Merge pull request #235 from pobonomo/master

otfix to L2O Part 1 notebook: Computation of violations and objective in notebook

Author: drgona

examples/parametric_programming/Part_1_basics.ipynb

@@ -936,7 +936,7 @@
     "    con_viol_mean = np.mean(con_viol)\n",
     "    return con_viol_mean\n",
     "\n",
-    "def eval_objective(x, y):\n",
+    "def eval_objective(x, y, a):\n",
     "    obj_value_mean = np.mean((1 - x) ** 2 + a * (y - x ** 2) ** 2)   \n",
     "    return obj_value_mean\n",
     "\n",
@@ -978,7 +978,7 @@
     "t = time.time()\n",
     "x_solver, y_solver = [], []\n",
     "for i in range(0, n_samples):\n",
-    "    prob, x, y = NLP_param(p_samples[i].numpy(), a_samples[i].numpy(), opti_silent=True)\n",
+    "    prob, x, y = NLP_param(a_samples[i].numpy(), p_samples[i].numpy(), opti_silent=True)\n",
     "    sol = prob.solve()\n",
     "    x_solver.append(sol.value(x))\n",
     "    y_solver.append(sol.value(y))\n",
@@ -1002,33 +1002,33 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Solution for 1000 problems via Neuromancer obtained in 0.0038 seconds\n",
-      "Neuromancer mean constraints violation 0.3537\n",
-      "Neuromancer mean objective value 0.0610\n",
+      "Solution for 1000 problems via Neuromancer obtained in 0.0074 seconds\n",
+      "Neuromancer mean constraints violation 0.0000\n",
+      "Neuromancer mean objective value 0.0550\n",
       "\n",
-      "Solution for 1000 problems via solver obtained in 9.5317 seconds\n",
-      "Solver mean constraints violation 0.0746\n",
-      "Solver mean objective value 0.2036\n",
+      "Solution for 1000 problems via solver obtained in 4.4246 seconds\n",
+      "Solver mean constraints violation 0.0000\n",
+      "Solver mean objective value 0.0468\n",
       "\n",
-      "Solution speedup factor 2540.9132\n",
-      "MSE primal optimizers: 0.11051816632586187\n",
-      "mean objective value discrepancy: 70.02 %\n"
+      "Solution speedup factor 598.4203\n",
+      "MSE primal optimizers: 0.1598999456654803\n",
+      "mean objective value discrepancy: 17.48 %\n"
      ]
     }
    ],
    "source": [
     "# Evaluate neuromancer solution\n",
     "print(f'Solution for {n_samples} problems via Neuromancer obtained in {nm_time:.4f} seconds')\n",
-    "nm_con_viol_mean = eval_constraints(x_nm, y_nm, p)\n",
+    "nm_con_viol_mean = eval_constraints(x_nm.ravel(), y_nm.ravel(), p_samples.numpy().ravel())\n",
     "print(f'Neuromancer mean constraints violation {nm_con_viol_mean:.4f}')\n",
-    "nm_obj_mean = eval_objective(x_nm, y_nm)\n",
+    "nm_obj_mean = eval_objective(x_nm.ravel(), y_nm.ravel(), a_samples.numpy().ravel())\n",
     "print(f'Neuromancer mean objective value {nm_obj_mean:.4f}\\n')\n",
     "\n",
     "# Evaluate solver solution\n",
     "print(f'Solution for {n_samples} problems via solver obtained in {solver_time:.4f} seconds')\n",
-    "solver_con_viol_mean = eval_constraints(x_solver, y_solver, p)\n",
+    "solver_con_viol_mean = eval_constraints(x_solver, y_solver, p_samples.numpy().ravel())\n",
     "print(f'Solver mean constraints violation {solver_con_viol_mean:.4f}')\n",
-    "solver_obj_mean = eval_objective(x_solver, y_solver)\n",
+    "solver_obj_mean = eval_objective(x_solver, y_solver, a_samples.numpy().ravel())\n",
     "print(f'Solver mean objective value {solver_obj_mean:.4f}\\n')\n",
     "\n",
     "# neuromancer solver comparison\n",