update example

notebooks/Example 10 - Quadratic Optimizer Comparison with CVXOPT.ipynb

@@ -207,8 +207,8 @@
    "source": [
     "def time_function(py_callable, n):\n",
     "    start = dt.datetime.now()\n",
-    "    py_callable(n)\n",
-    "    return (dt.datetime.now() - start).total_seconds()"
+    "    val = py_callable(n)\n",
+    "    return (dt.datetime.now() - start).total_seconds(), val"
    ]
   },
   {
@@ -229,7 +229,8 @@
     "                   sum_entries(w) == 1,]\n",
     "\n",
     "    prob = Problem(objective, constraints)\n",
-    "    prob.solve(verbose=False, solver='CVXOPT', display=False)"
+    "    prob.solve(verbose=False, solver='CVXOPT', display=False)\n",
+    "    return prob.value"
    ]
   },
   {
@@ -260,7 +261,8 @@
     "    b = matrix(b)\n",
     "    \n",
     "    solvers.options['show_progress'] = False\n",
-    "    sol = solvers.qp(P, q, G, h, A, b)"
+    "    sol = solvers.qp(P, q, G, h, A, b)\n",
+    "    return sol['primal objective']"
    ]
   },
   {
@@ -276,7 +278,7 @@
     "    clb = np.ones(1)\n",
     "    cub = np.ones(1)\n",
     "    qpopt = QPOptimizer(signal, sec_cov_values, lbound, ubound, cons_matrix, clb, cub, 1.)\n",
-    "    qpopt.feval()"
+    "    return qpopt.feval()"
    ]
   },
   {
@@ -294,27 +296,16 @@
     "for i, n in enumerate(n_steps):\n",
     "    sec_cov_values = sec_cov_values_full[:n, :n]\n",
     "    signal = signal_full[:n]\n",
-    "    cvxpy_times[i] = time_function(cvxpy, n) * 1000\n",
-    "    cvxopt_times[i] = time_function(cvxopt, n) * 1000\n",
-    "    ipopt_times[i] = time_function(ipopt, n) * 1000\n",
+    "    cvxpy_times[i], val1 = time_function(cvxpy, n)\n",
+    "    cvxopt_times[i], val2 = time_function(cvxopt, n)\n",
+    "    ipopt_times[i], val3 = time_function(ipopt, n)\n",
     "    \n",
-    "    print(\"{0:<8}{1:>12.2f}{2:>12.2f}{3:>12.2f}\".format(n, cvxpy_times[i], cvxopt_times[i], ipopt_times[i]))"
+    "    np.testing.assert_almost_equal(val1, val2, 4)\n",
+    "    np.testing.assert_almost_equal(val2, val3, 4)\n",
+    "    \n",
+    "    print(\"{0:<8}{1:>12.4f}{2:>12.4f}{3:>12.4f}\".format(n, cvxpy_times[i], cvxopt_times[i], ipopt_times[i]))"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -339,7 +330,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.5"
   },
   "varInspector": {
    "cols": {

notebooks/Example 7 - Portfolio Optimizer Performance.ipynb

@@ -113,8 +113,8 @@
     "    objective = cvxpy.Minimize(-w.T * er)\n",
     "    prob = cvxpy.Problem(objective, constraints)\n",
     "    \n",
-    "    prob.solve(solver='GLPK')\n",
-    "    elasped_time2 = timeit.timeit(\"prob.solve(solver='GLPK')\",\n",
+    "    prob.solve()\n",
+    "    elasped_time2 = timeit.timeit(\"prob.solve()\",\n",
     "                                  number=number, globals=globals()) / number * 1000\n",
     "\n",
     "    np.testing.assert_almost_equal(x1 @ er, np.array(w.value).flatten() @ er, 4)\n",
@@ -177,13 +177,13 @@
     "                                   current_position=current_position,\n",
     "                                   method='interior')\n",
     "    elasped_time1 = timeit.timeit(\"\"\"linear_builder(er,\n",
-    "                                                     lbound,\n",
-    "                                                     ubound,\n",
-    "                                                     risk_constraints,\n",
-    "                                                     risk_target,\n",
-    "                                                     turn_over_target=turn_over_target,\n",
-    "                                                     current_position=current_position,\n",
-    "                                                     method='interior')\"\"\", number=number, globals=globals()) / number * 1000\n",
+    "                                                    lbound,\n",
+    "                                                    ubound,\n",
+    "                                                    risk_constraints,\n",
+    "                                                    risk_target,\n",
+    "                                                    turn_over_target=turn_over_target,\n",
+    "                                                    current_position=current_position,\n",
+    "                                                    method='interior')\"\"\", number=number, globals=globals()) / number * 1000\n",
     "    \n",
     "    w = cvxpy.Variable(n)\n",
     "    curr_risk_exposure = risk_constraints.T @ w\n",
@@ -196,18 +196,18 @@
     "    objective = cvxpy.Minimize(-w.T * er)\n",
     "    prob = cvxpy.Problem(objective, constraints)\n",
     "    \n",
-    "    prob.solve(solver='GLPK')\n",
-    "    elasped_time2 = timeit.timeit(\"prob.solve(solver='GLPK')\",\n",
+    "    prob.solve()\n",
+    "    elasped_time2 = timeit.timeit(\"prob.solve()\",\n",
     "                                  number=number, globals=globals()) / number * 1000\n",
     "    \n",
     "    elasped_time3 = timeit.timeit(\"\"\"linear_builder(er,\n",
-    "                                                     lbound,\n",
-    "                                                     ubound,\n",
-    "                                                     risk_constraints,\n",
-    "                                                     risk_target,\n",
-    "                                                     turn_over_target=turn_over_target,\n",
-    "                                                     current_position=current_position,\n",
-    "                                                     method='simplex')\"\"\", number=number, globals=globals()) / number * 1000\n",
+    "                                                    lbound,\n",
+    "                                                    ubound,\n",
+    "                                                    risk_constraints,\n",
+    "                                                    risk_target,\n",
+    "                                                    turn_over_target=turn_over_target,\n",
+    "                                                    current_position=current_position,\n",
+    "                                                    method='simplex')\"\"\", number=number, globals=globals()) / number * 1000\n",
     "    \n",
     "    \n",
     "    np.testing.assert_almost_equal(x1 @ er, np.array(w.value).flatten() @ er, 4)\n",
@@ -529,7 +529,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.5"
   },
   "varInspector": {
    "cols": {

notebooks/Example 8 - Neutralize Algorithm Comparison.ipynb

@@ -355,7 +355,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.5"
   },
   "varInspector": {
    "cols": {

notebooks/Example 9 - Linear Optimizer Comparison with CVXOPT.ipynb

@@ -58,7 +58,7 @@
     "\n",
     "    objective = cvxpy.Minimize(-w.T * er)\n",
     "    prob = cvxpy.Problem(objective, constraints)\n",
-    "    prob.solve(solver='GLPK')\n",
+    "    prob.solve()\n",
     "    return w, prob"
    ]
   },
@@ -113,6 +113,13 @@
     "    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]))"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -137,7 +144,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.6.5"
   },
   "varInspector": {
    "cols": {