Update notebook outputs

Author: fdabrandao

notebooks/02/mad-portfolio-optimization.ipynb

@@ -6,7 +6,7 @@
         "id": "kq1oXGHPbfFP"
       },
       "source": [
-        "```{index} single: solver; cbc\n",
+        "```{index} single: solver; highs\n",
         "```\n",
         "```{index} pandas dataframe\n",
         "```\n",

notebooks/06/optimal-growth-portfolios.ipynb

@@ -4,14 +4,15 @@
       "cell_type": "markdown",
       "id": "6b0b3c2f-3aaa-43fe-a8d0-2b696df5c1a6",
       "metadata": {
-        "tags": []
+        "tags": [],
+        "id": "6b0b3c2f-3aaa-43fe-a8d0-2b696df5c1a6"
       },
       "source": [
         "```{index} single: application; portfolio\n",
         "```\n",
         "```{index} single: application; investment\n",
         "```\n",
-        "```{index} single: solver; highs\n",
+        "```{index} single: solver; scip\n",
         "```\n",
         "```{index} chance constraints\n",
         "```\n",
@@ -23,9 +24,24 @@
       "execution_count": 1,
       "id": "5da22c67-5c34-4c3a-90a4-61222899e855",
       "metadata": {
-        "tags": []
+        "tags": [],
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "5da22c67-5c34-4c3a-90a4-61222899e855",
+        "outputId": "22e416af-9eb0-4091-c467-7707e4465c8b"
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/5.7 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[91m━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.6/5.7 MB\u001b[0m \u001b[31m17.4 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.4/5.7 MB\u001b[0m \u001b[31m49.4 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m5.7/5.7 MB\u001b[0m \u001b[31m64.2 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.7/5.7 MB\u001b[0m \u001b[31m47.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hUsing default Community Edition License for Colab. Get yours at: https://ampl.com/ce\n",
+            "Licensed to AMPL Community Edition License for the AMPL Model Colaboratory (https://ampl.com/colab).\n"
+          ]
+        }
+      ],
       "source": [
         "# Install AMPL and solvers\n",
         "%pip install -q amplpy pandas numpy scipy\n",
@@ -45,7 +61,8 @@
       "execution_count": 2,
       "id": "b13edf26",
       "metadata": {
-        "tags": []
+        "tags": [],
+        "id": "b13edf26"
       },
       "outputs": [],
       "source": [
@@ -59,18 +76,19 @@
       "id": "a82bd88d-7b16-4c82-b0b7-5dbd0d81b71c",
       "metadata": {
         "jp-MarkdownHeadingCollapsed": true,
-        "tags": []
+        "tags": [],
+        "id": "a82bd88d-7b16-4c82-b0b7-5dbd0d81b71c"
       },
       "source": [
         "We consider here another variant of the Markowitz portfolio optimization problem, which we already encountered in the context of convex optimization [here](../05/markowitz_portfolio.ipynb) and in the context of conic optimization [here](../06/markowitz_portfolio_revisited.ipynb).\n",
         "\n",
         "Assuming there is an initial unit capital $C$ that needs to be invested in a selection of $n$ possible assets, each of them with a unknown return rate $r_i$, $i=1,\\dots,n$. Let $x$ be the vector whose $i$-th component $x_i$ describes the fraction of the capital invested in asset $i$. The return rate vector $r$ can be modelled by a multivariate Gaussian distribution with mean $\\mu$ and covariance $\\Sigma$. Assume there is also a risk-free asset with guaranteed return rate $R$ and let $\\tilde{x}$ the amount invested in that asset. We want to determine the portfolio that maximizes the _expected_ return $\\mathbb{E} ( R \\tilde{x} + r^\\top x )$, which in view of our assumptions rewrites as $ \\mathbb{E} ( R \\tilde{x} + r^\\top x ) = R \\tilde{x} + \\mu^\\top x$.\n",
         "\n",
-        "Additionally, we includ a _loss risk chance constraint_ of the form \n",
+        "Additionally, we includ a _loss risk chance constraint_ of the form\n",
         "\n",
         "$$\n",
         "\\mathbb{P} ( r^\\top x \\leq \\alpha) \\leq \\beta.\n",
-        "$$ \n",
+        "$$\n",
         "\n",
