MeandataVisual.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Date  General Electric  Chesapeake Energy   AT&T  Cisco Systems  \\\n",
      "0   2007-12-31             37.08              39.36  42.12          27.25   \n",
      "1   2008-03-31             36.63              45.76  37.71          24.08   \n",
      "2   2008-06-30             26.35              66.77  32.75          23.47   \n",
      "3   2008-09-30             23.87              33.29  28.35          22.25   \n",
      "4   2008-12-31             15.82              15.34  28.19          16.18   \n",
      "5   2009-03-31             10.33              17.65  25.44          16.53   \n",
      "6   2009-06-30             11.84              19.72  25.14          18.94   \n",
      "7   2009-09-30             16.83              28.80  27.34          23.55   \n",
      "8   2009-12-31             15.27              26.36  28.29          24.10   \n",
      "9   2010-03-31             18.21              23.52  25.90          26.52   \n",
      "10  2010-06-30             14.47              21.47  24.35          21.52   \n",
      "11  2010-09-30             16.46              22.66  28.78          22.05   \n",
      "12  2010-12-31             18.15              25.89  29.36          20.24   \n",
      "13  2011-03-31             20.08              34.58  30.63          17.32   \n",
      "14  2011-06-30             18.59              29.18  31.39          15.36   \n",
      "15  2011-09-30             15.65              26.52  28.73          15.63   \n",
      "16  2011-12-31             18.03              22.63  30.10          18.19   \n",
      "17  2012-03-31             20.07              23.38  31.32          21.12   \n",
      "18  2012-06-30             20.42              18.28  35.70          16.79   \n",
      "19  2012-09-30             22.77              18.97  37.91          19.00   \n",
      "20  2012-12-31             20.29              16.50  33.13          19.38   \n",
      "21  2013-03-31             23.14              20.44  36.69          20.84   \n",
      "22  2013-06-30             23.32              20.48  35.55          24.43   \n",
      "23  2013-09-30             23.88              25.92  33.78          23.12   \n",
      "24  2013-12-31             27.90              27.08  35.17          22.25   \n",
      "25  2014-03-31             26.02              25.72  35.30          22.49   \n",
      "26  2014-06-30             26.44              30.43  35.40          24.67   \n",
      "27  2014-09-30             25.36              23.60  35.26          24.90   \n",
      "28  2014-12-31             25.42              19.46  33.98          28.28   \n",
      "29  2015-03-31             25.09              13.88  32.97          27.34   \n",
      "30  2015-06-30             26.82              11.15  36.03          27.83   \n",
      "31  2015-09-30             24.73               6.88  32.36          26.00   \n",
      "32  2015-12-31             30.86               4.38  34.56          27.38   \n",
      "33  2016-03-31             31.89               3.97  39.42          28.53   \n",
      "34  2016-06-30             30.66               4.39  42.69          28.38   \n",
      "35  2016-09-30             29.60               6.13  40.91          31.61   \n",
      "36  2016-12-31             31.63               7.15  42.76          30.56   \n",
      "37  2017-03-31             29.86               5.83  41.79          33.72   \n",
      "38  2017-06-30             27.09               5.00  37.73          31.79   \n",
      "39  2017-09-30             24.22               4.34  38.95          33.31   \n",
      "40  2017-12-31             17.27               4.08  39.22          38.41   \n",
      "41  2018-03-31             12.92               3.03  34.85          44.49   \n",
      "\n",
      "    Campbell Soup  \n",
      "0           35.77  \n",
      "1           33.91  \n",
      "2           33.03  \n",
      "3           38.30  \n",
      "4           29.49  \n",
      "5           27.03  \n",
      "6           29.66  \n",
      "7           32.94  \n",
      "8           34.14  \n",
      "9           35.25  \n",
      "10          35.99  \n",
      "11          36.10  \n",
      "12          34.50  \n",
      "13          33.35  \n",
      "14          34.75  \n",
      "15          32.09  \n",
      "16          33.54  \n",
      "17          33.35  \n",
      "18          32.72  \n",
      "19          34.65  \n",
      "20          34.50  \n",
      "21          44.90  \n",
      "22          44.74  \n",
      "23          40.73  \n",
      "24          43.35  \n",
      "25          44.82  \n",
      "26          45.67  \n",
      "27          42.74  \n",
      "28          44.60  \n",
      "29          46.36  \n",
      "30          47.27  \n",
      "31          51.00  \n",
      "32          53.05  \n",
      "33          64.05  \n",
      "34          62.96  \n",
      "35          54.68  \n",
      "36          60.93  \n",
      "37          57.15  \n",
      "38          52.10  \n",
      "39          47.16  \n",
      "40          48.48  \n",
      "41          42.48  \n",
      "       Returns  Volatility  General Electric Weight  Chesapeake Energy Weight  \\\n",
      "0    -0.012764    0.120039                 0.242095                  0.379552   \n",
      "1     0.002007    0.099059                 0.397151                  0.026803   \n",
      "2     0.000759    0.083173                 0.261107                  0.026811   \n",
      "3    -0.003509    0.080866                 0.027942                  0.218436   \n",
      "4     0.007677    0.077756                 0.008698                  0.089331   \n",
      "...        ...         ...                      ...                       ...   \n",
      "4995 -0.003591    0.101448                 0.323222                  0.180850   \n",
      "4996 -0.007384    0.103067                 0.244592                  0.266842   \n",
      "4997 -0.002717    0.097707                 0.216250                  0.220659   \n",
      "4998 -0.004683    0.094871                 0.150691                  0.254934   \n",
      "4999 -0.001544    0.092984                 0.332967                  0.087717   \n",
      "\n",
      "      AT&T Weight  Cisco Systems Weight  Campbell Soup Weight  \n",
      "0        0.212347              0.067420              0.098586  \n",
      "1        0.075056              0.347543              0.153447  \n",
      "2        0.286160              0.153575              0.272345  \n",
      "3        0.430783              0.140906              0.181933  \n",
      "4        0.216427              0.502164              0.183380  \n",
      "...           ...                   ...                   ...  \n",
      "4995     0.011384              0.175036              0.309509  \n",
      "4996     0.203310              0.161097              0.124159  \n",
      "4997     0.096651              0.283049              0.183391  \n",
      "4998     0.207038              0.195649              0.191688  \n",
      "4999     0.139897              0.163375              0.276044  \n",
      "\n",
      "[5000 rows x 7 columns]\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.3517e-03 -1.0183e+00  1e+00  8e-17  3e+00\n",
      " 1:  1.8118e-03 -2.8254e-02  3e-02  6e-17  8e-02\n",
      " 2: -1.4950e-02 -1.9458e-02  5e-03  2e-16  2e-17\n",
      " 3: -1.7780e-02 -1.7871e-02  9e-05  2e-16  9e-18\n",
      " 4: -1.7834e-02 -1.7835e-02  9e-07  1e-16  9e-18\n",
      " 5: -1.7835e-02 -1.7835e-02  9e-09  1e-16  1e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.3950e-03 -1.0182e+00  1e+00  6e-17  3e+00\n",
      " 1:  1.8529e-03 -2.8227e-02  3e-02  1e-16  8e-02\n",
      " 2: -1.4853e-02 -1.9394e-02  5e-03  1e-16  2e-17\n",
      " 3: -1.7699e-02 -1.7791e-02  9e-05  1e-16  1e-17\n",
      " 4: -1.7756e-02 -1.7757e-02  9e-07  2e-16  1e-17\n",
      " 5: -1.7756e-02 -1.7756e-02  9e-09  1e-16  8e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.4435e-03 -1.0182e+00  1e+00  3e-17  3e+00\n",
      " 1:  1.8989e-03 -2.8196e-02  3e-02  8e-17  8e-02\n",
      " 2: -1.4745e-02 -1.9323e-02  5e-03  1e-16  2e-17\n",
      " 3: -1.7609e-02 -1.7701e-02  9e-05  1e-16  2e-17\n",
      " 4: -1.7667e-02 -1.7668e-02  9e-07  9e-17  2e-17\n",
      " 5: -1.7668e-02 -1.7668e-02  9e-09  2e-16  4e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.4980e-03 -1.0182e+00  1e+00  2e-16  3e+00\n",
      " 1:  1.9505e-03 -2.8162e-02  3e-02  8e-17  8e-02\n",
      " 2: -1.4625e-02 -1.9242e-02  5e-03  1e-16  1e-17\n",
      " 3: -1.7507e-02 -1.7600e-02  9e-05  3e-17  9e-18\n",
      " 4: -1.7569e-02 -1.7569e-02  9e-07  1e-16  8e-18\n",
      " 5: -1.7569e-02 -1.7569e-02  9e-09  2e-16  2e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.5590e-03 -1.0181e+00  1e+00  4e-16  3e+00\n",
      " 1:  2.0084e-03 -2.8123e-02  3e-02  2e-16  8e-02\n",
      " 2: -1.4491e-02 -1.9152e-02  5e-03  2e-16  1e-17\n",
      " 3: -1.7392e-02 -1.7486e-02  9e-05  2e-16  1e-17\n",
      " 4: -1.7457e-02 -1.7458e-02  9e-07  1e-16  2e-17\n",
      " 5: -1.7458e-02 -1.7458e-02  9e-09  2e-16  2e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.6275e-03 -1.0181e+00  1e+00  2e-16  3e+00\n",
      " 1:  2.0733e-03 -2.8079e-02  3e-02  1e-16  8e-02\n",
      " 2: -1.4342e-02 -1.9051e-02  5e-03  2e-16  3e-18\n",
      " 3: -1.7264e-02 -1.7358e-02  9e-05  5e-17  2e-17\n",
      " 4: -1.7333e-02 -1.7334e-02  9e-07  1e-16  2e-17\n",
      " 5: -1.7334e-02 -1.7334e-02  9e-09  8e-17  1e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.7043e-03 -1.0181e+00  1e+00  0e+00  3e+00\n",
      " 1:  2.1460e-03 -2.8030e-02  3e-02  1e-16  8e-02\n",
      " 2: -1.4177e-02 -1.8938e-02  5e-03  2e-16  4e-18\n",
      " 3: -1.7120e-02 -1.7214e-02  9e-05  1e-16  1e-17\n",
      " 4: -1.7193e-02 -1.7194e-02  9e-07  2e-16  1e-17\n",
      " 5: -1.7194e-02 -1.7194e-02  9e-09  1e-16  3e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.7903e-03 -1.0180e+00  1e+00  2e-16  3e+00\n",
      " 1:  2.2274e-03 -2.7975e-02  3e-02  5e-17  8e-02\n",
      " 2: -1.3994e-02 -1.8810e-02  5e-03  1e-16  1e-17\n",
      " 3: -1.6954e-02 -1.7055e-02  1e-04  2e-16  8e-18\n",
      " 4: -1.7036e-02 -1.7037e-02  1e-06  1e-16  1e-17\n",
      " 5: -1.7037e-02 -1.7037e-02  1e-08  4e-17  2e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.8868e-03 -1.0179e+00  1e+00  1e-16  3e+00\n",
      " 1:  2.3188e-03 -2.7913e-02  3e-02  6e-17  8e-02\n",
      " 2: -1.3791e-02 -1.8668e-02  5e-03  1e-16  2e-17\n",
      " 3: -1.6764e-02 -1.6879e-02  1e-04  2e-16  2e-17\n",
      " 4: -1.6860e-02 -1.6861e-02  1e-06  1e-16  1e-17\n",
