deploy: b39db29

mobook · Oct 23, 2023 · 1c27fe7 · 1c27fe7
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diff --git a/_sources/notebooks/05/svm.ipynb b/_sources/notebooks/05/svm.ipynb
diff --git a/_sources/notebooks/06/building-insulation.ipynb b/_sources/notebooks/06/building-insulation.ipynb
diff --git a/_sources/notebooks/06/economic-order-quantity.ipynb b/_sources/notebooks/06/economic-order-quantity.ipynb
diff --git a/_sources/notebooks/06/kelly-criterion.ipynb b/_sources/notebooks/06/kelly-criterion.ipynb
diff --git a/_sources/notebooks/06/markowitz_portfolio_revisited.ipynb b/_sources/notebooks/06/markowitz_portfolio_revisited.ipynb
@@ -26,41 +26,39 @@
     "\n",
     "This cell selects and verifies a global SOLVER for the notebook.\n",
     "\n",
-    "If run on Google Colab, the cell installs Pyomo and ipopt, then sets SOLVER to \n",
-    "use the ipopt solver. If run elsewhere, it assumes Pyomo and the Mosek solver\n",
-    "have been previously installed and sets SOLVER to use the Mosek solver via the Pyomo \n",
-    "SolverFactory. It then verifies that SOLVER is available."
+    "If run on Google Colab, the cell installs Pyomo and ipopt, then sets SOLVER to use the ipopt solver. If run elsewhere, it assumes Pyomo and the Mosek solver\n",
+    "have been previously installed and sets SOLVER to use the Mosek solver via the Pyomo SolverFactory. It then verifies that SOLVER is available."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 9,
    "id": "5da22c67-5c34-4c3a-90a4-61222899e855",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
     "import sys\n",
-    "import 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",
-    "    SOLVER_CONIC = \"ipopt\"\n",
-    "    \n",
+    "    solver = \"ipopt\"\n",
     "else:\n",
-    "    SOLVER_CONIC = \"mosek_direct\"\n",
+    "    solver = \"mosek_direct\"\n",
     "\n",
     "import pyomo.environ as pyo\n",
-    "if not pyo.SolverFactory(SOLVER_CONIC).available():\n",
-    "    print(f\"Solver {SOLVER_CONIC} is not available\")"
+    "if not pyo.SolverFactory(solver).available():\n",
+    "    print(f\"Solver {solver} is not available\")\n",
+    "else:\n",
+    "    SOLVER = pyo.SolverFactory(solver)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 10,
    "id": "b13edf26",
    "metadata": {
     "tags": []
@@ -114,7 +112,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 11,
    "id": "0e194e8d",
    "metadata": {
     "tags": []
@@ -158,7 +156,7 @@
     }
    ],
    "source": [
-    "# Specify the initial capital, the risk tolerance, and the guaranteed return rate. \n",
+    "# Specify the initial capital, the risk tolerance, and the guaranteed return rate.\n",
     "C = 1\n",
     "alpha = 0.1\n",
     "R = 1.05\n",
@@ -172,7 +170,7 @@
     "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",
@@ -184,8 +182,8 @@
     "#        y=Ax, |y|^2 <= s,\n",
     "# corresponding to the mathematical formulation above.\n",
     "\n",
+    "\n",
     "def markowitz_revisited(alpha, mu, Sigma):\n",
-    "    \n",
     "    model = pyo.ConcreteModel(\"Markowitz portfolio optimization revisited\")\n",
     "\n",
     "    model.xtilde = pyo.Var(domain=pyo.NonNegativeReals)\n",
@@ -194,7 +192,7 @@
     "\n",
     "    @model.Objective(sense=pyo.maximize)\n",
     "    def objective(m):\n",
-    "        return mu @ m.x + R * m.xtilde - alpha*m.s\n",
+    "        return mu @ m.x + R * m.xtilde - alpha * m.s\n",
     "\n",
     "    @model.Constraint()\n",
     "    def bounded_variance(m):\n",
@@ -204,20 +202,29 @@
     "    def total_assets(m):\n",
     "        return sum(m.x[i] for i in range(n)) + m.xtilde == C\n",
     "\n",
-    "    result = pyo.SolverFactory(SOLVER_CONIC).solve(model)\n",
-    "    \n",
+    "    result = SOLVER.solve(model)\n",
+    "\n",
     "    return result, model\n",
     "\n",
+    "\n",
     "result, model = markowitz_revisited(alpha, 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:** ${model.objective():.2f}$\"))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 12,
    "id": "9a2b00d7-433a-46c8-a4b6-c451244c9b0f",
    "metadata": {
     "tags": []
@@ -235,17 +242,43 @@
     }
    ],
    "source": [
-    "alpha_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]\n",
+    "alpha_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",
+    "]\n",
     "objective = []\n",
     "\n",
-    "plt.rcParams.update({'font.size': 14})\n",
+    "plt.rcParams.update({\"font.size\": 14})\n",
     "for alpha in alpha_values:\n",
     "    _, model = markowitz_revisited(alpha, mu, Sigma)\n",
-    "    objective.append(round(model.objective(),3))\n",
+    "    objective.append(round(model.objective(), 3))\n",
     "\n",
     "plt.plot(alpha_values, objective, color=plt.cm.tab20c(0))\n",
-    "plt.xlabel(r'Risk tolerance $\\alpha$')\n",
-    "plt.ylabel('Optimal objective value')\n",
+    "plt.xlabel(r\"Risk tolerance $\\alpha$\")\n",
+    "plt.ylabel(\"Optimal objective value\")\n",
     "plt.tight_layout()\n",
     "plt.show()"
    ]
@@ -267,7 +300,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.2"
+   "version": "3.10.10"
   },
   "varInspector": {
    "cols": {

diff --git a/_sources/notebooks/06/optimal-growth-portfolios.ipynb b/_sources/notebooks/06/optimal-growth-portfolios.ipynb
diff --git a/_sources/notebooks/06/svm-conic.ipynb b/_sources/notebooks/06/svm-conic.ipynb