whoops, missing plots

psets/pset5sol.ipynb

@@ -31,7 +31,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0075a58b",
+   "id": "cb563847",
    "metadata": {},
    "source": [
     "### Solution:\n",
@@ -44,23 +44,23 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "f147b166",
+   "id": "b77b6846",
    "metadata": {},
    "outputs": [
     {
-     "ename": "LoadError",
-     "evalue": "UndefVarError: plot not defined",
-     "output_type": "error",
-     "traceback": [
-      "UndefVarError: plot not defined",
-      "",
-      "Stacktrace:",
-      " [1] top-level scope",
-      "   @ In[1]:3"
-     ]
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "Figure(PyObject <Figure size 640x480 with 1 Axes>)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
+    "using PyPlot\n",
+    "\n",
     "# plot constraints\n",
     "x₁ = range(-2,2,length=200)\n",
     "plot(x₁, x₁.^3, \"b-\", linewidth=3)\n",
@@ -89,7 +89,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "71a039ef",
+   "id": "376a7e9c",
    "metadata": {},
    "source": [
     "**(b)** Our objective is $f_0(x) = x_1$, and our two constraints $f_i(x) \\le 0$ are $f_1(x) = x_2 - x_1^3$ and $f_2(x) = -x_2$.  Therefore, at the optimum $x_* = (0,0)$, their gradients are:\n",
@@ -110,20 +110,18 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "344ade48",
+   "id": "f2bfd986",
    "metadata": {},
    "outputs": [
     {
-     "ename": "LoadError",
-     "evalue": "UndefVarError: plot not defined",
-     "output_type": "error",
-     "traceback": [
-      "UndefVarError: plot not defined",
-      "",
-      "Stacktrace:",
-      " [1] top-level scope",
-      "   @ In[2]:3"
-     ]
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgMAAAGbCAYAAABZBpPkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAA9hAAAPYQGoP6dpAABs4UlEQVR4nO3dd3QUVRsG8GfTe0gIoYWQgPQuKEWQovRqASkiXaQpIiCgQlAgAgJiAdQPAUXAQhFQmkAABZQqTXovoQWSkJ7s/f64bJLJ7ia7ySaz5fmdsyfZuzOz72SSzLu3aoQQAkREROSwnNQOgIiIiNTFZICIiMjBMRkgIiJycEwGiIiIHByTASIiIgfHZICIiMjBMRkgIiJycEwGiIiIHByTASIiIgfHZICIiMjBMRkgIiJycEwG7ExERAQ0Gg3u3buX57ZLly6FRqPB5cuXCz8wE8THx2P8+PFo06YNSpQoAY1Gg4iIiFz3+fDDD1G9enVotVoA5p3/4sWLUbZsWSQkJFgifFXpzlunqK7t3r17ERERgYcPHxb4WFWqVIFGo8nz4ePjg/j4eMW+jx49wpAhQ1C2bFm4uLigQoUKRR6HvcVgCktef1KXi9oBEOncv38fX3/9NerUqYNu3brhf//7X67b37x5E7NmzcLSpUvh5GR+XtuvXz/MnDkTs2bNwtSpU/MbtlXq2LEj9u3bh9KlSxfq++zduxdTp05F//79UaxYsQIda9CgQXj33XcxdOhQVK1aVe/1e/fuYfr06ejZsyd8fX0Vr40ZMwarV6/GggULUL58efj7+xd5HPYWgyksef1JZYKsWkJCglnbT5kyRQAQd+/ezXPbJUuWCADi0qVL+YzOsrRardBqtUIIIe7evSsAiClTphjdfvz48aJs2bIiIyMjs8yc8xdCiE8++UT4+/ub/XO2FEu9r+68i9rs2bMt9jt0+/Zt4erqKkaMGGHw9blz5woAYv/+/YrylJQU4ePjI8aNG1fgGPIbhz3GYApLXn9SF5OBIqD7R3348GHxwgsvCF9fX+Hn5yf69Okj7ty5o7fdoUOHxEsvvSSKFSsmSpUqlfn6nj17RKtWrYSPj4/w9PQUjRs3Fhs3bszXewlhOBk4e/as6NWrlyhRooRwc3MTVatWFV988YXRc/r333/Fyy+/LPz8/ERAQIB4++23RVpamjh9+rRo27at8PHxEeXLlxczZ84062eWVzKQkpIiihcvrvePz5zzF0KIW7duCY1GIxYvXmxWfNmtW7dO1KpVS7i5uYnw8HDx6aefGrw553Z9z507J/r37y+eeOIJ4enpKcqUKSM6deokjh07pvd+GzduFHXq1BFubm4iLCxMzJ49W+/9jCV6plxf3bFOnDghevbsKfz8/ERwcLAYMGCAePjwod52OR87d+4Ud+7cEUOGDBEhISHCzc1NBAUFiSZNmoht27bl+rN88cUXRUBAgEhOTtZ7rWbNmqJWrVqKsv79++u9f8OGDXN9D1OYE4e9xWDq72Ju19+YmzdvCm9vb/HKK68oyjds2CBcXFzEpEmT8hUzFRyTgSKg+6MpX768GDdunNiyZYuYO3eu8Pb2FvXq1ROpqal627377rti27ZtYt26dUIIIaKiooSrq6uoX7+++PHHH8W6detEmzZthEajEatWrTL7vYTQv2GcPHlS+Pv7i1q1aonvvvtObN26VbzzzjvCyclJREREGDynKlWqiI8++khs27ZNjB8/XgAQI0eOFFWrVhWfffaZ2LZtmxgwYIAAIFavXm3yzyyvZGD37t0CgPj999/z9bPOrlq1auLFF19UlAEQzZs3zzPOTZs2CScnJ9GiRQuxdu1a8fPPP4uGDRuKsLAwo8mAoeu7a9cu8c4774hffvlF7Nq1S6xdu1Z069ZNeHp6itOnT2ce448//hDOzs6iadOmYs2aNeLnn38WTz31lAgNDc0zGTD1+ma/tpMnTxbbtm0Tc+fOFe7u7mLAgAGZ2127dk2MGjVKABBr1qwR+/btE/v27ROxsbGibdu2okSJEuLrr78WUVFRYt26dWLy5MmK31VjP08AYuXKlYry/fv3CwBi/vz5ivL//vtPTJw4UQAQ69evF/v27RNnz57N46rlzZw47C0GU38Xc7v+uZk6darQaDTi4MGDQgghdu7cKTw8PMSoUaPyFS9ZBpOBIqD75/r2228ryn/44QcBQCxfvlyx3eTJk/WO0ahRIxEcHCzi4+Mzy9LT00XNmjVFSEhIZvW6qe8lhP4No23btiIkJETvj3nkyJHCw8NDxMTE6J3TnDlzFNvWrVs385+DTlpamihRooTeDTc3eSUDM2fOFABEdHS0otyc89fp06ePKFmypKLM2dlZtGrVKs84n3rqKVGuXDmRkpKSWRYfHy+KFy9uNBkwdH1zSk9PF6mpqaJSpUqKc2nYsKEoU6aMSEpKyiyLi4sTgYGBeSYDpl5fXZyzZs1SbDd8+HDh4eGR+bsmhPFqYh8fHzF69Og8zzOnjIwMERoaKp5//nlF+euvv673O6gzatQoERAQkPn8zp07okOHDsLLy0tUqlRJbN26tdDjyBmDEEIsWLBA1KtXT7i4uOTa3FVYMSQnJ4v+/fuLkJAQ4evrKxo2bCj++usvs+Mw9rsoRP6aCRISEkSZMmXEc889J/755x/h6+srBgwYoPi9ym7JkiW51jaQZXA0QRHq06eP4nmPHj3g4uKCnTt3KspfeuklxfOEhAT8/fffePnll+Hj45NZ7uzsjL59++L69es4c+ZMvt5LJzk5Gdu3b8cLL7wALy8vpKenZz46dOiA5ORk7N+/X2+/Tp06KZ5Xq1YNGo0G7du3zyxzcXHBE088gStXrhh87/y4efMmNBoNgoKCDL5uzvkHBwfjzp07SE9PzyxLT0/H9u3bc40hISEBBw8eRLdu3eDm5pZZ7uPjg86dOxvdL+f11b3fjBkzUL16dbi5ucHFxQVubm44d+4c/vvvv8z3O3DgAF588UV4eHhk7uvr65vr+wH5u75dunRRPK9duzaSk5Nx586dXN8LAJ5++mksXboU06ZNw/79+5GWlpbnPgDg5OSEgQMHYvv27Zm/L4mJiVi1ahVeeuklBAQE6O1z6NAh1K9fP/P5iBEjUKpUKdy9exeffPIJevTogfv375v0/vmNI2cMAFC6dGlMnToV3bp1M+u9LRVDeno6wsPD8ddff+Hhw4cYNmwYunTpgsTExFzf15TfxYLw8vLCtGnTsH37drRs2RLt27fHN998oxgNAwArVqzAjz/+CAAQQuDzzz/Htm3bCvz+ZBiTgSJUqlQpxXMXFxcUL15c7x9Vzh7gDx48gBDCYM/wMmXKAIDeMUx9L5379+8jPT0dn3/+OVxdXRWPDh06AIDB4XqBgYGK525ubvDy8lLcrHTlycnJBt87P5KSkuDq6gpnZ2eDr5tz/h4eHhBCmB2f7rqULFlS7zVDZTqGruOYMWPwwQcfoFu3btiwYQP+/vtvHDhwAHXq1EFSUlLm+2m1Wr1zA/TPN6f8XN/ixYsrnru7uwNAZjy5+fHHH9GvXz/873//Q+PGjREYGIjXXnsN0dHRee47cOBAaDQaLF26FADw888/Iy4uDkOGDNHbNiMjA0ePHs28CT569Ajr1q1DREQEvLy80KVLF9SpUwe//vprnu+b3zhyxqDTrVs3dO7cuUA9+gsSg7e3NyZPnozQ0FA4OTmhX79+0Gq1OHfuXK7vacrvYkFVrlwZADLPzdDf8SuvvILo6Gh8+umnmDRpEooVK4bWrVtb5P1JH4cWFqHo6GiULVs283l6ejru37+v9083Z4YcEBAAJycn3Lp1S++YN2/eBAC9T8imvlf299DVNIwYMcLgNuHh4bmcXdEKCgpCamoqEhIS4O3trfe6OecfExMDd3d3Ra2LKQICAqDRaHD79m2D729MzusLAMuXL8drr72GGTNmKMrv3buXOWRL936Gjp3XTbaor29QUBA+/fRTfPrpp7h69SrWr1+PCRMm4M6dO9i8eXOu+5YrVw5t27bFkiVLMHnyZCxevBiVKlVC8+bN9bb977//kJiYmHkTPHfuHHx8fFCuXLnMbWrVqoWTJ0+afQ6mxpEzBkuyZAynT59GUlISKlasmOt7mvK7WBBHjx5Fp06d8Mwzz+Cvv/7Ct99+a/R3Uve3optXgQoPawaK0A8//KB4/tNPPyE9PR0tWrTIdT9vb280bNgQa9asUWTmWq0Wy5cvR0hISGamnd/38vLyQsuWLXHkyBHUrl0bDRo00HsYSyTUoBt7feHCBYOvm3P+Fy9eRPXq1c2OwdvbGw0aNMC6deuQmpqaWf7o0SNs3LjRrGNpNJrMT946v/32G27cuKF4v6effhpr1qxR1GLEx8djw4YNuR6/sK6vKbUFoaGhGDlyJFq3bo3Dhw+bdNzBgwfjypUrWLRoEfbs2YPBgwcb3O7gwYMAoKgZ8PPzU2zj5+eHR48emfS++YkjZwyWZokYEhMT0bdvX7z//vt5Jr2m/C7qmFNbBABnzpxB27Zt0bhxY+zcuRNdu3ZFREQEYmNj9bb98ccfERwcjNGjR2P69OmIiYlhM0EhYs1AEVqzZg1cXFzQunVrnDx5Eh988AHq1KmDHj165LlvZGQkWrdujZYtW2Ls2LFwc3PDggULcOLECaxcuVIva87Pe82fPx9NmzZFs2bNMGzYMISFhSE+Ph7nz5/Hhg0bsGPHjgL/DPKyadMmJCQkZM6qdurUKfzyyy8AgA4dOsDLywsAMm/q+/fvR+3atfWOY+r5a7Va/PPPPxg0aJCi3MXFBc2bN8+z38CHH36Ijh07om3btnjrrbeQkZGB2bNnw8fHBzExMSafd6dOnbB06VJUrVoVtWvXxqFDhzB79myEhIQotvvoo4/Qrl07tG7dGu+88w4yMjIwc+ZMeHt75/l+hXF9a9WqlXnsfv36wdXVFZUqVULLli3Ru3dvVK1Line truncated
+      "text/plain": [
+       "Figure(PyObject <Figure size 640x480 with 1 Axes>)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -677,7 +675,7 @@
     {
      "data": {
       "text/plain": [
-       "PyObject <matplotlib.legend.Legend object at 0x290b7ddf0>"
+       "PyObject <matplotlib.legend.Legend object at 0x295a97af0>"
       ]
      },
      "execution_count": 14,
@@ -727,7 +725,7 @@
     {
      "data": {
       "text/plain": [
-       "PyObject <matplotlib.legend.Legend object at 0x2a053fd00>"
+       "PyObject <matplotlib.legend.Legend object at 0x29798e580>"
       ]
      },
      "execution_count": 15,