      " 5: -1.6861e-02 -1.6861e-02  1e-08  3e-16  3e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.9949e-03 -1.0179e+00  1e+00  6e-17  3e+00\n",
      " 1:  2.4211e-03 -2.7843e-02  3e-02  7e-17  8e-02\n",
      " 2: -1.3567e-02 -1.8507e-02  5e-03  4e-16  2e-17\n",
      " 3: -1.6547e-02 -1.6682e-02  1e-04  1e-16  1e-17\n",
      " 4: -1.6663e-02 -1.6664e-02  1e-06  2e-16  1e-17\n",
      " 5: -1.6664e-02 -1.6664e-02  1e-08  2e-16  3e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  3.1160e-03 -1.0178e+00  1e+00  2e-16  3e+00\n",
      " 1:  2.5357e-03 -2.7765e-02  3e-02  1e-16  8e-02\n",
      " 2: -1.3319e-02 -1.8328e-02  5e-03  2e-16  2e-17\n",
      " 3: -1.6304e-02 -1.6461e-02  2e-04  1e-16  2e-17\n",
      " 4: -1.6441e-02 -1.6443e-02  2e-06  1e-16  2e-17\n",
      " 5: -1.6443e-02 -1.6443e-02  2e-08  1e-16  1e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  3.2517e-03 -1.0177e+00  1e+00  3e-16  3e+00\n",
      " 1:  2.6641e-03 -2.7677e-02  3e-02  8e-17  8e-02\n",
      " 2: -1.3046e-02 -1.8126e-02  5e-03  2e-16  1e-17\n",
      " 3: -1.6031e-02 -1.6213e-02  2e-04  3e-16  4e-17\n",
      " 4: -1.6192e-02 -1.6194e-02  2e-06  3e-17  9e-18\n",
      " 5: -1.6194e-02 -1.6194e-02  2e-08  1e-16  7e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  3.4038e-03 -1.0176e+00  1e+00  0e+00  3e+00\n",
      " 1:  2.8078e-03 -2.7578e-02  3e-02  1e-16  8e-02\n",
      " 2: -1.2745e-02 -1.7901e-02  5e-03  2e-16  2e-17\n",
      " 3: -1.5725e-02 -1.5935e-02  2e-04  5e-17  1e-17\n",
      " 4: -1.5913e-02 -1.5916e-02  2e-06  3e-16  2e-17\n",
      " 5: -1.5915e-02 -1.5915e-02  2e-08  2e-16  2e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  3.5740e-03 -1.0175e+00  1e+00  3e-17  3e+00\n",
      " 1:  2.9688e-03 -2.7467e-02  3e-02  8e-17  8e-02\n",
      " 2: -1.2415e-02 -1.7648e-02  5e-03  1e-16  9e-18\n",
      " 3: -1.5381e-02 -1.5624e-02  2e-04  9e-17  1e-17\n",
      " 4: -1.5600e-02 -1.5603e-02  3e-06  1e-16  2e-17\n",
      " 5: -1.5603e-02 -1.5603e-02  3e-08  1e-16  3e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  3.7647e-03 -1.0174e+00  1e+00  6e-17  3e+00\n",
      " 1:  3.1489e-03 -2.7341e-02  3e-02  8e-17  8e-02\n",
      " 2: -1.2054e-02 -1.7365e-02  5e-03  1e-16  1e-17\n",
      " 3: -1.4998e-02 -1.5275e-02  3e-04  3e-16  7e-18\n",
      " 4: -1.5248e-02 -1.5252e-02  4e-06  3e-16  2e-17\n",
      " 5: -1.5252e-02 -1.5252e-02  4e-08  1e-16  8e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  3.9782e-03 -1.0173e+00  1e+00  2e-16  3e+00\n",
      " 1:  3.3505e-03 -2.7200e-02  3e-02  1e-16  8e-02\n",
      " 2: -1.1658e-02 -1.7049e-02  5e-03  2e-16  2e-17\n",
      " 3: -1.4571e-02 -1.4885e-02  3e-04  2e-16  1e-17\n",
      " 4: -1.4853e-02 -1.4858e-02  5e-06  1e-16  8e-18\n",
      " 5: -1.4858e-02 -1.4858e-02  5e-08  1e-16  2e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  4.2172e-03 -1.0171e+00  1e+00  1e-16  3e+00\n",
      " 1:  3.5760e-03 -2.7042e-02  3e-02  1e-16  8e-02\n",
      " 2: -1.1227e-02 -1.6695e-02  5e-03  8e-17  1e-17\n",
      " 3: -1.4099e-02 -1.4450e-02  4e-04  1e-16  2e-17\n",
      " 4: -1.4409e-02 -1.4416e-02  8e-06  2e-16  1e-17\n",
      " 5: -1.4416e-02 -1.4416e-02  8e-08  5e-17  9e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  4.4846e-03 -1.0170e+00  1e+00  3e-16  3e+00\n",
      " 1:  3.8282e-03 -2.6863e-02  3e-02  1e-16  8e-02\n",
      " 2: -1.0758e-02 -1.6301e-02  6e-03  3e-16  2e-17\n",
      " 3: -1.3579e-02 -1.3965e-02  4e-04  1e-17  2e-17\n",
      " 4: -1.3909e-02 -1.3921e-02  1e-05  1e-16  8e-18\n",
      " 5: -1.3920e-02 -1.3920e-02  1e-07  2e-16  1e-17\n",
      " 6: -1.3920e-02 -1.3920e-02  1e-09  2e-16  4e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  4.7837e-03 -1.0168e+00  1e+00  8e-17  3e+00\n",
      " 1:  4.1100e-03 -2.6661e-02  3e-02  1e-16  8e-02\n",
      " 2: -1.0249e-02 -1.5861e-02  6e-03  3e-16  2e-17\n",
      " 3: -1.3012e-02 -1.3427e-02  4e-04  1e-16  3e-18\n",
      " 4: -1.3344e-02 -1.3365e-02  2e-05  1e-16  7e-18\n",
      " 5: -1.3364e-02 -1.3364e-02  3e-07  2e-16  2e-17\n",
      " 6: -1.3364e-02 -1.3364e-02  3e-09  3e-17  1e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  5.1182e-03 -1.0166e+00  1e+00  1e-16  3e+00\n",
      " 1:  4.4250e-03 -2.6434e-02  3e-02  7e-17  8e-02\n",
      " 2: -9.6979e-03 -1.5372e-02  6e-03  1e-16  2e-17\n",
      " 3: -1.2398e-02 -1.2832e-02  4e-04  2e-16  8e-18\n",
      " 4: -1.2712e-02 -1.2744e-02  3e-05  1e-17  8e-18\n",
      " 5: -1.2739e-02 -1.2740e-02  1e-06  1e-16  1e-17\n",
      " 6: -1.2740e-02 -1.2740e-02  2e-08  1e-16  9e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  5.4921e-03 -1.0163e+00  1e+00  6e-17  3e+00\n",
      " 1:  4.7768e-03 -2.6178e-02  3e-02  6e-17  8e-02\n",
      " 2: -9.1035e-03 -1.4829e-02  6e-03  1e-16  1e-17\n",
      " 3: -1.1740e-02 -1.2180e-02  4e-04  2e-16  7e-18\n",
      " 4: -1.2022e-02 -1.2057e-02  4e-05  2e-16  1e-17\n",
      " 5: -1.2038e-02 -1.2043e-02  4e-06  2e-16  8e-18\n",
      " 6: -1.2040e-02 -1.2041e-02  6e-07  3e-16  1e-17\n",
      " 7: -1.2040e-02 -1.2040e-02  6e-08  8e-17  1e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  5.9099e-03 -1.0161e+00  1e+00  3e-17  3e+00\n",
      " 1:  5.1694e-03 -2.5889e-02  3e-02  7e-17  8e-02\n",
      " 2: -8.4638e-03 -1.4227e-02  6e-03  1e-16  3e-17\n",
      " 3: -1.1040e-02 -1.1470e-02  4e-04  1e-16  4e-18\n",
      " 4: -1.1295e-02 -1.1319e-02  2e-05  1e-16  8e-18\n",
      " 5: -1.1302e-02 -1.1302e-02  6e-07  1e-16  2e-17\n",
      " 6: -1.1302e-02 -1.1302e-02  6e-09  3e-17  1e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  6.3765e-03 -1.0158e+00  1e+00  2e-16  3e+00\n",
      " 1:  5.6076e-03 -2.5563e-02  3e-02  2e-16  8e-02\n",
      " 2: -7.7773e-03 -1.3561e-02  6e-03  5e-16  3e-17\n",
      " 3: -1.0301e-02 -1.0705e-02  4e-04  1e-16  8e-18\n",
      " 4: -1.0545e-02 -1.0555e-02  1e-05  2e-16  7e-18\n",
      " 5: -1.0550e-02 -1.0550e-02  1e-07  3e-17  1e-17\n",
      " 6: -1.0550e-02 -1.0550e-02  1e-09  1e-16  7e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  6.8974e-03 -1.0154e+00  1e+00  2e-16  3e+00\n",
      " 1:  6.0961e-03 -2.5194e-02  3e-02  9e-17  8e-02\n",
      " 2: -7.0426e-03 -1.2827e-02  6e-03  1e-16  2e-17\n",
      " 3: -9.5260e-03 -9.8905e-03  4e-04  1e-16  2e-17\n",
      " 4: -9.7699e-03 -9.7774e-03  8e-06  1e-16  8e-18\n",
      " 5: -9.7762e-03 -9.7763e-03  8e-08  1e-16  3e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  7.4786e-03 -1.0150e+00  1e+00  2e-16  3e+00\n",
      " 1:  6.6405e-03 -2.4778e-02  3e-02  1e-16  8e-02\n",
      " 2: -6.2580e-03 -1.2020e-02  6e-03  1e-16  2e-17\n",
      " 3: -8.7132e-03 -9.0321e-03  3e-04  1e-16  1e-17\n",
      " 4: -8.9634e-03 -8.9699e-03  6e-06  1e-16  2e-17\n",
      " 5: -8.9691e-03 -8.9692e-03  6e-08  1e-16  4e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  8.1265e-03 -1.0146e+00  1e+00  6e-17  3e+00\n",
      " 1:  7.2467e-03 -2.4307e-02  3e-02  1e-16  8e-02\n",
      " 2: -5.4221e-03 -1.1136e-02  6e-03  1e-16  1e-17\n",
      " 3: -7.8184e-03 -8.1854e-03  4e-04  1e-16  2e-17\n",
      " 4: -8.1116e-03 -8.1192e-03  8e-06  6e-17  1e-17\n",
      " 5: -8.1184e-03 -8.1185e-03  8e-08  6e-17  1e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  8.8486e-03 -1.0141e+00  1e+00  1e-16  3e+00\n",
      " 1:  7.9212e-03 -2.3775e-02  3e-02  9e-17  8e-02\n",
      " 2: -4.5331e-03 -1.0171e-02  6e-03  1e-16  3e-17\n",
      " 3: -6.8548e-03 -7.3263e-03  5e-04  1e-16  2e-17\n",
      " 4: -7.1987e-03 -7.2142e-03  2e-05  1e-16  2e-17\n",
      " 5: -7.2126e-03 -7.2128e-03  2e-07  2e-16  3e-17\n",
      " 6: -7.2128e-03 -7.2128e-03  2e-09  2e-16  8e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  9.6527e-03 -1.0135e+00  1e+00  3e-17  3e+00\n",
      " 1:  8.6712e-03 -2.3173e-02  3e-02  9e-17  9e-02\n",
      " 2: -3.5887e-03 -9.1215e-03  6e-03  1e-16  8e-18\n",
      " 3: -5.8673e-03 -6.3829e-03  5e-04  2e-16  2e-17\n",
      " 4: -6.2173e-03 -6.2426e-03  3e-05  6e-17  1e-17\n",
      " 5: -6.2397e-03 -6.2401e-03  4e-07  2e-16  1e-17\n",
      " 6: -6.2401e-03 -6.2401e-03  4e-09  1e-16  8e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.0547e-02 -1.0129e+00  1e+00  6e-17  3e+00\n",
      " 1:  9.5045e-03 -2.2491e-02  3e-02  1e-16  9e-02\n",
      " 2: -2.5865e-03 -7.9849e-03  5e-03  1e-16  3e-17\n",
      " 3: -4.8459e-03 -5.3534e-03  5e-04  1e-16  1e-17\n",
      " 4: -5.1621e-03 -5.1944e-03  3e-05  2e-16  7e-18\n",
      " 5: -5.1850e-03 -5.1877e-03  3e-06  1e-16  3e-17\n",
      " 6: -5.1874e-03 -5.1875e-03  4e-08  1e-16  2e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.1542e-02 -1.0122e+00  1e+00  6e-17  3e+00\n",
      " 1:  1.0429e-02 -2.1718e-02  3e-02  1e-16  9e-02\n",
      " 2: -1.5231e-03 -6.7588e-03  5e-03  7e-17  2e-17\n",
      " 3: -3.7851e-03 -4.2377e-03  5e-04  6e-17  7e-18\n",
      " 4: -4.0418e-03 -4.0695e-03  3e-05  1e-16  1e-17\n",
      " 5: -4.0498e-03 -4.0522e-03  2e-06  1e-16  8e-18\n",
      " 6: -4.0500e-03 -4.0500e-03  4e-08  6e-17  2e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.2647e-02 -1.0114e+00  1e+00  3e-17  3e+00\n",
      " 1:  1.1455e-02 -2.0842e-02  3e-02  7e-17  9e-02\n",
      " 2: -3.9422e-04 -5.4417e-03  5e-03  1e-16  9e-18\n",
      " 3: -2.6746e-03 -3.0420e-03  4e-04  6e-17  1e-17\n",
      " 4: -2.8747e-03 -2.8878e-03  1e-05  1e-16  1e-17\n",
      " 5: -2.8773e-03 -2.8775e-03  2e-07  6e-17  7e-18\n",
      " 6: -2.8774e-03 -2.8774e-03  2e-09  2e-16  8e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.3874e-02 -1.0104e+00  1e+00  1e-16  3e+00\n",
      " 1:  1.2591e-02 -1.9850e-02  3e-02  1e-16  9e-02\n",
      " 2:  8.0522e-04 -4.0322e-03  5e-03  2e-16  1e-17\n",
      " 3: -1.5066e-03 -1.7774e-03  3e-04  2e-16  1e-17\n",
      " 4: -1.6652e-03 -1.6693e-03  4e-06  8e-17  1e-17\n",
      " 5: -1.6670e-03 -1.6670e-03  4e-08  1e-16  8e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.5234e-02 -1.0094e+00  1e+00  2e-16  3e+00\n",
      " 1:  1.3849e-02 -1.8724e-02  3e-02  6e-17  9e-02\n",
      " 2:  2.0817e-03 -2.5295e-03  5e-03  1e-16  2e-17\n",
      " 3: -2.7042e-04 -4.5649e-04  2e-04  1e-16  2e-17\n",
      " 4: -4.0141e-04 -4.0334e-04  2e-06  1e-16  7e-18\n",
      " 5: -4.0277e-04 -4.0279e-04  2e-08  6e-17  1e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.6741e-02 -1.0082e+00  1e+00  0e+00  3e+00\n",
      " 1:  1.5240e-02 -1.7448e-02  3e-02  2e-16  9e-02\n",
      " 2:  3.4432e-03 -9.3251e-04  4e-03  2e-16  3e-17\n",
      " 3:  1.0575e-03  9.0984e-04  1e-04  2e-16  2e-17\n",
      " 4:  9.3334e-04  9.3182e-04  2e-06  2e-16  1e-17\n",
      " 5:  9.3205e-04  9.3203e-04  2e-08  1e-16  1e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.8408e-02 -1.0068e+00  1e+00  8e-17  3e+00\n",
