 65, 68, 69], "omit": [4, 69], "emb": 4, "extern": [4, 21, 44, 55], "directli": [4, 5, 7, 14, 22, 23, 32, 35, 44, 45, 50, 59], "effect": [4, 5, 21, 23, 34, 44, 46, 47, 53, 68], "doe": [4, 5, 15, 21, 23, 25, 27, 29, 30, 35, 37, 40, 42, 45, 50, 55, 62, 66], "keep": [4, 12, 18, 25, 33, 35, 39, 50, 53, 63, 65, 69], "shorter": 4, "remov": [4, 6, 8, 15, 29, 37, 42, 53], "overhead": 4, "blur": 4, "boundari": [4, 27, 69], "statement": [4, 30, 32, 62], "edit": [4, 37, 46], "locat": [4, 16, 24, 29, 32, 34, 39, 41, 42, 57, 59, 69], "portion": [4, 49], "therebi": 4, "significantli": [4, 8, 27, 30, 39, 42, 50], "improv": [4, 5, 25, 39, 42, 44, 45, 47, 64], "maintainabi": 4, "concern": [4, 30], "overkil": 4, "small": [4, 5, 8, 12, 25, 26, 29, 30, 31, 33, 34, 37, 39, 41, 63, 65, 69], "consider": [4, 14, 15, 21, 25, 32, 42, 59, 69], "note": [4, 5, 9, 11, 12, 13, 14, 15, 17, 18, 20, 24, 25, 32, 33, 34, 35, 37, 40, 41, 42, 48, 55, 57, 60, 62, 66, 68, 69], "becaus": [4, 8, 11, 18, 21, 25, 27, 29, 30, 32, 35, 37, 39, 42, 45, 50, 53, 55, 57, 63, 65, 68, 69], "take": [4, 5, 7, 8, 9, 11, 16, 17, 18, 21, 22, 23, 24, 27, 29, 31, 33, 35, 38, 39, 42, 45, 47, 48, 50, 51, 53, 55, 57, 58, 59, 60, 62, 63, 65, 66, 68], "logic": [4, 5, 8, 12, 17, 18, 19, 22, 26, 29, 52, 55, 69], "def": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 52, 53, 55, 57, 58, 59, 60, 62, 63, 64, 65, 66, 68, 69], "coeffici": [4, 6, 7, 8, 13, 15, 17, 18, 29, 40, 42, 44, 50, 55, 62, 63], "cp": [4, 41], "cr": 4, "argumentn": 4, "var": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 48, 52, 53, 55, 57, 58, 59, 60, 62, 63, 64, 65, 66, 68, 69], "fix": [4, 5, 6, 7, 12, 14, 24, 25, 27, 30, 32, 35, 40, 44, 45, 46, 47, 49, 57, 60, 62, 63, 64, 65, 66, 68], "done": [4, 5, 21, 22, 39, 42, 59, 63], "parlanc": 4, "sometim": [4, 36, 46, 58], "call": [4, 8, 24, 27, 29, 31, 32, 35, 38, 39, 40, 42, 48, 49, 50, 53, 59, 62, 66, 68], "rule": [4, 12, 29, 39, 45, 60, 61, 63, 65, 69], "lambda": [4, 18, 19, 20, 21, 22, 25, 27, 29, 31, 35, 39, 42, 44, 46, 47, 49, 50, 52, 53, 55, 57, 60, 65, 66, 68, 69], "argument": [4, 5, 18, 33, 39, 42, 50, 55, 62, 63, 65, 68, 69], "member": [4, 30, 32], "tupl": [4, 5, 8, 21, 25, 29, 37, 68], "quicksum": [4, 12, 18, 30, 33, 52, 55], "accept": [4, 5, 21, 22, 29, 32, 42, 46, 49, 57, 63, 69], "success": [4, 7, 47, 68, 69], "sum": [4, 7, 8, 9, 13, 14, 15, 19, 20, 21, 22, 23, 25, 27, 29, 30, 31, 32, 34, 35, 37, 38, 39, 40, 41, 42, 44, 45, 46, 48, 49, 50, 52, 53, 55, 57, 58, 59, 60, 62, 63, 64, 65, 66, 68, 69], "sens": [4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 46, 47, 48, 49, 52, 53, 55, 57, 58, 59, 60, 62, 63, 64, 65, 66, 68, 69], "Then": [4, 22, 33, 34, 35, 42, 50, 53, 55, 62, 65, 66, 68], "associ": [4, 21, 24, 26, 32, 34, 35, 37, 40, 68, 