Fix preambles, imports, and positioning of the solve() command

mobook · Nov 30, 2023 · c076df9 · c076df9
1 parent 9926d67
commit c076df9
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diff --git a/notebooks/01/01-production-planning.ipynb b/notebooks/01/01-production-planning.ipynb
@@ -14,14 +14,8 @@
     "```\n",
     "```{index} single: Pyomo; decorators\n",
     "```\n",
-    "```{index} single: solver; cbc\n",
-    "```\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
+    "```{index} single: solver; HiGHS\n",
+    "```\n",
     "# 1.1 A first production planning problem"
    ]
   },
@@ -172,7 +166,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.2"
+   "version": "3.10.10"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,

diff --git a/notebooks/01/02-production-planning-basic.ipynb b/notebooks/01/02-production-planning-basic.ipynb
diff --git a/notebooks/01/03-production-planning-advanced.ipynb b/notebooks/01/03-production-planning-advanced.ipynb
@@ -15,7 +15,9 @@
     "```\n",
     "```{index} single: Pyomo; decorators\n",
     "```\n",
-    "```{index} single: solver; cbc\n",
+    "```{index} single: Pyomo; parameters\n",
+    "```\n",
+    "```{index} single: solver; HiGHS\n",
     "```\n",
     "\n",
     "# 1.3 A data-driven Pyomo Model\n",
@@ -63,7 +65,7 @@
     " \n",
     "import pyomo.environ as pyo\n",
     "SOLVER = pyo.SolverFactory(solver)\n",
-    " \n",
+    "\n",
     "assert SOLVER.available(), f\"Solver {solver} is not available.\""
    ]
   },
@@ -543,8 +545,6 @@
       "        labor B :  -Inf : 2*y[U] + y[V] - x[labor B] :   0.0 :   True\n",
       "\n",
       "12 Declarations: PRODUCTS RESOURCES demand available cp cr a_index a x y profit materials_used\n",
-      "Running HiGHS 1.5.3 [date: 2023-05-16, git hash: 594fa5a9d]\n",
-      "Copyright (c) 2023 HiGHS under MIT licence terms\n",
       "Profit = 2600.0\n",
       "\n",
       "Production Report\n",
@@ -594,8 +594,6 @@
      "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",
       "Profit = 2600.0\n",
       "\n",
       "Production Report\n",

diff --git a/notebooks/02/01-bim.ipynb b/notebooks/02/01-bim.ipynb
@@ -9,14 +9,20 @@
    "source": [
     "```{index} dual problem\n",
     "```\n",
-    "```{index} single: solver; cbc\n",
-    "```\n",
-    "```{index} single: solver; highs\n",
+    "```{index} single: solver; HiGHS\n",
     "```\n",
     "```{index} single: application; production planning\n",
     "```\n",
+    "```{index} single: Pyomo; sets \n",
+    "```\n",
+    "```{index} single: Pyomo; decorators \n",
+    "```\n",
+    "```{index} single: numpy\n",
+    "```\n",
+    "```{index} single: Pyomo; ConstraintList \n",
+    "```\n",
     "\n",
-    "# 2.1 BIM production"
+    "# 2.1 BIM production planning using linear optimization"
    ]
   },
   {
@@ -251,9 +257,7 @@
     }
    ],
    "source": [
-    "import pyomo.environ as pyo\n",
-    "\n",
-    "model = pyo.ConcreteModel(\"BIM\")\n",
+    "model = pyo.ConcreteModel(\"BIM production planning\")\n",
     "\n",
     "# Decision variables and their domains\n",
     "model.x1 = pyo.Var(domain=pyo.NonNegativeReals)\n",
@@ -297,7 +301,7 @@
     }
    ],
    "source": [
-    "model = pyo.ConcreteModel(\"BIM with decorators\")\n",
+    "model = pyo.ConcreteModel(\"BIM BIM production planning with decorators\")\n",
     "\n",
     "# Decision variables and their domains\n",
     "model.x1 = pyo.Var(domain=pyo.NonNegativeReals)\n",
@@ -332,7 +336,7 @@
     "\n",
     "\n",
     "# Solve and print solution\n",
-    "SOLVER.solve(model, tee=False)\n",
+    "SOLVER.solve(model)\n",
     "print(f\"x = ({model.x1.value:.1f}, {model.x2.value:.1f})\")\n",
     "print(f\"optimal value = {pyo.value(model.profit):.1f}\")"
    ]
@@ -464,7 +468,7 @@
    "source": [
     "import numpy as np\n",
     "\n",
-    "model = pyo.ConcreteModel(\"BIM in matrix form\")\n",
+    "model = pyo.ConcreteModel(\"BIM production planning in matrix form\")\n",
     "\n",
     "# Define the number of variables and constraints\n",
     "n_vars = 2\n",
@@ -519,7 +523,9 @@
     }
    ],
    "source": [
-    "model = pyo.ConcreteModel(\"BIM in matrix form using decorators and index sets\")\n",
+    "model = pyo.ConcreteModel(\n",
+    "    \"BIM production planning in matrix form using decorators and index sets\"\n",
+    ")\n",
     "\n",
     "# Define the number of variables and constraints and two corresponding index sets\n",
     "n_vars = 2\n",
@@ -572,7 +578,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.2"
+   "version": "3.10.10"
   }
  },
  "nbformat": 4,