      " 1:  1.6776e-02 -1.6000e-02  3e-02  8e-17  9e-02\n",
      " 2:  4.8991e-03  7.6012e-04  4e-03  6e-17  2e-17\n",
      " 3:  2.5054e-03  2.3311e-03  2e-04  1e-16  2e-17\n",
      " 4:  2.3568e-03  2.3549e-03  2e-06  8e-17  1e-17\n",
      " 5:  2.3552e-03  2.3552e-03  2e-08  2e-16  3e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.0252e-02 -1.0053e+00  1e+00  8e-17  3e+00\n",
      " 1:  1.8471e-02 -1.4359e-02  3e-02  1e-16  9e-02\n",
      " 2:  6.4610e-03  2.5506e-03  4e-03  8e-17  3e-17\n",
      " 3:  4.0367e-03  3.8669e-03  2e-04  2e-16  8e-18\n",
      " 4:  3.8872e-03  3.8853e-03  2e-06  2e-16  5e-18\n",
      " 5:  3.8855e-03  3.8855e-03  2e-08  6e-17  9e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.2287e-02 -1.0035e+00  1e+00  1e-16  3e+00\n",
      " 1:  2.0339e-02 -1.2498e-02  3e-02  2e-16  9e-02\n",
      " 2:  8.1430e-03  4.4428e-03  4e-03  1e-16  3e-17\n",
      " 3:  5.6832e-03  5.5285e-03  2e-04  2e-16  2e-17\n",
      " 4:  5.5449e-03  5.5432e-03  2e-06  8e-17  2e-17\n",
      " 5:  5.5434e-03  5.5434e-03  2e-08  2e-16  2e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.4533e-02 -1.0016e+00  1e+00  3e-17  3e+00\n",
      " 1:  2.2397e-02 -1.0388e-02  3e-02  1e-16  9e-02\n",
      " 2:  9.9622e-03  6.4430e-03  4e-03  4e-16  3e-17\n",
      " 3:  7.4934e-03  7.3343e-03  2e-04  2e-16  2e-17\n",
      " 4:  7.3524e-03  7.3506e-03  2e-06  8e-17  2e-17\n",
      " 5:  7.3508e-03  7.3507e-03  2e-08  2e-16  2e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.7008e-02 -9.9930e-01  1e+00  8e-17  3e+00\n",
      " 1:  2.4661e-02 -7.9997e-03  3e-02  1e-16  9e-02\n",
      " 2:  1.2018e-02  8.4524e-03  4e-03  1e-16  7e-04\n",
      " 3:  9.4930e-03  9.3124e-03  2e-04  8e-17  2e-17\n",
      " 4:  9.3337e-03  9.3314e-03  2e-06  2e-16  1e-17\n",
      " 5:  9.3316e-03  9.3316e-03  2e-08  2e-16  2e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.9732e-02 -9.9674e-01  1e+00  6e-17  3e+00\n",
      " 1:  2.7151e-02 -5.2963e-03  3e-02  8e-17  9e-02\n",
      " 2:  1.4241e-02  1.0636e-02  4e-03  2e-16  1e-03\n",
      " 3:  1.1690e-02  1.1492e-02  2e-04  2e-16  3e-17\n",
      " 4:  1.1515e-02  1.1512e-02  3e-06  1e-16  1e-17\n",
      " 5:  1.1512e-02  1.1512e-02  3e-08  2e-16  8e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  3.2728e-02 -9.9382e-01  1e+00  0e+00  3e+00\n",
      " 1:  2.9887e-02 -2.2624e-03  3e-02  6e-17  9e-02\n",
      " 2:  1.6495e-02  1.3028e-02  3e-03  2e-16  7e-17\n",
      " 3:  1.4097e-02  1.3904e-02  2e-04  1e-16  3e-17\n",
      " 4:  1.3924e-02  1.3921e-02  3e-06  1e-16  1e-17\n",
      " 5:  1.3922e-02  1.3922e-02  3e-08  1e-16  1e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  3.6020e-02 -9.9267e-01  1e+00  3e-16  3e+00\n",
      " 1:  3.2902e-02 -9.8424e-04  3e-02  2e-16  9e-02\n",
      " 2:  1.9238e-02  1.5505e-02  4e-03  7e-17  2e-17\n",
      " 3:  1.6786e-02  1.6572e-02  2e-04  2e-16  2e-17\n",
      " 4:  1.6596e-02  1.6591e-02  4e-06  8e-17  3e-17\n",
      " 5:  1.6592e-02  1.6592e-02  4e-08  1e-16  2e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  3.9632e-02 -9.9140e-01  1e+00  6e-17  3e+00\n",
      " 1:  3.6214e-02  5.2879e-04  4e-02  7e-17  1e-01\n",
      " 2:  2.2265e-02  1.8263e-02  4e-03  2e-16  3e-17\n",
      " 3:  1.9769e-02  1.9536e-02  2e-04  1e-16  2e-17\n",
      " 4:  1.9563e-02  1.9558e-02  6e-06  8e-17  1e-17\n",
      " 5:  1.9558e-02  1.9558e-02  6e-08  1e-16  1e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  4.3594e-02 -9.8988e-01  1e+00  3e-17  3e+00\n",
      " 1:  3.9851e-02  2.3157e-03  4e-02  7e-17  1e-01\n",
      " 2:  2.5614e-02  2.1353e-02  4e-03  2e-16  4e-17\n",
      " 3:  2.3094e-02  2.2829e-02  3e-04  2e-16  3e-17\n",
      " 4:  2.2867e-02  2.2859e-02  8e-06  1e-16  2e-17\n",
      " 5:  2.2859e-02  2.2859e-02  8e-08  2e-16  3e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  4.7936e-02 -9.8809e-01  1e+00  3e-17  3e+00\n",
      " 1:  4.3845e-02  4.4200e-03  4e-02  1e-16  1e-01\n",
      " 2:  2.9327e-02  2.4827e-02  5e-03  6e-17  6e-17\n",
      " 3:  2.6792e-02  2.6502e-02  3e-04  8e-17  2e-17\n",
      " 4:  2.6551e-02  2.6540e-02  1e-05  8e-17  4e-17\n",
      " 5:  2.6540e-02  2.6540e-02  1e-07  1e-16  3e-17\n",
      " 6:  2.6540e-02  2.6540e-02  1e-09  8e-17  4e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  5.2690e-02 -9.8596e-01  1e+00  6e-17  3e+00\n",
      " 1:  4.8229e-02  6.8911e-03  4e-02  1e-16  1e-01\n",
      " 2:  3.3450e-02  2.8744e-02  5e-03  1e-16  3e-17\n",
      " 3:  3.0911e-02  3.0602e-02  3e-04  1e-16  3e-17\n",
      " 4:  3.0664e-02  3.0649e-02  1e-05  8e-17  4e-17\n",
      " 5:  3.0649e-02  3.0649e-02  2e-07  1e-16  2e-17\n",
      " 6:  3.0649e-02  3.0649e-02  2e-09  6e-17  2e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  5.7894e-02 -9.8345e-01  1e+00  6e-17  3e+00\n",
      " 1:  5.3045e-02  9.7852e-03  4e-02  6e-17  1e-01\n",
      " 2:  3.8036e-02  3.3165e-02  5e-03  2e-16  3e-17\n",
      " 3:  3.5507e-02  3.5184e-02  3e-04  8e-17  4e-17\n",
      " 4:  3.5259e-02  3.5240e-02  2e-05  2e-16  2e-17\n",
      " 5:  3.5241e-02  3.5241e-02  2e-07  8e-17  5e-17\n",
      " 6:  3.5241e-02  3.5241e-02  2e-09  5e-22  3e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  6.3588e-02 -9.8051e-01  1e+00  2e-16  3e+00\n",
      " 1:  5.8336e-02  1.3166e-02  5e-02  1e-16  1e-01\n",
      " 2:  4.3142e-02  3.8158e-02  5e-03  1e-16  4e-17\n",
      " 3:  4.0642e-02  4.0309e-02  3e-04  2e-16  4e-17\n",
      " 4:  4.0398e-02  4.0375e-02  2e-05  1e-16  2e-17\n",
      " 5:  4.0377e-02  4.0376e-02  3e-07  1e-16  2e-17\n",
      " 6:  4.0376e-02  4.0376e-02  3e-09  1e-16  2e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  6.9815e-02 -9.7707e-01  1e+00  6e-17  3e+00\n",
      " 1:  6.4155e-02  1.7107e-02  5e-02  8e-17  1e-01\n",
      " 2:  4.8834e-02  4.3792e-02  5e-03  8e-17  7e-17\n",
      " 3:  4.6382e-02  4.6045e-02  3e-04  1e-16  4e-17\n",
      " 4:  4.6149e-02  4.6121e-02  3e-05  8e-17  3e-17\n",
      " 5:  4.6124e-02  4.6123e-02  5e-07  2e-16  4e-17\n",
      " 6:  4.6123e-02  4.6123e-02  5e-09  6e-17  3e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  7.6627e-02 -9.7306e-01  1e+00  6e-17  3e+00\n",
      " 1:  7.0557e-02  2.1690e-02  5e-02  1e-16  1e-01\n",
      " 2:  5.5186e-02  5.0145e-02  5e-03  2e-16  8e-17\n",
      " 3:  5.2806e-02  5.2468e-02  3e-04  2e-16  3e-17\n",
      " 4:  5.2587e-02  5.2554e-02  3e-05  1e-16  4e-17\n",
      " 5:  5.2559e-02  5.2558e-02  1e-06  1e-16  2e-17\n",
      " 6:  5.2558e-02  5.2558e-02  1e-08  1e-16  2e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  8.4079e-02 -9.6840e-01  1e+00  6e-17  3e+00\n",
      " 1:  7.7611e-02  2.7009e-02  5e-02  1e-16  1e-01\n",
      " 2:  6.2282e-02  5.7302e-02  5e-03  8e-17  7e-17\n",
      " 3:  5.9997e-02  5.9663e-02  3e-04  1e-16  3e-17\n",
      " 4:  5.9797e-02  5.9761e-02  4e-05  1e-16  3e-17\n",
      " 5:  5.9769e-02  5.9766e-02  2e-06  1e-16  6e-17\n",
      " 6:  5.9767e-02  5.9767e-02  2e-08  1e-16  6e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  9.2234e-02 -9.6300e-01  1e+00  3e-16  3e+00\n",
      " 1:  8.5391e-02  3.3171e-02  5e-02  1e-16  1e-01\n",
      " 2:  7.0215e-02  6.5356e-02  5e-03  1e-16  9e-17\n",
      " 3:  6.8052e-02  6.7725e-02  3e-04  1e-16  4e-17\n",
      " 4:  6.7874e-02  6.7834e-02  4e-05  8e-17  7e-17\n",
      " 5:  6.7847e-02  6.7844e-02  4e-06  8e-17  6e-17\n",
      " 6:  6.7844e-02  6.7844e-02  6e-08  1e-16  3e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.0117e-01 -9.5675e-01  1e+00  2e-16  3e+00\n",
      " 1:  9.3984e-02  4.0295e-02  5e-02  2e-16  1e-01\n",
      " 2:  7.9094e-02  7.4409e-02  5e-03  4e-17  1e-16\n",
      " 3:  7.7079e-02  7.6762e-02  3e-04  1e-16  7e-17\n",
      " 4:  7.6923e-02  7.6883e-02  4e-05  8e-17  5e-17\n",
      " 5:  7.6902e-02  7.6896e-02  5e-06  6e-17  4e-17\n",
      " 6:  7.6898e-02  7.6897e-02  4e-07  1e-16  6e-17\n",
      " 7:  7.6898e-02  7.6898e-02  6e-09  1e-16  3e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.1095e-01 -9.4956e-01  1e+00  2e-16  3e+00\n",
      " 1:  1.0349e-01  4.8519e-02  5e-02  1e-16  1e-01\n",
      " 2:  8.9038e-02  8.4578e-02  4e-03  2e-16  9e-17\n",
      " 3:  8.7197e-02  8.6893e-02  3e-04  8e-17  5e-17\n",
      " 4:  8.7065e-02  8.7025e-02  4e-05  8e-17  3e-17\n",
      " 5:  8.7049e-02  8.7044e-02  6e-06  2e-16  1e-16\n",
      " 6:  8.7047e-02  8.7047e-02  8e-07  8e-17  6e-17\n",
      " 7:  8.7047e-02  8.7047e-02  9e-08  2e-16  9e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.2170e-01 -9.4129e-01  1e+00  6e-17  3e+00\n",
      " 1:  1.1402e-01  5.7994e-02  6e-02  8e-17  2e-01\n",
      " 2:  1.0019e-01  9.5991e-02  4e-03  1e-16  5e-17\n",
      " 3:  9.8541e-02  9.8252e-02  3e-04  8e-17  3e-17\n",
      " 4:  9.8432e-02  9.8394e-02  4e-05  2e-16  4e-17\n",
      " 5:  9.8422e-02  9.8418e-02  4e-06  2e-16  6e-17\n",
      " 6:  9.8422e-02  9.8422e-02  2e-07  8e-17  1e-16\n",
      " 7:  9.8422e-02  9.8422e-02  2e-09  3e-17  6e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.3350e-01 -9.3181e-01  1e+00  1e-16  3e+00\n",
      " 1:  1.2572e-01  6.8892e-02  6e-02  8e-17  2e-01\n",
      " 2:  1.1269e-01  1.0880e-01  4e-03  1e-16  5e-17\n",
      " 3:  1.1126e-01  1.1099e-01  3e-04  1e-16  9e-17\n",
      " 4:  1.1117e-01  1.1114e-01  3e-05  8e-17  7e-17\n",
      " 5:  1.1117e-01  1.1117e-01  3e-06  6e-17  4e-17\n",
      " 6:  1.1117e-01  1.1117e-01  3e-08  6e-17  5e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.4650e-01 -9.2096e-01  1e+00  1e-16  3e+00\n",
      " 1:  1.3872e-01  8.1405e-02  6e-02  8e-17  2e-01\n",
      " 2:  1.2674e-01  1.2315e-01  4e-03  6e-17  2e-16\n",
      " 3:  1.2553e-01  1.2528e-01  3e-04  8e-17  2e-16\n",
      " 4:  1.2546e-01  1.2543e-01  3e-05  6e-17  8e-17\n",
      " 5:  1.2546e-01  1.2546e-01  1e-06  2e-16  1e-16\n",
      " 6:  1.2546e-01  1.2546e-01  1e-08  8e-17  1e-16\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.6083e-01 -9.0857e-01  1e+00  0e+00  3e+00\n",
      " 1:  1.5322e-01  9.5748e-02  6e-02  8e-17  2e-01\n",
      " 2:  1.4253e-01  1.3921e-01  3e-03  6e-17  2e-04\n",
      " 3:  1.4153e-01  1.4130e-01  2e-04  6e-17  1e-06\n",
      " 4:  1.4148e-01  1.4146e-01  2e-05  2e-16  1e-16\n",
      " 5:  1.4148e-01  1.4148e-01  4e-07  8e-17  6e-17\n",
      " 6:  1.4148e-01  1.4148e-01  4e-09  1e-16  9e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.7667e-01 -8.9446e-01  1e+00  1e-16  3e+00\n",
      " 1:  1.6940e-01  1.1216e-01  6e-02  6e-17  2e-01\n",
      " 2:  1.6028e-01  1.5717e-01  3e-03  1e-16  5e-04\n",
      " 3:  1.5948e-01  1.5926e-01  2e-04  8e-17  4e-06\n",
      " 4:  1.5944e-01  1.5943e-01  2e-05  6e-17  7e-17\n",
