update notebook

notebooks/Linear Optimizer Check.ipynb

@@ -3,9 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import datetime as dt\n",
@@ -29,9 +27,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def time_function(py_callable, n):\n",
@@ -45,18 +41,23 @@
     "\n",
     "    bndl = np.zeros(n)\n",
     "    bndu = 0.01 * np.ones(n)\n",
-    "    risk_constraints = np.ones((n,1))\n",
+    "    risk_constraints1 = np.ones((n,1))\n",
+    "    risk_constraints2 = np.zeros((n,1))\n",
+    "    risk_constraints2[0][0] = 1.\n",
+    "    risk_constraints2[1][0] = 1.\n",
+    "    risk_constraints = np.concatenate((risk_constraints1, risk_constraints2), axis=1)\n",
     "\n",
     "    curr_risk_exposure = risk_constraints.T @ w\n",
+    "    risk_targets = np.array([1., 0.015])\n",
     "\n",
     "    constraints = [w >= bndl,\n",
     "                   w <= bndu,\n",
-    "                   curr_risk_exposure >= 1.,\n",
-    "                   curr_risk_exposure <= 1.]\n",
-    "\n",
+    "                   curr_risk_exposure >= risk_targets,\n",
+    "                   curr_risk_exposure <= risk_targets]\n",
+    "    \n",
+    "    np.random.seed(1)\n",
     "    er = np.random.randn(n)\n",
     "\n",
-    "\n",
     "    objective = cvxpy.Minimize(-w.T * er)\n",
     "    prob = cvxpy.Problem(objective, constraints)\n",
     "    prob.solve(solver='GLPK')\n",
@@ -69,12 +70,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "print(\"{0:<8}{1:>12}{2:>12}{3:>12}{4:>12}{5:>12}\".format('Scale(n)', 'time(ms)', 'feval', 'min(x)', 'max(x)', 'sum(x)'))\n",
+    "print(\"{0:<8}{1:>12}{2:>12}{3:>12}{4:>12}{5:>12}{6:>15}\".format('Scale(n)', 'time(ms)', 'feval', 'min(x)', 'max(x)', 'sum(x)', 'x(0) + x(1)'))\n",
     "\n",
-    "for n in range(200, 3200, 200):\n",
+    "for n in range(200, 10200, 200):\n",
     "    elapsed, result = time_function(cvxpy_lp, n)\n",
     "    s = np.array(result[0].value).flatten()\n",
-    "    print(\"{0:<8}{1:>12.2f}{2:>12.2f}{3:>12f}{4:>12f}{5:>12f}\".format(n, elapsed*1000, result[1].value, s.min(), s.max(), s.sum()))"
+    "    print(\"{0:<8}{1:>12.2f}{2:>12.2f}{3:>12f}{4:>12f}{5:>12f}{6:>15}\".format(n, elapsed*1000, result[1].value, s.min(), s.max(), s.sum(), s[0] + s[1]))"
    ]
   },
   {
@@ -86,12 +87,17 @@
    "outputs": [],
    "source": [
     "def clp_lp(n):\n",
+    "    np.random.seed(1)\n",
     "    er = np.random.randn(n)\n",
     "\n",
     "    bndl = np.zeros(n)\n",
     "    bndu = 0.01 * np.ones(n)\n",
-    "    risk_constraints = np.ones((n,1))\n",
-    "    risk_target = np.ones(1), np.ones(1)\n",
+    "    risk_constraints1 = np.ones((n,1))\n",
+    "    risk_constraints2 = np.zeros((n,1))\n",
+    "    risk_constraints2[0][0] = 1.\n",
+    "    risk_constraints2[1][0] = 1.\n",
+    "    risk_constraints = np.concatenate((risk_constraints1, risk_constraints2), axis=1)\n",
+    "    risk_target = np.array([1., 0.015]), np.array([1., 0.015])\n",
     "    \n",
     "    result = linear_build(er, bndl, bndu, risk_constraints, risk_target)\n",
     "    return result"
@@ -103,12 +109,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "print(\"{0:<8}{1:>12}{2:>12}{3:>12}{4:>12}{5:>12}\".format('Scale(n)', 'time(ms)', 'feval', 'min(x)', 'max(x)', 'sum(x)'))\n",
+    "print(\"{0:<8}{1:>12}{2:>12}{3:>12}{4:>12}{5:>12}{6:>15}\".format('Scale(n)', 'time(ms)', 'feval', 'min(x)', 'max(x)', 'sum(x)', 'x(0) + x(1)'))\n",
     "\n",
-    "for n in range(200, 3200, 200):\n",
+    "for n in range(200, 10200, 200):\n",
     "    elapsed, result = time_function(clp_lp, n)\n",
     "    s = result[2]\n",
-    "    print(\"{0:<8}{1:>12.2f}{2:>12.2f}{3:>12f}{4:>12f}{5:>12f}\".format(n, elapsed*1000, result[1], s.min(), s.max(), s.sum()))"
+    "    print(\"{0:<8}{1:>12.2f}{2:>12.2f}{3:>12f}{4:>12f}{5:>12f}{6:>15}\".format(n, elapsed*1000, result[1], s.min(), s.max(), s.sum(), s[0] + s[1]))"
    ]
   },
   {
@@ -123,7 +129,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python [default]",
    "language": "python",
    "name": "python3"
   },