         "Thanks to the assumption that $r$ is multivariate Gaussian, this chance constraint can be equivalently rewritten as\n",
         "\n",
@@ -97,7 +115,9 @@
       "cell_type": "code",
       "execution_count": 3,
       "id": "1e9ef269",
-      "metadata": {},
+      "metadata": {
+        "id": "1e9ef269"
+      },
       "outputs": [],
       "source": [
         "# we import the inverse CDF or quantile function for the standard normal norm.ppf() from scipy.stats\n",
@@ -130,13 +150,19 @@
       "cell_type": "code",
       "execution_count": 4,
       "id": "3936f9b5",
-      "metadata": {},
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3936f9b5",
+        "outputId": "d61cab63-9231-455b-9d02-5f66ea445ea0"
+      },
       "outputs": [
         {
-          "name": "stdout",
           "output_type": "stream",
+          "name": "stdout",
           "text": [
-            "Overwriting markowitz_chanceconstraints.mod\n"
+            "Writing markowitz_chanceconstraints.mod\n"
           ]
         }
       ],
@@ -159,7 +185,7 @@
         "\n",
         "maximize expected_return:\n",
         "    sum{i in N} mu[i] * x[i];\n",
-        "      \n",
+        "\n",
         "subject to bounded_variance:\n",
         "    phi_val * (sum{i in N, j in N} x[i] * Sigma[i, j] * x[j]) <= sum{i in N} mu[i] * x[i] - alpha;\n",
         "\n",
@@ -172,54 +198,53 @@
       "execution_count": 5,
       "id": "0e194e8d",
       "metadata": {
-        "tags": []
+        "tags": [],
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 135
+        },
+        "id": "0e194e8d",
+        "outputId": "02d0cc76-b727-484a-f973-c9fc2e406235"
       },
       "outputs": [
         {
-          "name": "stdout",
           "output_type": "stream",
+          "name": "stdout",
           "text": [
-            "HiGHS 1.5.1: HiGHS 1.5.1: optimal solution; objective 1.229721418\n",
-            "860 simplex iterations\n",
-            "5 branching nodes\n",
-            "absmipgap=8.14792e-06, relmipgap=6.62583e-06\n"
+            "SCIP 9.0.1: WARNING: No dual information available when presolving was performed.\n",
+            "\b\b\b\b\b\b\b\b\b\b\b\bSCIP 9.0.1: optimal solution; objective 1.232266138\n",
+            "685 simplex iterations\n"
           ]
         },
         {
+          "output_type": "display_data",
           "data": {
-            "text/markdown": [
-              "**Solver result:** *solved*"
-            ],
             "text/plain": [
               "<IPython.core.display.Markdown object>"
-            ]
+            ],
+            "text/markdown": "**Solver result:** *solved*"
           },
-          "metadata": {},
-          "output_type": "display_data"
+          "metadata": {}
         },
         {
+          "output_type": "display_data",
           "data": {
-            "text/markdown": [
-              "**Solution:** $\\tilde x = 0.000$, $x_1 = 0.797$,  $x_2 = 0.203$,  $x_3 = 0.000$"
-            ],
             "text/plain": [
               "<IPython.core.display.Markdown object>"
-            ]
+            ],
+            "text/markdown": "**Solution:** $\\tilde x = 0.000$, $x_1 = 0.666$,  $x_2 = 0.256$,  $x_3 = 0.078$"
           },
-          "metadata": {},
-          "output_type": "display_data"
+          "metadata": {}
         },
         {
+          "output_type": "display_data",
           "data": {
-            "text/markdown": [
-              "**Maximizes objective value to:** $1.23$"
-            ],
             "text/plain": [
               "<IPython.core.display.Markdown object>"
-            ]
+            ],
+            "text/markdown": "**Maximizes objective value to:** $1.23$"
           },
-          "metadata": {},
-          "output_type": "display_data"
+          "metadata": {}
         }
       ],
       "source": [
@@ -309,8 +334,11 @@
         "_Feature"
       ],
       "window_display": false
+    },
+    "colab": {
+      "provenance": []
     }
   },
   "nbformat": 4,
   "nbformat_minor": 5
-}
+}