      " 5:  1.5944e-01  1.5944e-01  2e-07  1e-16  6e-17\n",
      " 6:  1.5944e-01  1.5944e-01  2e-09  1e-16  2e-16\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.9421e-01 -8.7840e-01  1e+00  1e-16  3e+00\n",
      " 1:  1.8751e-01  1.3089e-01  6e-02  1e-16  2e-01\n",
      " 2:  1.8024e-01  1.7730e-01  3e-03  2e-16  8e-04\n",
      " 3:  1.7962e-01  1.7941e-01  2e-04  8e-17  6e-06\n",
      " 4:  1.7959e-01  1.7958e-01  1e-05  2e-16  2e-16\n",
      " 5:  1.7959e-01  1.7959e-01  1e-07  1e-16  2e-16\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.1368e-01 -8.6017e-01  1e+00  2e-16  3e+00\n",
      " 1:  2.0780e-01  1.5225e-01  6e-02  1e-16  2e-01\n",
      " 2:  2.0268e-01  1.9989e-01  3e-03  6e-17  1e-03\n",
      " 3:  2.0221e-01  2.0202e-01  2e-04  4e-17  9e-06\n",
      " 4:  2.0219e-01  2.0218e-01  8e-06  6e-17  1e-16\n",
      " 5:  2.0219e-01  2.0219e-01  8e-08  1e-16  2e-16\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.3533e-01 -8.3950e-01  7e+00  2e+00  3e+00\n",
      " 1:  2.9092e-01 -4.6163e-01  8e-01  4e-16  4e-16\n",
      " 2:  2.5255e-01  1.7481e-01  8e-02  2e-16  4e-16\n",
      " 3:  2.2985e-01  2.2436e-01  5e-03  4e-17  1e-16\n",
      " 4:  2.2760e-01  2.2725e-01  3e-04  1e-16  1e-16\n",
      " 5:  2.2753e-01  2.2752e-01  2e-05  1e-16  2e-16\n",
      " 6:  2.2753e-01  2.2753e-01  2e-07  3e-17  5e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.5946e-01 -8.1609e-01  7e+00  2e+00  3e+00\n",
      " 1:  3.1882e-01 -4.3346e-01  8e-01  3e-16  3e-16\n",
      " 2:  2.7941e-01  2.0331e-01  8e-02  2e-16  5e-16\n",
      " 3:  2.5798e-01  2.5292e-01  5e-03  1e-16  4e-16\n",
      " 4:  2.5601e-01  2.5570e-01  3e-04  6e-17  3e-16\n",
      " 5:  2.5597e-01  2.5595e-01  1e-05  1e-16  2e-16\n",
      " 6:  2.5597e-01  2.5597e-01  1e-07  1e-16  1e-16\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.8639e-01 -7.8962e-01  7e+00  2e+00  3e+00\n",
      " 1:  3.4941e-01 -4.0152e-01  8e-01  4e-16  4e-16\n",
      " 2:  3.0944e-01  2.3568e-01  7e-02  6e-17  4e-16\n",
      " 3:  2.8957e-01  2.8496e-01  5e-03  8e-17  3e-16\n",
      " 4:  2.8790e-01  2.8761e-01  3e-04  6e-17  1e-16\n",
      " 5:  2.8786e-01  2.8785e-01  9e-06  6e-17  3e-16\n",
      " 6:  2.8786e-01  2.8786e-01  9e-08  2e-16  7e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  3.1652e-01 -7.5971e-01  7e+00  2e+00  3e+00\n",
      " 1:  3.8299e-01 -3.6539e-01  7e-01  4e-16  5e-16\n",
      " 2:  3.4308e-01  2.7235e-01  7e-02  2e-16  5e-16\n",
      " 3:  3.2503e-01  3.2089e-01  4e-03  3e-17  9e-17\n",
      " 4:  3.2367e-01  3.2342e-01  2e-04  1e-16  2e-16\n",
      " 5:  3.2364e-01  3.2364e-01  6e-06  1e-16  1e-16\n",
      " 6:  3.2364e-01  3.2364e-01  6e-08  1e-16  2e-16\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  3.5026e-01 -7.2595e-01  8e+00  2e+00  3e+00\n",
      " 1:  4.1989e-01 -3.2462e-01  7e-01  2e-16  5e-16\n",
      " 2:  3.8082e-01  3.1375e-01  7e-02  1e-16  4e-16\n",
      " 3:  3.6488e-01  3.6119e-01  4e-03  1e-16  5e-16\n",
      " 4:  3.6381e-01  3.6359e-01  2e-04  6e-17  2e-16\n",
      " 5:  3.6379e-01  3.6378e-01  4e-06  1e-16  1e-16\n",
      " 6:  3.6379e-01  3.6379e-01  4e-08  2e-16  2e-16\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  3.8810e-01 -6.8789e-01  8e+00  2e+00  3e+00\n",
      " 1:  4.6053e-01 -2.7873e-01  7e-01  5e-16  3e-16\n",
      " 2:  4.2322e-01  3.6036e-01  6e-02  8e-17  4e-16\n",
      " 3:  4.0964e-01  4.0639e-01  3e-03  8e-17  2e-16\n",
      " 4:  4.0884e-01  4.0866e-01  2e-04  1e-16  2e-16\n",
      " 5:  4.0883e-01  4.0882e-01  3e-06  8e-17  2e-16\n",
      " 6:  4.0883e-01  4.0883e-01  3e-08  1e-16  3e-16\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  4.3057e-01 -6.4499e-01  8e+00  2e+00  3e+00\n",
      " 1:  5.0538e-01 -2.2733e-01  7e-01  6e-16  5e-16\n",
      " 2:  4.7094e-01  4.1271e-01  6e-02  1e-16  3e-16\n",
      " 3:  4.5994e-01  4.5709e-01  3e-03  8e-17  4e-16\n",
      " 4:  4.5936e-01  4.5921e-01  1e-04  2e-16  1e-16\n",
      " 5:  4.5935e-01  4.5935e-01  2e-06  1e-16  3e-16\n",
      " 6:  4.5935e-01  4.5935e-01  2e-08  1e-16  2e-16\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  4.7827e-01 -5.9668e-01  8e+00  2e+00  3e+00\n",
      " 1:  5.5499e-01 -1.7002e-01  7e-01  4e-16  3e-16\n",
      " 2:  5.2472e-01  4.7137e-01  5e-02  8e-17  2e-16\n",
      " 3:  5.1644e-01  5.1394e-01  2e-03  1e-16  7e-16\n",
      " 4:  5.1605e-01  5.1593e-01  1e-04  1e-16  3e-16\n",
      " 5:  5.1605e-01  5.1604e-01  1e-06  1e-16  2e-16\n",
      " 6:  5.1605e-01  5.1605e-01  1e-08  6e-17  4e-16\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  5.3189e-01 -5.4231e-01  8e+00  2e+00  3e+00\n",
      " 1:  6.0997e-01 -1.0591e-01  7e-01  1e-05  2e-05\n",
      " 2:  5.8543e-01  5.3704e-01  5e-02  6e-07  9e-07\n",
      " 3:  5.7990e-01  5.7771e-01  2e-03  1e-08  2e-08\n",
      " 4:  5.7965e-01  5.7956e-01  1e-04  1e-10  1e-10\n",
      " 5:  5.7965e-01  5.7965e-01  1e-06  1e-12  1e-12\n",
      " 6:  5.7965e-01  5.7965e-01  1e-08  1e-14  1e-14\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  5.9219e-01 -4.8114e-01  8e+00  2e+00  3e+00\n",
      " 1:  6.6991e-01 -3.8916e-02  8e-01  3e-02  4e-02\n",
      " 2:  6.5681e-01  6.0379e-01  5e-02  2e-03  2e-03\n",
      " 3:  6.5131e-01  6.4871e-01  3e-03  3e-05  4e-05\n",
      " 4:  6.5102e-01  6.5089e-01  1e-04  2e-07  3e-07\n",
      " 5:  6.5101e-01  6.5101e-01  1e-06  2e-09  3e-09\n",
      " 6:  6.5101e-01  6.5101e-01  1e-08  2e-11  3e-11\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  6.5999e-01 -4.1236e-01  8e+00  2e+00  3e+00\n",
      " 1:  7.3678e-01  3.6181e-02  8e-01  5e-02  7e-02\n",
      " 2:  7.3666e-01  6.7975e-01  6e-02  3e-03  4e-03\n",
      " 3:  7.3142e-01  7.2844e-01  3e-03  5e-05  7e-05\n",
      " 4:  7.3109e-01  7.3094e-01  1e-04  4e-07  6e-07\n",
      " 5:  7.3108e-01  7.3108e-01  2e-06  4e-09  6e-09\n",
      " 6:  7.3108e-01  7.3108e-01  2e-08  4e-11  6e-11\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  7.3626e-01 -3.3505e-01  8e+00  2e+00  3e+00\n",
      " 1:  8.1156e-01  1.2022e-01  9e-01  8e-02  1e-01\n",
      " 2:  8.2636e-01  7.6586e-01  6e-02  4e-03  5e-03\n",
      " 3:  8.2128e-01  8.1797e-01  3e-03  7e-05  9e-05\n",
      " 4:  8.2092e-01  8.2075e-01  2e-04  5e-07  7e-07\n",
      " 5:  8.2092e-01  8.2092e-01  2e-06  5e-09  7e-09\n",
      " 6:  8.2092e-01  8.2092e-01  2e-08  5e-11  7e-11\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  8.2204e-01 -2.4816e-01  8e+00  3e+00  3e+00\n",
      " 1:  8.9535e-01  2.1416e-01  9e-01  1e-01  1e-01\n",
      " 2:  9.2728e-01  8.6324e-01  7e-02  4e-03  6e-03\n",
      " 3:  9.2210e-01  9.1850e-01  4e-03  7e-05  1e-04\n",
      " 4:  9.2172e-01  9.2154e-01  2e-04  5e-07  7e-07\n",
      " 5:  9.2172e-01  9.2171e-01  2e-06  5e-09  7e-09\n",
      " 6:  9.2172e-01  9.2172e-01  2e-08  5e-11  7e-11\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  9.1851e-01 -1.5055e-01  8e+00  3e+00  3e+00\n",
      " 1:  9.8939e-01  3.1905e-01  1e+00  1e-01  2e-01\n",
      " 2:  1.0409e+00  9.7318e-01  7e-02  4e-03  5e-03\n",
      " 3:  1.0352e+00  1.0314e+00  4e-03  6e-05  9e-05\n",
      " 4:  1.0348e+00  1.0346e+00  2e-04  5e-07  6e-07\n",
      " 5:  1.0348e+00  1.0348e+00  2e-06  4e-09  6e-09\n",
      " 6:  1.0348e+00  1.0348e+00  2e-08  4e-11  6e-11\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.0270e+00 -4.0915e-02  9e+00  3e+00  3e+00\n",
      " 1:  1.0951e+00  4.3612e-01  1e+00  2e-01  2e-01\n",
      " 2:  1.1688e+00  1.0972e+00  7e-02  2e-03  3e-03\n",
      " 3:  1.1621e+00  1.1581e+00  4e-03  4e-05  6e-05\n",
      " 4:  1.1617e+00  1.1615e+00  2e-04  3e-07  4e-07\n",
      " 5:  1.1617e+00  1.1617e+00  2e-06  3e-09  4e-09\n",
      " 6:  1.1617e+00  1.1617e+00  2e-08  3e-11  4e-11\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.1490e+00  8.2207e-02  9e+00  3e+00  3e+00\n",
      " 1:  1.2140e+00  5.6671e-01  1e+00  2e-01  2e-01\n",
      " 2:  1.3149e+00  1.2355e+00  8e-02  2e-16  3e-15\n",
      " 3:  1.3045e+00  1.3004e+00  4e-03  1e-16  2e-15\n",
      " 4:  1.3041e+00  1.3039e+00  2e-04  8e-17  8e-16\n",
      " 5:  1.3041e+00  1.3041e+00  2e-06  2e-16  8e-16\n",
      " 6:  1.3041e+00  1.3041e+00  2e-08  1e-16  1e-15\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.2861e+00  2.2045e-01  9e+00  3e+00  3e+00\n",
      " 1:  1.3477e+00  7.1240e-01  1e+00  2e-01  3e-01\n",
      " 2:  1.4785e+00  1.3786e+00  1e-01  2e-16  1e-15\n",
      " 3:  1.4644e+00  1.4597e+00  5e-03  1e-16  2e-15\n",
      " 4:  1.4638e+00  1.4636e+00  2e-04  6e-17  5e-16\n",
      " 5:  1.4638e+00  1.4638e+00  2e-06  1e-16  1e-15\n",
      " 6:  1.4638e+00  1.4638e+00  2e-08  2e-16  1e-15\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.4402e+00  3.7565e-01  9e+00  3e+00  3e+00\n",
      " 1:  1.4984e+00  8.7494e-01  1e+00  2e-01  3e-01\n",
      " 2:  1.6637e+00  1.5393e+00  1e-01  1e-16  1e-15\n",
      " 3:  1.6438e+00  1.6385e+00  5e-03  1e-16  2e-15\n",
      " 4:  1.6431e+00  1.6429e+00  2e-04  8e-17  1e-15\n",
      " 5:  1.6431e+00  1.6431e+00  2e-06  1e-16  6e-16\n",
      " 6:  1.6431e+00  1.6431e+00  2e-08  4e-17  6e-16\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.6133e+00  5.4988e-01  9e+00  3e+00  3e+00\n",
      " 1:  1.6680e+00  1.0563e+00  1e+00  2e-01  3e-01\n",
      " 2:  1.8737e+00  1.7202e+00  2e-01  2e-16  4e-15\n",
      " 3:  1.8452e+00  1.8393e+00  6e-03  1e-16  2e-15\n",
      " 4:  1.8442e+00  1.8440e+00  2e-04  6e-17  1e-15\n",
      " 5:  1.8442e+00  1.8442e+00  2e-06  1e-16  6e-16\n",
      " 6:  1.8442e+00  1.8442e+00  2e-08  2e-16  1e-15\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.8078e+00  7.4543e-01  9e+00  3e+00  3e+00\n",
      " 1:  1.8589e+00  1.2588e+00  1e+00  3e-01  3e-01\n",
      " 2:  2.1123e+00  1.9239e+00  2e-01  2e-16  2e-15\n",
      " 3:  2.0713e+00  2.0645e+00  7e-03  1e-16  1e-15\n",
      " 4:  2.0698e+00  2.0696e+00  2e-04  3e-17  2e-15\n",
      " 5:  2.0698e+00  2.0698e+00  2e-06  1e-16  7e-16\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.0264e+00  9.6492e-01  9e+00  3e+00  3e+00\n",
      " 1:  2.0739e+00  1.4849e+00  1e+00  3e-01  4e-01\n",
      " 2:  2.3841e+00  2.1531e+00  2e-01  1e-16  2e-15\n",
      " 3:  2.3253e+00  2.3174e+00  8e-03  1e-16  2e-15\n",
      " 4:  2.3230e+00  2.3228e+00  2e-04  4e-17  1e-15\n",
      " 5:  2.3230e+00  2.3230e+00  2e-06  8e-17  2e-15\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.2718e+00  1.2112e+00  9e+00  3e+00  3e+00\n",
      " 1:  2.3158e+00  1.7376e+00  1e+00  3e-01  4e-01\n",
      " 2:  2.6942e+00  2.4111e+00  3e-01  1e-16  6e-15\n",
      " 3:  2.6108e+00  2.6012e+00  1e-02  4e-17  1e-15\n",
      " 4:  2.6071e+00  2.6069e+00  2e-04  6e-17  5e-16\n",