diff --git a/notebooks/02/02-lad-regression.ipynb b/notebooks/02/02-lad-regression.ipynb
@@ -7,6 +7,16 @@
     "tags": []
    },
    "source": [
+    "```{index} single: application; regression\n",
+    "```\n",
+    "```{index} single: solver; HiGHS\n",
+    "```\n",
+    "```{index} single: Pyomo; RangeSet\n",
+    "```\n",
+    "```{index} single: Pyomo; decorators\n",
+    "```\n",
+    "```{index} single: sklearn\n",
+    "```\n",
     "```{index} single: application; regression\n",
     "```\n",
     "\n",
@@ -76,8 +86,9 @@
    },
    "outputs": [],
    "source": [
-    "from sklearn.datasets import make_regression\n",
+    "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
+    "from sklearn.datasets import make_regression\n",
     "\n",
     "n_features = 1\n",
     "n_samples = 1000\n",
@@ -121,10 +132,6 @@
     }
    ],
    "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "plt.rcParams[\"font.size\"] = 14\n",
-    "\n",
     "if n_features == 1:\n",
     "    fig, ax = plt.subplots()\n",
     "    ax.scatter(X, y, alpha=0.7, color=plt.get_cmap(\"coolwarm\")(0))\n",
@@ -208,9 +215,6 @@
     }
    ],
    "source": [
-    "import pyomo.environ as pyo\n",
-    "\n",
-    "\n",
     "def lad_regression(X, y):\n",
     "    model = pyo.ConcreteModel(\"LAD Regression\")\n",
     "\n",
@@ -296,8 +300,6 @@
    "source": [
     "y_fit = np.array([sum(x[j] * model.m[j]() for j in model.J) + model.b() for x in X])\n",
     "\n",
-    "plt.rcParams[\"font.size\"] = 14\n",
-    "\n",
     "if n_features == 1:\n",
     "    fig, ax = plt.subplots()\n",
     "    ax.scatter(X, y, alpha=0.7, color=plt.get_cmap(\"coolwarm\")(0), label=\"data\")\n",

diff --git a/notebooks/02/03-mad-portfolio-optimization.ipynb b/notebooks/02/03-mad-portfolio-optimization.ipynb
@@ -11,14 +11,14 @@
     "```\n",
     "```{index} single: application; investment\n",
     "```\n",
-    "```{index} single: solver; cbc\n",
-    "```\n",
     "```{index} single: solver; highs\n",
     "```\n",
-    "```{index} pandas dataframe\n",
+    "```{index} single: pandas\n",
     "```\n",
     "```{index} web scraping\n",
     "```\n",
+    "```{index} yfinance\n",
+    "```\n",
     "\n",
     "# 2.3 Mean Absolute Deviation (MAD) portfolio optimization\n",
     "\n",

