updated notebooks (init versions)

Author: AdrienTaylor

Part 1 - PEPs and fundamental proof structures/PEPit_demo_extracting_a_proof_session2.ipynb

@@ -163,7 +163,7 @@
     "    x1 = x0 - gamma * f.gradient(x0)\n",
     "\n",
     "    # Set the performance metric to the function values accuracy\n",
-    "    problem.set_performance_metric( (x1 - xs) ** 2 ) #TODO OPERAND\n",
+    "    problem.set_performance_metric( (x1 - xs) ** 2 )\n",
     "\n",
     "    # Solve the PEP\n",
     "    pepit_verbose = max(verbose, 0)\n",
@@ -442,7 +442,7 @@
     "    x1 = x0 - gamma * f.gradient(x0)\n",
     "\n",
     "    # Set the performance metric to the function values accuracy\n",
-    "    problem.set_performance_metric( f(x1) - fs ) #TODO OPERAND\n",
+    "    problem.set_performance_metric( f(x1) - fs )\n",
     "\n",
     "    # Solve the PEP\n",
     "    pepit_verbose = max(verbose, 0)\n",
@@ -1081,9 +1081,14 @@
     "    # Output prepared problem\n",
     "    pepit_verbose = max(verbose, 0)\n",
     "    problem._prepare_constraints(verbose=pepit_verbose)\n",
+    "    \n",
+    "    # Complete below: what constraints should be deactivated?\n",
+    "    ################## TODO BLOCK\n",
     "    problem._list_of_prepared_constraints[4].deactivate()\n",
     "    problem._list_of_prepared_constraints[6].deactivate()\n",
     "    problem._list_of_prepared_constraints[7].deactivate()\n",
+    "    ################## END TODO BLOCK\n",
+    "\n",
     "\n",
     "    pepit_tau = problem.solve(verbose=pepit_verbose)\n",
     "\n",
@@ -1353,7 +1358,9 @@
     "\n",
     "# This is the LMI:\n",
     "LMI = sm.simplify(sm.hessian( -Lagrangian , (x0,g0))/2)\n",
-    "LMI"
+    "\n",
+    "\n",
+    "LMI # Should this matrix be PSD or NSD?"
    ]
   },
   {

Part 1 - PEPs and fundamental proof structures/PEPit_demo_extracting_a_proof_session2_init_student_version.ipynb

@@ -8,7 +8,7 @@
     "tags": []
    },
    "source": [
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/bgoujaud/PEPit/blob/master/ressources/demo/PEPit_demo_extracting_a_proof.ipynb)"
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/bgoujaud/PEPit/blob/master/ressources/demo/PEPit_demo_extracting_a_proof_init_student_version.ipynb)"
    ]
   },
   {
@@ -55,7 +55,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2d37f524",
+   "id": "02dc4a16",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -163,7 +163,7 @@
     "    x1 = x0 - gamma * f.gradient(x0)\n",
     "\n",
     "    # Set the performance metric to the function values accuracy\n",
-    "    problem.set_performance_metric( (x1 - xs) ** 2 ) #TODO OPERAND\n",
+    "    problem.set_performance_metric( (x1 - xs) ** 2 )\n",
     "\n",
     "    # Solve the PEP\n",
     "    pepit_verbose = max(verbose, 0)\n",
@@ -442,7 +442,7 @@
     "    x1 = x0 - gamma * f.gradient(x0)\n",
     "\n",
     "    # Set the performance metric to the function values accuracy\n",
-    "    problem.set_performance_metric( f(x1) - fs ) #TODO OPERAND\n",
+    "    problem.set_performance_metric( f(x1) - fs )\n",
     "\n",
     "    # Solve the PEP\n",
     "    pepit_verbose = max(verbose, 0)\n",
@@ -1086,9 +1086,16 @@
     "    # Output prepared problem\n",
     "    pepit_verbose = max(verbose, 0)\n",
     "    problem._prepare_constraints(verbose=pepit_verbose)\n",
-    "    problem._list_of_prepared_constraints[4].deactivate()\n",
-    "    problem._list_of_prepared_constraints[6].deactivate()\n",
-    "    problem._list_of_prepared_constraints[7].deactivate()\n",
+    "    \n",
+    "    # Complete below: what constraints should be deactivated?\n",
+    "    \n",
+    "    #\n",
+    "    #\n",
+    "    # YOUR CODE HERE\n",
+    "    #\n",
+    "    #\n",
+    "    \n",
+    "\n",
     "\n",
     "    pepit_tau = problem.solve(verbose=pepit_verbose)\n",
     "\n",
@@ -1358,7 +1365,9 @@
     "\n",
     "# This is the LMI:\n",
     "LMI = sm.simplify(sm.hessian( -Lagrangian , (x0,g0))/2)\n",
-    "LMI"
+    "\n",
+    "\n",
+    "LMI # Should this matrix be PSD or NSD?"
    ]
   },
   {