      " 5:  2.6070e+00  2.6070e+00  2e-06  6e-17  1e-15\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.5473e+00  1.4877e+00  9e+00  3e+00  3e+00\n",
      " 1:  2.5880e+00  2.0201e+00  1e+00  3e-01  4e-01\n",
      " 2:  3.0489e+00  2.7011e+00  3e-01  1e-16  3e-15\n",
      " 3:  2.9317e+00  2.9198e+00  1e-02  1e-16  1e-15\n",
      " 4:  2.9258e+00  2.9256e+00  2e-04  8e-17  2e-15\n",
      " 5:  2.9258e+00  2.9258e+00  2e-06  1e-16  2e-15\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.8568e+00  1.7979e+00  9e+00  3e+00  4e+00\n",
      " 1:  2.8941e+00  2.3359e+00  1e+00  3e-01  4e-01\n",
      " 2:  3.4558e+00  3.0268e+00  4e-01  1e-16  2e-14\n",
      " 3:  3.2929e+00  3.2773e+00  2e-02  2e-16  2e-15\n",
      " 4:  3.2835e+00  3.2833e+00  2e-04  8e-17  2e-15\n",
      " 5:  3.2834e+00  3.2834e+00  2e-06  1e-16  2e-15\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  3.2041e+00  2.1460e+00  9e+00  3e+00  4e+00\n",
      " 1:  3.2384e+00  2.6893e+00  1e+00  3e-01  4e-01\n",
      " 2:  3.9239e+00  3.3918e+00  5e-01  1e-16  8e-15\n",
      " 3:  3.6997e+00  3.6786e+00  2e-02  1e-16  2e-15\n",
      " 4:  3.6848e+00  3.6845e+00  3e-04  4e-17  2e-15\n",
      " 5:  3.6846e+00  3.6846e+00  3e-06  1e-16  2e-15\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  3.5941e+00  2.5367e+00  9e+00  3e+00  4e+00\n",
      " 1:  3.6254e+00  3.0849e+00  1e+00  3e-01  5e-01\n",
      " 2:  4.4641e+00  3.7998e+00  7e-01  2e-16  6e-15\n",
      " 3:  4.1583e+00  4.1289e+00  3e-02  2e-16  4e-15\n",
      " 4:  4.1351e+00  4.1348e+00  3e-04  2e-16  3e-15\n",
      " 5:  4.1348e+00  4.1348e+00  3e-06  1e-16  1e-15\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  4.0318e+00  2.9750e+00  9e+00  3e+00  4e+00\n",
      " 1:  4.0603e+00  3.5278e+00  1e+00  3e-01  5e-01\n",
      " 2:  5.0899e+00  4.2543e+00  8e-01  2e-16  5e-15\n",
      " 3:  4.6763e+00  4.6333e+00  4e-02  1e-16  5e-15\n",
      " 4:  4.6404e+00  4.6399e+00  5e-04  2e-16  5e-15\n",
      " 5:  4.6400e+00  4.6400e+00  5e-06  2e-16  2e-15\n",
      " 6:  4.6400e+00  4.6400e+00  5e-08  1e-16  3e-15\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  4.5230e+00  3.4669e+00  9e+00  3e+00  4e+00\n",
      " 1:  4.5490e+00  4.0238e+00  2e+00  4e-01  5e-01\n",
      " 2:  5.8181e+00  4.7583e+00  1e+00  8e-17  2e-15\n",
      " 3:  5.2614e+00  5.1991e+00  6e-02  1e-16  2e-15\n",
      " 4:  5.2075e+00  5.2067e+00  8e-04  1e-16  5e-15\n",
      " 5:  5.2068e+00  5.2068e+00  8e-06  2e-16  4e-15\n",
      " 6:  5.2068e+00  5.2068e+00  8e-08  8e-17  2e-15\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  5.0744e+00  4.0188e+00  9e+00  3e+00  4e+00\n",
      " 1:  5.0980e+00  4.5796e+00  2e+00  4e-01  5e-01\n",
      " 2:  6.6692e+00  5.3138e+00  1e+00  2e-16  1e-14\n",
      " 3:  5.9222e+00  5.8335e+00  9e-02  8e-17  5e-15\n",
      " 4:  5.8439e+00  5.8426e+00  1e-03  6e-17  6e-15\n",
      " 5:  5.8427e+00  5.8427e+00  1e-05  9e-17  3e-15\n",
      " 6:  5.8427e+00  5.8427e+00  1e-07  1e-16  4e-15\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  5.6932e+00  4.6380e+00  9e+00  3e+00  4e+00\n",
      " 1:  5.7146e+00  5.2024e+00  2e+00  4e-01  5e-01\n",
      " 2:  7.6689e+00  5.9217e+00  2e+00  1e-16  2e-14\n",
      " 3:  6.6683e+00  6.5445e+00  1e-01  4e-17  3e-15\n",
      " 4:  6.5576e+00  6.5561e+00  1e-03  1e-16  7e-15\n",
      " 5:  6.5563e+00  6.5562e+00  1e-05  1e-16  6e-15\n",
      " 6:  6.5562e+00  6.5562e+00  1e-07  3e-17  2e-15\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  6.3876e+00  5.3329e+00  9e+00  3e+00  4e+00\n",
      " 1:  6.4069e+00  5.9005e+00  2e+00  4e-01  5e-01\n",
      " 2:  8.8493e+00  6.5802e+00  2e+00  1e-16  2e-14\n",
      " 3:  7.5210e+00  7.3350e+00  2e-01  1e-16  5e-15\n",
      " 4:  7.3590e+00  7.3566e+00  2e-03  1e-16  6e-15\n",
      " 5:  7.3569e+00  7.3568e+00  2e-05  1e-16  5e-15\n",
      " 6:  7.3568e+00  7.3568e+00  2e-07  6e-17  2e-15\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  7.1668e+00  6.1126e+00  9e+00  3e+00  4e+00\n",
      " 1:  7.1843e+00  6.6832e+00  2e+00  4e-01  5e-01\n",
      " 2:  1.0250e+01  7.2849e+00  3e+00  2e-16  6e-15\n",
      " 3:  8.4917e+00  8.2154e+00  3e-01  6e-17  4e-15\n",
      " 4:  8.2591e+00  8.2548e+00  4e-03  6e-17  2e-15\n",
      " 5:  8.2552e+00  8.2551e+00  4e-05  3e-16  6e-15\n",
      " 6:  8.2551e+00  8.2551e+00  4e-07  3e-17  6e-15\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  8.0413e+00  6.9874e+00  9e+00  3e+00  4e+00\n",
      " 1:  8.0570e+00  7.5607e+00  2e+00  4e-01  5e-01\n",
      " 2:  1.1921e+01  8.0268e+00  4e+00  1e-16  2e-14\n",
      " 3:  9.5984e+00  9.1780e+00  4e-01  1e-16  2e-14\n",
      " 4:  9.2730e+00  9.2622e+00  1e-02  1e-16  9e-15\n",
      " 5:  9.2631e+00  9.2630e+00  1e-04  3e-17  7e-15\n",
      " 6:  9.2630e+00  9.2630e+00  1e-06  2e-16  1e-14\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  9.0225e+00  7.9690e+00  9e+00  3e+00  4e+00\n",
      " 1:  9.0367e+00  8.5447e+00  2e+00  4e-01  5e-01\n",
      " 2:  1.3921e+01  8.7921e+00  5e+00  2e-16  5e-14\n",
      " 3:  1.0865e+01  1.0154e+01  7e-01  1e-16  5e-15\n",
      " 4:  1.0412e+01  1.0389e+01  2e-02  1e-16  6e-15\n",
      " 5:  1.0394e+01  1.0394e+01  2e-04  2e-16  7e-15\n",
      " 6:  1.0394e+01  1.0394e+01  2e-06  1e-16  1e-14\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.0124e+01  9.0703e+00  9e+00  3e+00  4e+00\n",
      " 1:  1.0136e+01  9.6482e+00  2e+00  4e-01  5e-01\n",
      " 2:  1.6323e+01  9.5604e+00  7e+00  2e-16  2e-14\n",
      " 3:  1.2380e+01  1.0938e+01  1e+00  7e-17  7e-15\n",
      " 4:  1.1703e+01  1.1617e+01  9e-02  6e-17  7e-15\n",
      " 5:  1.1663e+01  1.1660e+01  3e-03  1e-16  1e-14\n",
      " 6:  1.1663e+01  1.1663e+01  4e-05  1e-16  6e-15\n",
      " 7:  1.1663e+01  1.1663e+01  4e-07  1e-16  8e-15\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.1359e+01  1.0306e+01  9e+00  3e+00  4e+00\n",
      " 1:  1.1371e+01  1.0886e+01  2e+00  4e-01  5e-01\n",
      " 2:  1.9213e+01  1.0304e+01  9e+00  6e-17  2e-14\n",
      " 3:  1.5109e+01  1.0066e+01  5e+00  2e-16  3e-14\n",
      " 4:  1.3282e+01  1.2736e+01  5e-01  8e-17  1e-14\n",
      " 5:  1.3096e+01  1.3034e+01  6e-02  2e-16  5e-15\n",
      " 6:  1.3087e+01  1.3083e+01  4e-03  1e-16  4e-15\n",
      " 7:  1.3086e+01  1.3086e+01  5e-05  2e-16  6e-15\n",
      " 8:  1.3086e+01  1.3086e+01  5e-07  1e-16  4e-15\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.2745e+01  1.1693e+01  9e+00  3e+00  4e+00\n",
      " 1:  1.2756e+01  1.2274e+01  2e+00  4e-01  6e-01\n",
      " 2:  2.2449e+01  1.1122e+01  1e+01  5e-03  7e-03\n",
      " 3:  1.7697e+01  1.0367e+01  7e+00  1e-16  2e-14\n",
      " 4:  1.5004e+01  1.4121e+01  9e-01  1e-16  1e-14\n",
      " 5:  1.4703e+01  1.4601e+01  1e-01  2e-16  1e-14\n",
      " 6:  1.4684e+01  1.4676e+01  8e-03  8e-17  1e-14\n",
      " 7:  1.4684e+01  1.4684e+01  1e-04  1e-16  8e-15\n",
      " 8:  1.4684e+01  1.4684e+01  1e-06  9e-17  1e-14\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.4301e+01  1.3248e+01  9e+00  3e+00  4e+00\n",
      " 1:  1.4310e+01  1.3832e+01  2e+00  4e-01  6e-01\n",
      " 2:  2.6257e+01  1.1925e+01  1e+01  1e-02  1e-02\n",
      " 3:  2.0736e+01  1.0610e+01  1e+01  2e-16  1e-14\n",
      " 4:  1.6958e+01  1.5631e+01  1e+00  1e-16  2e-14\n",
      " 5:  1.6508e+01  1.6357e+01  2e-01  1e-16  6e-15\n",
      " 6:  1.6477e+01  1.6463e+01  1e-02  8e-17  5e-15\n",
      " 7:  1.6476e+01  1.6476e+01  2e-04  1e-16  6e-15\n",
      " 8:  1.6476e+01  1.6476e+01  2e-06  8e-17  1e-14\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.6046e+01  1.4994e+01  9e+00  3e+00  4e+00\n",
      " 1:  1.6055e+01  1.5579e+01  2e+00  4e-01  6e-01\n",
      " 2:  3.0720e+01  1.2698e+01  2e+01  2e-02  2e-02\n",
      " 3:  2.4200e+01  1.0904e+01  1e+01  2e-16  4e-14\n",
      " 4:  1.9159e+01  1.7297e+01  2e+00  1e-16  2e-14\n",
      " 5:  1.8534e+01  1.8328e+01  2e-01  1e-16  2e-14\n",
      " 6:  1.8489e+01  1.8468e+01  2e-02  1e-16  2e-14\n",
      " 7:  1.8487e+01  1.8487e+01  4e-04  2e-16  9e-15\n",
      " 8:  1.8487e+01  1.8487e+01  4e-06  8e-17  2e-14\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.0516e-03 -1.0184e+00  1e+00  3e-17  3e+00\n",
      " 1:  1.5273e-03 -2.8444e-02  3e-02  9e-17  8e-02\n",
      " 2: -1.5551e-02 -1.9941e-02  4e-03  2e-16  4e-04\n",
      " 3: -1.8337e-02 -1.8423e-02  9e-05  2e-16  5e-06\n",
      " 4: -1.8378e-02 -1.8378e-02  9e-07  2e-16  5e-08\n",
      " 5: -1.8378e-02 -1.8378e-02  9e-09  2e-17  5e-10\n",
      "Optimal solution found.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from rf import return_portfolios, optimal_portfolio\n",
    "import numpy as np\n",
    "\n",
    "# 1. Load the stock data\n",
    "stock_data = pd.read_csv('stock_data_weak.csv')\n",
    "print(stock_data)\n",
    "# 2. Find the quarterly return for each period\n",
    "selected = list(stock_data.columns[1:])\n",
    "returns_quarterly = stock_data[selected].pct_change()\n",
    "# 3. Find the expected returns \n",
    "expected_returns = returns_quarterly.mean()\n",
    "\n",
    "# 4. Find the covariance \n",
    "cov_quarterly =returns_quarterly.cov()\n",
    "\n",
    "# 5. Find a set of random portfolios\n",
    "random_portfolios = return_portfolios(expected_returns,cov_quarterly)\n",
    "print(random_portfolios)\n",
    "# 6. Plot the set of random portfolios\n",
    "random_portfolios.plot.scatter(x='Volatility',y='Returns',fontsize=12)\n",
    "\n",
    "# 7. Calculate the set of portfolios on the EF\n",
    "weights, returns, risks = optimal_portfolio(returns_quarterly[1:])\n",
    "\n",
    "# 8. Plot the set of portfolios on the EF\n",
    "plt.plot(risks, returns, 'y-o')\n",
    "plt.ylabel('Expected Returns',fontsize=14)\n",
    "plt.xlabel('Volatility (Std. Deviation)',fontsize=14)\n",
    "plt.title('Efficient Frontier', fontsize=24)\n",
    "# Compare the set of portfolios on the EF to the \n",
    "try:\n",
    "  single_asset_std=np.sqrt(np.diagonal(cov_quarterly))\n",
    "  plt.scatter(single_asset_std,expected_returns,marker='X',color='red',s=200)\n",
    "except:\n",
    "  pass\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -1.0662e-02 -1.0359e+00  1e+00  0e+00  3e+00\n",
      " 1: -1.1534e-02 -4.5915e-02  3e-02  1e-16  1e-01\n",
      " 2: -3.2498e-02 -3.8737e-02  6e-03  2e-16  2e-03\n",
      " 3: -3.4247e-02 -3.4697e-02  4e-04  1e-16  1e-04\n",
      " 4: -3.4442e-02 -3.4469e-02  3e-05  1e-16  5e-07\n",
      " 5: -3.4446e-02 -3.4446e-02  4e-07  1e-16  5e-09\n",
      " 6: -3.4446e-02 -3.4446e-02  4e-09  1e-16  5e-11\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -1.0582e-02 -1.0358e+00  1e+00  2e-16  3e+00\n",