69], "manner": [4, 32], "block": [4, 25, 37, 39, 44, 45, 46, 47, 49, 50, 55, 63, 68], "materials_us": [4, 68], "report": [4, 14, 18, 29, 30, 35, 37, 39, 42, 50, 52, 55, 57, 60, 62, 68, 69], "access": [4, 21, 32, 35, 45, 57, 68], "iter": [4, 5, 18, 29, 55, 63, 68, 69], "pprint": [4, 69], "nproduct": 4, "nresourc": 4, "3": [4, 7, 11, 12, 14, 15, 18, 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 33, 34, 35, 37, 38, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 52, 53, 55, 58, 59, 60, 63, 66, 69], "size": [4, 5, 7, 12, 13, 14, 22, 24, 25, 27, 29, 32, 37, 38, 39, 40, 42, 45, 47, 48, 49, 50, 52, 53, 55, 57, 59, 60, 62, 69], "dimen": [4, 21, 32, 33], 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"util": [8, 27, 29, 32, 46, 47, 49, 53, 68], "encompass": 8, "advantag": [8, 18, 28, 30, 31, 42, 50, 68], "concis": [8, 20], "space": [8, 16, 44, 45, 53, 62, 68], "clariti": 8, "reveal": [8, 39, 50], "across": [8, 29, 34, 35, 63, 65], "uniform": [8, 23, 32, 39, 45, 46, 60, 63, 65, 69], "furthermor": [8, 18, 23, 44, 69], "unnecessari": [8, 69], "theoret": [8, 52, 60], "streamlin": [8, 21, 69], "Is": [8, 15, 30, 46], "answer": [8, 42, 59], "textit": 8, "sign": [8, 42, 50], "onc": [8, 22, 23, 37, 42, 50, 62, 66], "commit": [8, 31, 65], "deriv": [8, 22, 27, 35, 41, 42, 45, 50, 62], "sort": [8, 14, 20, 21, 25, 29, 33, 42, 46, 50], "consid": [8, 11, 13, 14, 15, 18, 20, 21, 22, 24, 27, 28, 29, 30, 31, 32, 33, 37, 38, 39, 40, 42, 44, 46, 47, 48, 49, 53, 55, 57, 58, 60, 62, 63, 64, 65, 66, 68], "anywai": [8, 30, 63], "without": [8, 15, 23, 27, 32, 33, 37, 39, 42, 46, 47, 50, 53, 59, 63, 68, 69], "Of": [8, 16, 63], "cours": [8, 16, 17, 22, 33, 39, 63, 69], "illustr": [8, 12, 18, 20, 21, 26, 27, 29, 30, 37, 44, 45, 52, 55, 60, 65, 66], "shall": [8, 18, 45, 50, 65], "upon": [8, 21, 57], "had": [8, 17, 18, 29, 42, 50, 59, 65], "mention": [8, 11], "neg": [8, 9, 17, 18, 21, 29, 34, 35, 37, 42, 45, 47, 50, 51, 57, 59, 62, 69], "therefor": [8, 14, 27, 32, 35, 38, 42, 45, 50, 62, 63, 66], "overlin": 8, "side": [8, 9, 14, 25, 29, 30, 42, 55, 57, 68], "One": [8, 9, 15, 19, 21, 22, 27, 29, 30, 35, 37, 38, 63, 65, 68], "inde": [8, 20, 25, 44, 50, 57, 63, 65], "yield": [8, 10, 17, 18, 27, 29, 39, 41, 45, 50, 52, 53, 55, 57, 63, 64, 68], "n_var": 8, "n_constraint": 8, "constraintlist": [8, 11, 60], "optimal_x": 8, "round": [8, 12, 14, 17, 18, 24, 25, 30, 31, 32, 33, 34, 37, 38, 39, 41, 44, 45, 48, 52, 55, 62, 64, 65], "addition": [8, 58, 63], "avoid": [8, 12, 18, 19, 21, 22, 27, 32, 33, 42, 52, 66, 68, 69], "hard": [8, 27, 29, 42, 50, 63], "doc": [8, 37, 45, 48, 69], "lambda_1": 9, "lambda_2": 9, "lambda_3": 9, "lambda_4": 9, "four": [9, 24, 39, 41, 42, 50, 63], "rh": [9, 50], "tight": [9, 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