diff --git a/notebooks/02/04-bim-dual.ipynb b/notebooks/02/04-bim-dual.ipynb
@@ -9,10 +9,12 @@
    "source": [
     "```{index} dual problem\n",
     "```\n",
-    "```{index} single: solver; cbc\n",
+    "```{index} single: solver; HiGHS\n",
     "```\n",
     "```{index} single: application; production planning\n",
     "```\n",
+    "```{index} single: Pyomo; Suffix\n",
+    "```\n",
     "\n",
     "# 2.4 Dual of the BIM production problem"
    ]
@@ -50,7 +52,7 @@
     " \n",
     "import pyomo.environ as pyo\n",
     "SOLVER = pyo.SolverFactory(solver)\n",
-    " \n",
+    "\n",
     "assert SOLVER.available(), f\"Solver {solver} is not available.\""
    ]
   },
@@ -139,9 +141,7 @@
     }
    ],
    "source": [
-    "import pyomo.environ as pyo\n",
-    "\n",
-    "model = pyo.ConcreteModel(\"BIM dual\")\n",
+    "model = pyo.ConcreteModel(\"BIM production planning dual problem\")\n",
     "\n",
     "# Decision variables and their domain\n",
     "model.y1 = pyo.Var(domain=pyo.NonNegativeReals)\n",
@@ -209,7 +209,7 @@
     }
    ],
    "source": [
-    "model = pyo.ConcreteModel(\"BIM with decorators\")\n",
+    "model = pyo.ConcreteModel(\"BIM production planning with decorators\")\n",
     "\n",
     "# Decision variables and their domains\n",
     "model.x1 = pyo.Var(domain=pyo.NonNegativeReals)\n",

diff --git a/notebooks/02/05-bim-maxmin.ipynb b/notebooks/02/05-bim-maxmin.ipynb
@@ -4,9 +4,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{index} single: solver; cbc\n",
+    "```{index} single: solver; HiGHS\n",
     "```\n",
-    "```{index} single: solver; highs\n",
+    "```{index} single: Pyomo; ConstraintList\n",
+    "```\n",
+    "```{index} minmax objective\n",
     "```\n",
     "\n",
     "# 2.5 BIM production for worst case"
@@ -43,7 +45,7 @@
     " \n",
     "import pyomo.environ as pyo\n",
     "SOLVER = pyo.SolverFactory(solver)\n",
-    " \n",
+    "\n",
     "assert SOLVER.available(), f\"Solver {solver} is not available.\""
    ]
   },
@@ -53,7 +55,7 @@
    "source": [
     "## Minmax objective function\n",
     "\n",
-    "Another class of seemingly complicated objective functions that can be easily rewritten as an LP are those stated as maxima over several linear functions. Given a finite set of indices $K$ and a collection of vectors $\\{c_k\\}_{k \\in K}$, the minimax problem given by\n",
+    "Another class of seemingly complicated objective functions that can be easily rewritten as an LO problem are those stated as maxima over several linear functions. Given a finite set of indices $K$ and a collection of vectors $\\{c_k\\}_{k \\in K}$, the minimax problem given by\n",
     "\n",
     "$$\n",
     "\\begin{align}\n",
@@ -99,11 +101,8 @@
     }
    ],
    "source": [
-    "import pyomo.environ as pyo\n",
-    "\n",
-    "\n",
     "def BIM_maxmin(costs):\n",
-    "    m = pyo.ConcreteModel(\"BIM with maxmin objective\")\n",
+    "    m = pyo.ConcreteModel(\"BIM production planning with maxmin objective\")\n",
     "\n",
     "    m.x1 = pyo.Var(domain=pyo.NonNegativeReals)\n",
     "    m.x2 = pyo.Var(domain=pyo.NonNegativeReals)\n",
@@ -124,7 +123,6 @@
     "\n",
     "\n",
     "m = BIM_maxmin([[12, 9], [11, 10], [8, 11]])\n",
-    "\n",
     "SOLVER.solve(m)\n",
     "\n",
     "print(f\"x = ({pyo.value(m.x1):.1f}, {pyo.value(m.x2):.1f})\")\n",

diff --git a/notebooks/02/06-bim-fractional.ipynb b/notebooks/02/06-bim-fractional.ipynb
@@ -4,9 +4,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```{index} single: solver; cbc\n",
+    "```{index} single: solver; HiGHS\n",
     "```\n",
-    "```{index} single: solver; highs\n",
+    "```{index} single: Pyomo; ConstraintList\n",
+    "```\n",
+    "```{index} single: Pyomo; Expression\n",
     "```\n",
     "\n",
     "# 2.6 BIM production variants"
@@ -43,7 +45,7 @@
     " \n",
     "import pyomo.environ as pyo\n",
     "SOLVER = pyo.SolverFactory(solver)\n",
-    " \n",
+    "\n",
     "assert SOLVER.available(), f\"Solver {solver} is not available.\""
    ]
   },