      " 1: -1.1451e-02 -4.5792e-02  3e-02  1e-16  1e-01\n",
      " 2: -3.2227e-02 -3.8492e-02  6e-03  2e-16  2e-03\n",
      " 3: -3.4012e-02 -3.4437e-02  4e-04  2e-16  8e-05\n",
      " 4: -3.4193e-02 -3.4213e-02  2e-05  1e-16  7e-07\n",
      " 5: -3.4196e-02 -3.4196e-02  2e-07  2e-16  7e-09\n",
      " 6: -3.4196e-02 -3.4196e-02  2e-09  2e-16  7e-11\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -1.0492e-02 -1.0357e+00  1e+00  0e+00  3e+00\n",
      " 1: -1.1358e-02 -4.5653e-02  3e-02  9e-17  1e-01\n",
      " 2: -3.1924e-02 -3.8217e-02  6e-03  1e-16  2e-03\n",
      " 3: -3.3750e-02 -3.4150e-02  4e-04  2e-16  7e-05\n",
      " 4: -3.3920e-02 -3.3935e-02  1e-05  3e-16  7e-07\n",
      " 5: -3.3923e-02 -3.3923e-02  2e-07  1e-16  7e-09\n",
      " 6: -3.3923e-02 -3.3923e-02  2e-09  2e-16  7e-11\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -1.0392e-02 -1.0355e+00  1e+00  2e-16  3e+00\n",
      " 1: -1.1254e-02 -4.5497e-02  3e-02  1e-16  1e-01\n",
      " 2: -3.1587e-02 -3.7908e-02  6e-03  1e-16  1e-03\n",
      " 3: -3.3458e-02 -3.3831e-02  4e-04  5e-17  6e-05\n",
      " 4: -3.3620e-02 -3.3630e-02  1e-05  1e-16  6e-07\n",
      " 5: -3.3622e-02 -3.3623e-02  1e-07  2e-16  6e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -1.0279e-02 -1.0353e+00  1e+00  6e-17  3e+00\n",
      " 1: -1.1137e-02 -4.5322e-02  3e-02  1e-16  1e-01\n",
      " 2: -3.1211e-02 -3.7562e-02  6e-03  2e-16  1e-03\n",
      " 3: -3.3132e-02 -3.3478e-02  3e-04  1e-16  5e-05\n",
      " 4: -3.3290e-02 -3.3296e-02  7e-06  2e-16  5e-07\n",
      " 5: -3.3291e-02 -3.3292e-02  7e-08  2e-16  5e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -1.0153e-02 -1.0351e+00  1e+00  2e-16  3e+00\n",
      " 1: -1.1006e-02 -4.5126e-02  3e-02  8e-17  1e-01\n",
      " 2: -3.0792e-02 -3.7172e-02  6e-03  1e-16  1e-03\n",
      " 3: -3.2768e-02 -3.3088e-02  3e-04  3e-16  4e-05\n",
      " 4: -3.2923e-02 -3.2928e-02  5e-06  1e-16  4e-07\n",
      " 5: -3.2925e-02 -3.2925e-02  5e-08  2e-16  4e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -1.0012e-02 -1.0349e+00  1e+00  3e-17  3e+00\n",
      " 1: -1.0860e-02 -4.4906e-02  3e-02  6e-17  1e-01\n",
      " 2: -3.0327e-02 -3.6735e-02  6e-03  3e-16  8e-04\n",
      " 3: -3.2361e-02 -3.2656e-02  3e-04  1e-16  3e-05\n",
      " 4: -3.2517e-02 -3.2521e-02  3e-06  2e-16  3e-07\n",
      " 5: -3.2519e-02 -3.2519e-02  3e-08  3e-16  3e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -9.8539e-03 -1.0347e+00  1e+00  3e-17  3e+00\n",
      " 1: -1.0696e-02 -4.4659e-02  3e-02  7e-17  1e-01\n",
      " 2: -2.9810e-02 -3.6243e-02  6e-03  1e-16  6e-04\n",
      " 3: -3.1906e-02 -3.2179e-02  3e-04  8e-17  2e-05\n",
      " 4: -3.2066e-02 -3.2068e-02  3e-06  5e-17  2e-07\n",
      " 5: -3.2067e-02 -3.2067e-02  3e-08  1e-16  2e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -9.6771e-03 -1.0344e+00  1e+00  3e-16  3e+00\n",
      " 1: -1.0513e-02 -4.4382e-02  3e-02  8e-17  1e-01\n",
      " 2: -2.9238e-02 -3.5691e-02  6e-03  3e-16  4e-04\n",
      " 3: -3.1396e-02 -3.1651e-02  3e-04  1e-16  1e-05\n",
      " 4: -3.1562e-02 -3.1565e-02  3e-06  2e-16  1e-07\n",
      " 5: -3.1564e-02 -3.1564e-02  3e-08  1e-16  1e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -9.4792e-03 -1.0341e+00  1e+00  6e-17  3e+00\n",
      " 1: -1.0309e-02 -4.4071e-02  3e-02  1e-16  1e-01\n",
      " 2: -2.8606e-02 -3.5072e-02  6e-03  1e-16  1e-04\n",
      " 3: -3.0825e-02 -3.1067e-02  2e-04  2e-16  3e-06\n",
      " 4: -3.1001e-02 -3.1003e-02  2e-06  2e-16  3e-08\n",
      " 5: -3.1003e-02 -3.1003e-02  2e-08  3e-17  3e-10\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -9.2579e-03 -1.0338e+00  1e+00  1e-16  3e+00\n",
      " 1: -1.0080e-02 -4.3722e-02  3e-02  1e-16  1e-01\n",
      " 2: -2.7883e-02 -3.4390e-02  7e-03  5e-16  4e-17\n",
      " 3: -3.0182e-02 -3.0421e-02  2e-04  2e-16  2e-17\n",
      " 4: -3.0374e-02 -3.0376e-02  2e-06  2e-16  1e-17\n",
      " 5: -3.0376e-02 -3.0376e-02  2e-08  2e-16  3e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -9.0105e-03 -1.0334e+00  1e+00  3e-17  3e+00\n",
      " 1: -9.8250e-03 -4.3331e-02  3e-02  2e-16  1e-01\n",
      " 2: -2.7072e-02 -3.3637e-02  7e-03  7e-17  3e-17\n",
      " 3: -2.9443e-02 -2.9730e-02  3e-04  1e-16  4e-17\n",
      " 4: -2.9672e-02 -2.9675e-02  3e-06  6e-17  3e-17\n",
      " 5: -2.9675e-02 -2.9675e-02  3e-08  2e-16  1e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -8.7340e-03 -1.0329e+00  1e+00  2e-16  3e+00\n",
      " 1: -9.5402e-03 -4.2892e-02  3e-02  6e-17  1e-01\n",
      " 2: -2.6189e-02 -3.2795e-02  7e-03  1e-16  2e-17\n",
      " 3: -2.8611e-02 -2.8963e-02  4e-04  1e-16  2e-17\n",
      " 4: -2.8888e-02 -2.8891e-02  4e-06  4e-16  2e-17\n",
      " 5: -2.8891e-02 -2.8891e-02  4e-08  4e-16  3e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -8.4252e-03 -1.0324e+00  1e+00  8e-17  3e+00\n",
      " 1: -9.2225e-03 -4.2400e-02  3e-02  6e-17  1e-01\n",
      " 2: -2.5233e-02 -3.1856e-02  7e-03  6e-17  2e-17\n",
      " 3: -2.7684e-02 -2.8096e-02  4e-04  2e-16  2e-17\n",
      " 4: -2.8009e-02 -2.8014e-02  5e-06  6e-17  2e-17\n",
      " 5: -2.8013e-02 -2.8013e-02  5e-08  6e-17  1e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -8.0805e-03 -1.0319e+00  1e+00  6e-17  3e+00\n",
      " 1: -8.8683e-03 -4.1848e-02  3e-02  8e-17  1e-01\n",
      " 2: -2.4203e-02 -3.0812e-02  7e-03  2e-16  1e-17\n",
      " 3: -2.6652e-02 -2.7118e-02  5e-04  6e-17  1e-17\n",
      " 4: -2.7024e-02 -2.7031e-02  6e-06  1e-16  2e-17\n",
      " 5: -2.7030e-02 -2.7030e-02  6e-08  1e-16  3e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -7.6959e-03 -1.0313e+00  1e+00  6e-17  3e+00\n",
      " 1: -8.4739e-03 -4.1228e-02  3e-02  9e-17  1e-01\n",
      " 2: -2.3100e-02 -2.9654e-02  7e-03  2e-16  2e-17\n",
      " 3: -2.5508e-02 -2.6017e-02  5e-04  1e-16  4e-17\n",
      " 4: -2.5920e-02 -2.5929e-02  1e-05  1e-16  8e-18\n",
      " 5: -2.5928e-02 -2.5929e-02  1e-07  1e-16  2e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -7.2671e-03 -1.0306e+00  1e+00  6e-17  3e+00\n",
      " 1: -8.0349e-03 -4.0534e-02  3e-02  8e-17  1e-01\n",
      " 2: -2.1927e-02 -2.8377e-02  6e-03  4e-16  1e-17\n",
      " 3: -2.4247e-02 -2.4784e-02  5e-04  1e-16  1e-17\n",
      " 4: -2.4678e-02 -2.4695e-02  2e-05  2e-16  2e-17\n",
      " 5: -2.4694e-02 -2.4694e-02  2e-07  2e-16  1e-17\n",
      " 6: -2.4694e-02 -2.4694e-02  2e-09  4e-16  1e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -6.7894e-03 -1.0298e+00  1e+00  2e-16  3e+00\n",
      " 1: -7.5468e-03 -3.9755e-02  3e-02  1e-16  1e-01\n",
      " 2: -2.0688e-02 -2.6976e-02  6e-03  1e-16  4e-17\n",
      " 3: -2.2874e-02 -2.3417e-02  5e-04  1e-16  6e-18\n",
      " 4: -2.3281e-02 -2.3313e-02  3e-05  1e-16  1e-17\n",
      " 5: -2.3310e-02 -2.3311e-02  5e-07  2e-16  2e-17\n",
      " 6: -2.3311e-02 -2.3311e-02  5e-09  6e-17  6e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -6.2576e-03 -1.0290e+00  1e+00  1e-16  3e+00\n",
      " 1: -7.0047e-03 -3.8882e-02  3e-02  2e-16  1e-01\n",
      " 2: -1.9391e-02 -2.5450e-02  6e-03  1e-16  3e-17\n",
      " 3: -2.1408e-02 -2.1922e-02  5e-04  1e-16  2e-17\n",
      " 4: -2.1739e-02 -2.1778e-02  4e-05  1e-16  8e-18\n",
      " 5: -2.1762e-02 -2.1765e-02  3e-06  2e-16  1e-17\n",
      " 6: -2.1762e-02 -2.1763e-02  1e-07  3e-16  2e-17\n",
      " 7: -2.1762e-02 -2.1762e-02  1e-09  1e-16  2e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -5.6660e-03 -1.0280e+00  1e+00  4e-16  3e+00\n",
      " 1: -6.4033e-03 -3.7903e-02  3e-02  2e-16  1e-01\n",
      " 2: -1.8045e-02 -2.3800e-02  6e-03  1e-16  2e-17\n",
      " 3: -1.9880e-02 -2.0337e-02  5e-04  1e-16  3e-17\n",
      " 4: -2.0148e-02 -2.0183e-02  4e-05  2e-16  1e-17\n",
      " 5: -2.0179e-02 -2.0180e-02  4e-07  2e-16  3e-17\n",
      " 6: -2.0180e-02 -2.0180e-02  4e-09  1e-16  1e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -5.0087e-03 -1.0269e+00  1e+00  2e-16  3e+00\n",
      " 1: -5.7369e-03 -3.6806e-02  3e-02  7e-17  9e-02\n",
      " 2: -1.6663e-02 -2.2037e-02  5e-03  2e-16  2e-17\n",
      " 3: -1.8349e-02 -1.8709e-02  4e-04  1e-16  2e-17\n",
      " 4: -1.8606e-02 -1.8632e-02  3e-05  1e-16  9e-18\n",
      " 5: -1.8626e-02 -1.8626e-02  7e-07  1e-16  2e-17\n",
      " 6: -1.8626e-02 -1.8626e-02  7e-09  2e-16  1e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -4.2791e-03 -1.0257e+00  1e+00  2e-16  3e+00\n",
      " 1: -4.9996e-03 -3.5578e-02  3e-02  8e-17  9e-02\n",
      " 2: -1.5259e-02 -2.0174e-02  5e-03  9e-17  2e-17\n",
      " 3: -1.6747e-02 -1.7341e-02  6e-04  3e-17  9e-18\n",
      " 4: -1.7065e-02 -1.7100e-02  3e-05  8e-17  5e-18\n",
      " 5: -1.7081e-02 -1.7084e-02  3e-06  1e-16  3e-17\n",
      " 6: -1.7082e-02 -1.7082e-02  4e-07  8e-17  1e-17\n",
      " 7: -1.7082e-02 -1.7082e-02  2e-08  8e-17  8e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -3.4702e-03 -1.0243e+00  1e+00  3e-17  3e+00\n",
      " 1: -4.1849e-03 -3.4202e-02  3e-02  6e-17  9e-02\n",
      " 2: -1.3846e-02 -1.8236e-02  4e-03  3e-17  2e-17\n",
      " 3: -1.5295e-02 -1.5848e-02  6e-04  7e-17  1e-17\n",
      " 4: -1.5575e-02 -1.5592e-02  2e-05  1e-16  2e-17\n",
      " 5: -1.5582e-02 -1.5582e-02  2e-07  1e-16  2e-17\n",
      " 6: -1.5582e-02 -1.5582e-02  2e-09  2e-16  4e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -2.5746e-03 -1.0228e+00  1e+00  6e-17  3e+00\n",
      " 1: -3.2864e-03 -3.2662e-02  3e-02  7e-17  9e-02\n",
      " 2: -1.2442e-02 -1.6256e-02  4e-03  7e-17  3e-17\n",
      " 3: -1.4024e-02 -1.4249e-02  2e-04  8e-17  1e-17\n",
      " 4: -1.4146e-02 -1.4149e-02  3e-06  6e-17  1e-17\n",
      " 5: -1.4148e-02 -1.4148e-02  3e-08  7e-17  2e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -1.5843e-03 -1.0211e+00  1e+00  6e-17  3e+00\n",
      " 1: -2.2972e-03 -3.0939e-02  3e-02  1e-16  9e-02\n",
      " 2: -1.1060e-02 -1.4278e-02  3e-03  1e-16  2e-17\n",
      " 3: -1.2639e-02 -1.2881e-02  2e-04  6e-17  9e-18\n",
      " 4: -1.2758e-02 -1.2761e-02  3e-06  1e-16  9e-18\n",
      " 5: -1.2760e-02 -1.2760e-02  3e-08  1e-16  2e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -4.9097e-04 -1.0192e+00  1e+00  3e-17  3e+00\n",
      " 1: -1.2101e-03 -2.9012e-02  3e-02  1e-16  8e-02\n",
      " 2: -9.4797e-03 -1.2834e-02  3e-03  6e-17  3e-03\n",
      " 3: -1.1271e-02 -1.1513e-02  2e-04  6e-17  2e-17\n",
      " 4: -1.1398e-02 -1.1401e-02  3e-06  8e-17  2e-17\n",
      " 5: -1.1399e-02 -1.1399e-02  3e-08  7e-17  6e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  7.1408e-04 -1.0171e+00  1e+00  8e-17  3e+00\n",
      " 1: -1.8122e-05 -2.6860e-02  3e-02  7e-17  8e-02\n",
      " 2: -7.9093e-03 -1.1547e-02  4e-03  7e-17  5e-03\n",
      " 3: -9.9132e-03 -1.0176e-02  3e-04  1e-16  1e-17\n",
      " 4: -1.0046e-02 -1.0051e-02  5e-06  1e-16  3e-17\n",
      " 5: -1.0049e-02 -1.0049e-02  5e-08  1e-16  1e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.0399e-03 -1.0147e+00  1e+00  4e-16  3e+00\n",
      " 1:  1.2859e-03 -2.4459e-02  3e-02  8e-17  8e-02\n",
      " 2: -6.4622e-03 -1.0172e-02  4e-03  7e-17  7e-03\n",
      " 3: -8.5549e-03 -8.8301e-03  3e-04  9e-17  9e-18\n",
      " 4: -8.6851e-03 -8.6931e-03  8e-06  1e-16  2e-17\n",
      " 5: -8.6905e-03 -8.6906e-03  8e-08  2e-16  1e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  3.4959e-03 -1.0120e+00  1e+00  6e-17  3e+00\n",
      " 1:  2.7092e-03 -2.1781e-02  2e-02  1e-16  7e-02\n",
      " 2: -5.3709e-03 -8.6313e-03  3e-03  1e-16  5e-03\n",
      " 3: -7.1932e-03 -7.4476e-03  3e-04  1e-16  8e-18\n",
      " 4: -7.2967e-03 -7.3104e-03  1e-05  8e-17  2e-17\n",
      " 5: -7.3057e-03 -7.3061e-03  3e-07  1e-16  2e-17\n",
      " 6: -7.3060e-03 -7.3060e-03  3e-09  2e-16  2e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  5.0916e-03 -1.0109e+00  1e+00  8e-17  3e+00\n",
      " 1:  4.2614e-03 -2.0555e-02  2e-02  1e-16  8e-02\n",
      " 2: -4.1128e-03 -7.1892e-03  3e-03  1e-16  3e-03\n",
      " 3: -5.7968e-03 -6.0458e-03  2e-04  2e-16  1e-17\n",
      " 4: -5.8754e-03 -5.8920e-03  2e-05  3e-16  2e-17\n",
      " 5: -5.8790e-03 -5.8803e-03  1e-06  2e-16  2e-17\n",
      " 6: -5.8791e-03 -5.8792e-03  5e-08  1e-16  5e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  6.8368e-03 -1.0098e+00  1e+00  1e-16  3e+00\n",
      " 1:  5.9475e-03 -1.9492e-02  3e-02  2e-16  8e-02\n",
      " 2: -2.7737e-03 -5.7860e-03  3e-03  1e-16  2e-03\n",
      " 3: -4.3822e-03 -4.6099e-03  2e-04  6e-17  1e-17\n",
      " 4: -4.4440e-03 -4.4554e-03  1e-05  1e-16  4e-17\n",
      " 5: -4.4448e-03 -4.4449e-03  1e-07  2e-16  1e-17\n",
      " 6: -4.4448e-03 -4.4448e-03  1e-09  1e-16  2e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  8.7410e-03 -1.0093e+00  1e+00  2e-16  3e+00\n",
      " 1:  7.7748e-03 -1.8794e-02  3e-02  1e-16  8e-02\n",
      " 2: -1.2887e-03 -4.4533e-03  3e-03  6e-17  2e-03\n",
      " 3: -2.9393e-03 -3.1676e-03  2e-04  1e-16  1e-17\n",
      " 4: -3.0060e-03 -3.0187e-03  1e-05  1e-16  2e-17\n",
      " 5: -3.0071e-03 -3.0073e-03  2e-07  8e-17  2e-17\n",
      " 6: -3.0071e-03 -3.0071e-03  2e-09  1e-16  1e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.0814e-02 -1.0090e+00  1e+00  6e-17  3e+00\n",
      " 1:  9.7498e-03 -1.8447e-02  3e-02  8e-17  9e-02\n",
      " 2:  2.5682e-04 -3.1343e-03  3e-03  4e-16  2e-03\n",
      " 3: -1.4644e-03 -1.7046e-03  2e-04  2e-16  1e-17\n",
      " 4: -1.5450e-03 -1.5618e-03  2e-05  1e-16  8e-18\n",
      " 5: -1.5470e-03 -1.5476e-03  6e-07  1e-16  2e-17\n",
      " 6: -1.5471e-03 -1.5471e-03  6e-09  1e-16  3e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.3066e-02 -1.0087e+00  1e+00  8e-17  3e+00\n",
      " 1:  1.1878e-02 -1.7985e-02  3e-02  8e-17  9e-02\n",
      " 2:  1.8828e-03 -1.7836e-03  4e-03  2e-16  1e-03\n",
      " 3:  5.9971e-05 -2.0451e-04  3e-04  2e-16  2e-17\n",
      " 4: -4.0651e-05 -6.1866e-05  2e-05  6e-17  3e-17\n",
      " 5: -4.5133e-05 -4.6962e-05  2e-06  6e-17  7e-18\n",
      " 6: -4.5239e-05 -4.5267e-05  3e-08  1e-16  2e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.5505e-02 -1.0083e+00  1e+00  1e-16  3e+00\n",
      " 1:  1.4165e-02 -1.7385e-02  3e-02  1e-16  1e-01\n",
      " 2:  3.6033e-03 -3.7966e-04  4e-03  3e-16  1e-03\n",
      " 3:  1.6543e-03  1.3540e-03  3e-04  6e-17  3e-17\n",
      " 4:  1.5281e-03  1.5033e-03  2e-05  1e-16  2e-17\n",
      " 5:  1.5191e-03  1.5159e-03  3e-06  2e-16  1e-17\n",
      " 6:  1.5183e-03  1.5179e-03  4e-07  8e-17  2e-17\n",
      " 7:  1.5183e-03  1.5183e-03  1e-08  6e-17  3e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.8142e-02 -1.0077e+00  1e+00  2e-16  3e+00\n",
      " 1:  1.6617e-02 -1.6619e-02  3e-02  2e-16  1e-01\n",
      " 2:  5.4349e-03  1.1016e-03  4e-03  2e-16  2e-03\n",
      " 3:  3.3399e-03  2.9944e-03  3e-04  8e-17  3e-17\n",
      " 4:  3.1835e-03  3.1564e-03  3e-05  1e-16  2e-17\n",
      " 5:  3.1689e-03  3.1655e-03  3e-06  1e-16  2e-17\n",
      " 6:  3.1665e-03  3.1662e-03  3e-07  8e-17  1e-17\n",
      " 7:  3.1662e-03  3.1662e-03  3e-09  2e-16  4e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.0986e-02 -1.0081e+00  1e+00  3e-16  3e+00\n",
      " 1:  1.9243e-02 -1.6555e-02  4e-02  1e-16  1e-01\n",
      " 2:  7.3793e-03  2.6684e-03  5e-03  8e-17  1e-03\n",
      " 3:  5.1375e-03  4.7415e-03  4e-04  6e-17  1e-17\n",
      " 4:  4.9489e-03  4.9210e-03  3e-05  2e-16  2e-17\n",
      " 5:  4.9297e-03  4.9276e-03  2e-06  2e-16  1e-17\n",
      " 6:  4.9278e-03  4.9278e-03  3e-08  1e-16  1e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.4047e-02 -1.0083e+00  1e+00  8e-17  3e+00\n",
      " 1:  2.2048e-02 -1.6433e-02  4e-02  6e-17  1e-01\n",
      " 2:  9.4239e-03  4.3103e-03  5e-03  1e-16  5e-17\n",
      " 3:  7.0670e-03  6.6252e-03  4e-04  1e-16  3e-17\n",
      " 4:  6.8487e-03  6.8216e-03  3e-05  6e-17  1e-17\n",
      " 5:  6.8281e-03  6.8270e-03  1e-06  1e-16  2e-17\n",
      " 6:  6.8270e-03  6.8270e-03  1e-08  1e-16  3e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.7334e-02 -1.0083e+00  1e+00  8e-17  3e+00\n",
      " 1:  2.5039e-02 -1.6131e-02  4e-02  9e-17  1e-01\n",
      " 2:  1.1714e-02  5.9548e-03  6e-03  7e-17  6e-17\n",
      " 3:  9.1657e-03  8.6625e-03  5e-04  8e-17  3e-17\n",
      " 4:  8.9103e-03  8.8836e-03  3e-05  1e-16  1e-17\n",
      " 5:  8.8897e-03  8.8891e-03  6e-07  1e-16  3e-17\n",
      " 6:  8.8892e-03  8.8892e-03  6e-09  6e-17  2e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  3.0857e-02 -1.0082e+00  1e+00  1e-16  3e+00\n",
      " 1:  2.8225e-02 -1.5612e-02  4e-02  3e-17  1e-01\n",
      " 2:  1.4186e-02  7.8180e-03  6e-03  1e-16  5e-17\n",
      " 3:  1.1451e-02  1.0892e-02  6e-04  8e-17  2e-17\n",
      " 4:  1.1161e-02  1.1135e-02  3e-05  1e-16  2e-17\n",
      " 5:  1.1142e-02  1.1141e-02  4e-07  1e-16  2e-17\n",
      " 6:  1.1142e-02  1.1142e-02  4e-09  2e-16  2e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  3.4629e-02 -1.0079e+00  1e+00  2e-16  3e+00\n",
      " 1:  3.1617e-02 -1.4838e-02  5e-02  8e-17  1e-01\n",
      " 2:  1.6866e-02  9.9496e-03  7e-03  1e-16  4e-17\n",
      " 3:  1.3953e-02  1.3344e-02  6e-04  8e-17  1e-17\n",
      " 4:  1.3631e-02  1.3606e-02  3e-05  2e-16  3e-17\n",
      " 5:  1.3614e-02  1.3614e-02  3e-07  8e-17  2e-17\n",
      " 6:  1.3614e-02  1.3614e-02  3e-09  2e-16  2e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  3.8663e-02 -1.0074e+00  1e+00  2e-16  3e+00\n",
      " 1:  3.5230e-02 -1.3765e-02  5e-02  1e-16  1e-01\n",
      " 2:  1.9783e-02  1.2398e-02  7e-03  1e-16  4e-17\n",
      " 3:  1.6702e-02  1.6054e-02  6e-04  1e-16  3e-17\n",
      " 4:  1.6355e-02  1.6329e-02  3e-05  6e-17  2e-17\n",
      " 5:  1.6340e-02  1.6339e-02  3e-07  8e-17  2e-17\n",
      " 6:  1.6340e-02  1.6340e-02  3e-09  1e-16  2e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  4.2973e-02 -1.0066e+00  1e+00  1e-16  3e+00\n",
      " 1:  3.9081e-02 -1.2348e-02  5e-02  8e-17  2e-01\n",
      " 2:  2.2969e-02  1.5209e-02  8e-03  7e-17  4e-17\n",
      " 3:  1.9735e-02  1.9057e-02  7e-04  6e-17  4e-17\n",
      " 4:  1.9368e-02  1.9340e-02  3e-05  1e-16  3e-17\n",
      " 5:  1.9354e-02  1.9354e-02  3e-07  2e-16  2e-17\n",
      " 6:  1.9354e-02  1.9354e-02  3e-09  1e-16  2e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  4.7578e-02 -1.0054e+00  1e+00  1e-16  3e+00\n",
      " 1:  4.3192e-02 -1.0539e-02  5e-02  8e-17  2e-01\n",
      " 2:  2.6461e-02  1.8428e-02  8e-03  9e-17  7e-17\n",
      " 3:  2.3090e-02  2.2394e-02  7e-04  1e-16  3e-17\n",
      " 4:  2.2710e-02  2.2681e-02  3e-05  8e-17  4e-17\n",
      " 5:  2.2698e-02  2.2698e-02  3e-07  1e-16  4e-17\n",
      " 6:  2.2698e-02  2.2698e-02  3e-09  1e-16  5e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  5.2501e-02 -1.0038e+00  1e+00  2e-16  3e+00\n",
      " 1:  4.7589e-02 -8.2862e-03  6e-02  8e-17  2e-01\n",
      " 2:  3.0296e-02  2.2098e-02  8e-03  1e-16  7e-17\n",
      " 3:  2.6812e-02  2.6108e-02  7e-04  2e-16  3e-17\n",
      " 4:  2.6427e-02  2.6394e-02  3e-05  2e-16  2e-17\n",
      " 5:  2.6415e-02  2.6415e-02  5e-07  8e-17  3e-17\n",
      " 6:  2.6415e-02  2.6415e-02  5e-09  2e-16  4e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  5.7766e-02 -1.0017e+00  1e+00  8e-17  3e+00\n",
      " 1:  5.2305e-02 -5.5356e-03  6e-02  4e-17  2e-01\n",
      " 2:  3.4522e-02  2.6266e-02  8e-03  1e-16  1e-16\n",
      " 3:  3.0950e-02  3.0249e-02  7e-04  1e-16  6e-17\n",
      " 4:  3.0568e-02  3.0531e-02  4e-05  1e-16  3e-17\n",
      " 5:  3.0555e-02  3.0554e-02  7e-07  1e-16  3e-17\n",
      " 6:  3.0555e-02  3.0555e-02  7e-09  1e-16  4e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  6.3405e-02 -9.9915e-01  1e+00  2e-16  3e+00\n",
      " 1:  5.7378e-02 -2.2294e-03  6e-02  2e-16  2e-01\n",
      " 2:  3.9187e-02  3.0976e-02  8e-03  1e-16  8e-17\n",
      " 3:  3.5559e-02  3.4872e-02  7e-04  1e-16  5e-17\n",
      " 4:  3.5187e-02  3.5145e-02  4e-05  2e-16  5e-17\n",
      " 5:  3.5173e-02  3.5172e-02  1e-06  8e-17  3e-17\n",
      " 6:  3.5173e-02  3.5173e-02  1e-08  1e-16  5e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  6.9455e-02 -9.9599e-01  1e+00  4e-16  3e+00\n",
      " 1:  6.2856e-02  1.6937e-03  6e-02  1e-16  2e-01\n",
      " 2:  4.4351e-02  3.6281e-02  8e-03  2e-16  1e-16\n",
      " 3:  4.0702e-02  4.0036e-02  7e-04  2e-16  5e-17\n",
      " 4:  4.0348e-02  4.0299e-02  5e-05  1e-16  6e-17\n",
      " 5:  4.0330e-02  4.0328e-02  2e-06  1e-16  6e-17\n",
      " 6:  4.0329e-02  4.0329e-02  2e-08  1e-16  5e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  7.5959e-02 -9.9217e-01  1e+00  6e-17  3e+00\n",
      " 1:  6.8791e-02  6.3002e-03  6e-02  6e-17  2e-01\n",
      " 2:  5.0077e-02  4.2236e-02  8e-03  1e-16  7e-17\n",
      " 3:  4.6447e-02  4.5810e-02  6e-04  2e-16  7e-17\n",
      " 4:  4.6116e-02  4.6061e-02  5e-05  1e-16  5e-17\n",
      " 5:  4.6094e-02  4.6092e-02  2e-06  2e-16  5e-17\n",
      " 6:  4.6094e-02  4.6094e-02  3e-08  2e-16  6e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  8.2966e-02 -9.8762e-01  1e+00  1e-16  3e+00\n",
      " 1:  7.5245e-02  1.1662e-02  6e-02  1e-16  2e-01\n",
      " 2:  5.6441e-02  4.8905e-02  8e-03  1e-16  8e-17\n",
      " 3:  5.2874e-02  5.2270e-02  6e-04  1e-16  1e-16\n",
      " 4:  5.2567e-02  5.2509e-02  6e-05  1e-16  3e-17\n",
      " 5:  5.2542e-02  5.2539e-02  4e-06  2e-16  2e-17\n",
      " 6:  5.2542e-02  5.2542e-02  6e-08  3e-22  5e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  9.0532e-02 -9.8225e-01  1e+00  8e-17  3e+00\n",
      " 1:  8.2290e-02  1.7859e-02  6e-02  6e-17  2e-01\n",
      " 2:  6.3566e-02  5.6255e-02  7e-03  1e-16  7e-04\n",
      " 3:  6.0072e-02  5.9499e-02  6e-04  8e-17  8e-17\n",
      " 4:  5.9789e-02  5.9727e-02  6e-05  3e-16  5e-17\n",
      " 5:  5.9761e-02  5.9755e-02  5e-06  6e-17  9e-17\n",
      " 6:  5.9759e-02  5.9759e-02  2e-07  8e-17  7e-17\n",
      " 7:  5.9759e-02  5.9759e-02  2e-09  1e-16  4e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  9.8724e-02 -9.7599e-01  1e+00  2e-16  3e+00\n",
      " 1:  9.0006e-02  2.4979e-02  7e-02  9e-17  2e-01\n",
      " 2:  7.1512e-02  6.4454e-02  7e-03  1e-16  1e-03\n",
      " 3:  6.8136e-02  6.7593e-02  5e-04  1e-16  5e-07\n",
      " 4:  6.7876e-02  6.7810e-02  7e-05  2e-16  6e-17\n",
      " 5:  6.7845e-02  6.7838e-02  8e-06  1e-16  7e-17\n",
      " 6:  6.7842e-02  6.7842e-02  5e-07  2e-16  7e-17\n",
      " 7:  6.7842e-02  6.7842e-02  1e-08  1e-16  8e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.0761e-01 -9.6873e-01  1e+00  4e-16  3e+00\n",
      " 1:  9.8485e-02  3.3119e-02  7e-02  1e-16  2e-01\n",
      " 2:  8.0373e-02  7.3632e-02  7e-03  2e-16  2e-03\n",
      " 3:  7.7172e-02  7.6658e-02  5e-04  1e-16  3e-06\n",
      " 4:  7.6934e-02  7.6867e-02  7e-05  1e-16  1e-16\n",
      " 5:  7.6903e-02  7.6894e-02  9e-06  8e-17  7e-17\n",
      " 6:  7.6898e-02  7.6897e-02  1e-06  1e-16  9e-17\n",
      " 7:  7.6898e-02  7.6897e-02  1e-07  6e-17  9e-17\n",
      " 8:  7.6898e-02  7.6898e-02  2e-08  2e-16  7e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.1729e-01 -9.6038e-01  1e+00  2e-16  3e+00\n",
      " 1:  1.0783e-01  4.2391e-02  7e-02  1e-16  2e-01\n",
      " 2:  9.0269e-02  8.3903e-02  6e-03  2e-16  3e-03\n",
      " 3:  8.7300e-02  8.6817e-02  5e-04  8e-17  5e-06\n",
      " 4:  8.7082e-02  8.7017e-02  7e-05  1e-16  1e-16\n",
      " 5:  8.7052e-02  8.7044e-02  9e-06  2e-16  1e-16\n",
      " 6:  8.7048e-02  8.7047e-02  1e-06  1e-16  9e-17\n",
      " 7:  8.7047e-02  8.7047e-02  6e-08  1e-16  9e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.2783e-01 -9.5083e-01  1e+00  0e+00  3e+00\n",
      " 1:  1.1815e-01  5.2920e-02  7e-02  2e-16  2e-01\n",
      " 2:  1.0134e-01  9.5400e-02  6e-03  2e-16  3e-03\n",
      " 3:  9.8652e-02  9.8202e-02  4e-04  2e-16  8e-06\n",
      " 4:  9.8453e-02  9.8394e-02  6e-05  2e-16  8e-17\n",
      " 5:  9.8425e-02  9.8419e-02  6e-06  2e-16  1e-16\n",
      " 6:  9.8422e-02  9.8422e-02  9e-08  3e-17  1e-16\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.3936e-01 -9.3994e-01  1e+00  6e-17  3e+00\n",
      " 1:  1.2959e-01  6.4846e-02  6e-02  1e-16  2e-01\n",
      " 2:  1.1375e-01  1.0827e-01  5e-03  8e-17  3e-03\n",
      " 3:  1.1138e-01  1.1096e-01  4e-04  2e-16  1e-05\n",
      " 4:  1.1120e-01  1.1115e-01  5e-05  8e-17  6e-17\n",
      " 5:  1.1117e-01  1.1117e-01  2e-06  8e-17  8e-17\n",
      " 6:  1.1117e-01  1.1117e-01  2e-08  1e-16  7e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.5198e-01 -9.2760e-01  1e+00  6e-17  4e+00\n",
      " 1:  1.4230e-01  7.8330e-02  6e-02  2e-16  2e-01\n",
      " 2:  1.2767e-01  1.2268e-01  5e-03  8e-17  3e-03\n",
      " 3:  1.2565e-01  1.2527e-01  4e-04  2e-16  1e-05\n",
      " 4:  1.2548e-01  1.2544e-01  4e-05  2e-16  2e-16\n",
      " 5:  1.2546e-01  1.2546e-01  6e-07  1e-16  7e-17\n",
      " 6:  1.2546e-01  1.2546e-01  6e-09  1e-16  1e-16\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.6583e-01 -9.1364e-01  9e+00  3e+00  4e+00\n",
      " 1:  2.2557e-01 -5.9658e-01  8e-01  3e-16  6e-16\n",
      " 2:  1.8180e-01  8.1035e-02  1e-01  1e-16  5e-16\n",
      " 3:  1.4864e-01  1.3594e-01  1e-02  1e-16  1e-16\n",
      " 4:  1.4200e-01  1.4103e-01  1e-03  1e-16  2e-16\n",
      " 5:  1.4153e-01  1.4144e-01  9e-05  1e-16  1e-16\n",
      " 6:  1.4148e-01  1.4148e-01  2e-06  6e-17  1e-16\n",
      " 7:  1.4148e-01  1.4148e-01  2e-08  1e-16  2e-16\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.8108e-01 -8.9789e-01  1e+01  3e+00  4e+00\n",
      " 1:  2.4432e-01 -5.7344e-01  8e-01  5e-16  4e-16\n",
      " 2:  1.9856e-01  9.9740e-02  1e-01  1e-16  6e-16\n",
      " 3:  1.6620e-01  1.5422e-01  1e-02  8e-17  2e-16\n",
      " 4:  1.5990e-01  1.5904e-01  9e-04  2e-16  2e-16\n",
      " 5:  1.5949e-01  1.5942e-01  7e-05  9e-17  3e-16\n",
      " 6:  1.5944e-01  1.5944e-01  1e-06  1e-16  1e-16\n",
      " 7:  1.5944e-01  1.5944e-01  1e-08  2e-16  7e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.9788e-01 -8.8019e-01  1e+01  3e+00  4e+00\n",
      " 1:  2.6470e-01 -5.4776e-01  8e-01  3e-16  8e-16\n",
      " 2:  2.1725e-01  1.2090e-01  1e-01  7e-17  8e-16\n",
      " 3:  1.8584e-01  1.7470e-01  1e-02  6e-17  2e-16\n",
      " 4:  1.8000e-01  1.7924e-01  8e-04  8e-17  2e-16\n",
      " 5:  1.7962e-01  1.7958e-01  5e-05  1e-16  2e-16\n",
      " 6:  1.7959e-01  1.7959e-01  6e-07  1e-16  2e-16\n",
      " 7:  1.7959e-01  1.7959e-01  6e-09  3e-17  2e-16\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.1644e-01 -8.6031e-01  1e+01  3e+00  4e+00\n",
      " 1:  2.8694e-01 -5.1934e-01  8e-01  3e-16  7e-16\n",
      " 2:  2.3814e-01  1.4476e-01  9e-02  8e-17  5e-16\n",
      " 3:  2.0786e-01  1.9764e-01  1e-02  2e-16  2e-16\n",
      " 4:  2.0256e-01  2.0188e-01  7e-04  1e-16  2e-16\n",
      " 5:  2.0221e-01  2.0218e-01  3e-05  1e-16  1e-16\n",
      " 6:  2.0219e-01  2.0219e-01  4e-07  3e-17  2e-16\n",
      " 7:  2.0219e-01  2.0219e-01  4e-09  6e-17  2e-16\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.3699e-01 -8.3803e-01  1e+01  3e+00  4e+00\n",
      " 1:  3.1129e-01 -4.8793e-01  8e-01  6e-16  8e-16\n",
      " 2:  2.6152e-01  1.7159e-01  9e-02  2e-16  8e-16\n",
      " 3:  2.3257e-01  2.2334e-01  9e-03  6e-17  4e-16\n",
      " 4:  2.2787e-01  2.2727e-01  6e-04  6e-17  1e-16\n",
      " 5:  2.2755e-01  2.2753e-01  2e-05  9e-17  2e-16\n",
      " 6:  2.2753e-01  2.2753e-01  2e-07  8e-17  2e-16\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.5979e-01 -8.1310e-01  1e+01  3e+00  4e+00\n",
      " 1:  3.3801e-01 -4.5327e-01  8e-01  3e-16  7e-16\n",
      " 2:  2.8772e-01  2.0169e-01  9e-02  6e-17  1e-15\n",
      " 3:  2.6033e-01  2.5214e-01  8e-03  6e-17  2e-16\n",
      " 4:  2.5627e-01  2.5575e-01  5e-04  3e-17  2e-16\n",
      " 5:  2.5598e-01  2.5596e-01  1e-05  2e-16  2e-16\n",
      " 6:  2.5597e-01  2.5597e-01  1e-07  6e-17  2e-16\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.8515e-01 -7.8521e-01  1e+01  3e+00  4e+00\n",
      " 1:  3.6702e-01 -4.1750e-01  8e-01  6e-03  8e-03\n",
      " 2:  3.1829e-01  2.3364e-01  9e-02  5e-04  7e-04\n",
      " 3:  2.9182e-01  2.8421e-01  8e-03  2e-05  3e-05\n",
      " 4:  2.8814e-01  2.8767e-01  5e-04  2e-07  2e-07\n",
      " 5:  2.8787e-01  2.8786e-01  1e-05  2e-09  3e-09\n",
      " 6:  2.8786e-01  2.8786e-01  1e-07  2e-11  3e-11\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  3.1340e-01 -7.5406e-01  1e+01  3e+00  4e+00\n",
      " 1:  3.9867e-01 -3.7919e-01  8e-01  2e-02  2e-02\n",
      " 2:  3.5366e-01  2.6784e-01  9e-02  2e-03  2e-03\n",
      " 3:  3.2743e-01  3.1999e-01  7e-03  7e-05  9e-05\n",
      " 4:  3.2390e-01  3.2346e-01  4e-04  5e-07  7e-07\n",
      " 5:  3.2365e-01  3.2364e-01  8e-06  6e-09  8e-09\n",
      " 6:  3.2364e-01  3.2364e-01  8e-08  6e-11  8e-11\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  3.4493e-01 -7.1928e-01  1e+01  3e+00  4e+00\n",
      " 1:  4.3362e-01 -3.3650e-01  9e-01  3e-02  4e-02\n",
      " 2:  3.9290e-01  3.0677e-01  9e-02  3e-03  4e-03\n",
      " 3:  3.6735e-01  3.6015e-01  7e-03  1e-04  1e-04\n",
      " 4:  3.6403e-01  3.6362e-01  4e-04  8e-07  1e-06\n",
      " 5:  3.6379e-01  3.6379e-01  7e-06  9e-09  1e-08\n",
      " 6:  3.6379e-01  3.6379e-01  7e-08  9e-11  1e-10\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  3.8019e-01 -6.8046e-01  1e+01  3e+00  4e+00\n",
      " 1:  4.7225e-01 -2.8896e-01  9e-01  4e-02  5e-02\n",
      " 2:  4.3653e-01  3.5092e-01  9e-02  3e-03  5e-03\n",
      " 3:  4.1211e-01  4.0524e-01  7e-03  1e-04  2e-04\n",
      " 4:  4.0904e-01  4.0867e-01  4e-04  1e-06  1e-06\n",
      " 5:  4.0883e-01  4.0882e-01  5e-06  1e-08  1e-08\n",
      " 6:  4.0883e-01  4.0883e-01  5e-08  1e-10  1e-10\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  4.1967e-01 -6.3715e-01  1e+01  3e+00  4e+00\n",
      " 1:  5.1499e-01 -2.3604e-01  9e-01  5e-02  6e-02\n",
      " 2:  4.8517e-01  4.0086e-01  9e-02  4e-03  5e-03\n",
      " 3:  4.6232e-01  4.5584e-01  7e-03  1e-04  2e-04\n",
      " 4:  4.5954e-01  4.5920e-01  3e-04  1e-06  1e-06\n",
      " 5:  4.5936e-01  4.5935e-01  4e-06  1e-08  1e-08\n",
      " 6:  4.5935e-01  4.5935e-01  4e-08  1e-10  1e-10\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  4.6394e-01 -5.8881e-01  1e+01  3e+00  4e+00\n",
      " 1:  5.6231e-01 -1.7716e-01  9e-01  5e-02  7e-02\n",
      " 2:  5.3952e-01  4.5722e-01  9e-02  4e-03  6e-03\n",
      " 3:  5.1867e-01  5.1261e-01  6e-03  1e-04  2e-04\n",
      " 4:  5.1620e-01  5.1590e-01  3e-04  1e-06  1e-06\n",
      " 5:  5.1605e-01  5.1604e-01  3e-06  1e-08  1e-08\n",
      " 6:  5.1605e-01  5.1605e-01  3e-08  1e-10  1e-10\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  5.1363e-01 -5.3487e-01  1e+01  3e+00  4e+00\n",
      " 1:  6.1475e-01 -1.1170e-01  9e-01  6e-02  8e-02\n",
      " 2:  6.0038e-01  5.2071e-01  9e-02  5e-03  6e-03\n",
      " 3:  5.8193e-01  5.7631e-01  6e-03  1e-04  2e-04\n",
      " 4:  5.7978e-01  5.7952e-01  3e-04  1e-06  1e-06\n",
      " 5:  5.7965e-01  5.7965e-01  3e-06  1e-08  1e-08\n",
      " 6:  5.7965e-01  5.7965e-01  3e-08  1e-10  1e-10\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  5.6944e-01 -4.7466e-01  1e+01  3e+00  4e+00\n",
      " 1:  6.7295e-01 -3.9170e-02  9e-01  7e-02  9e-02\n",
      " 2:  6.6866e-01  5.9211e-01  8e-02  5e-03  6e-03\n",
      " 3:  6.5296e-01  6.4777e-01  5e-03  1e-04  2e-04\n",
      " 4:  6.5111e-01  6.5089e-01  2e-04  1e-06  1e-06\n",
      " 5:  6.5102e-01  6.5101e-01  2e-06  1e-08  1e-08\n",
      " 6:  6.5101e-01  6.5101e-01  2e-08  1e-10  1e-10\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  6.3218e-01 -4.0745e-01  1e+01  3e+00  4e+00\n",
      " 1:  7.3760e-01  4.1279e-02  9e-01  7e-02  1e-01\n",
      " 2:  7.4545e-01  6.7232e-01  8e-02  4e-03  6e-03\n",
      " 3:  7.3271e-01  7.2794e-01  5e-03  1e-04  2e-04\n",
      " 4:  7.3116e-01  7.3096e-01  2e-04  1e-06  1e-06\n",
      " 5:  7.3108e-01  7.3108e-01  2e-06  1e-08  1e-08\n",
      " 6:  7.3108e-01  7.3108e-01  2e-08  1e-10  1e-10\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  7.0273e-01 -3.3239e-01  1e+01  3e+00  4e+00\n",
      " 1:  8.0952e-01  1.3052e-01  9e-01  8e-02  1e-01\n",
      " 2:  8.3200e-01  7.6232e-01  8e-02  4e-03  6e-03\n",
      " 3:  8.2224e-01  8.1788e-01  4e-03  1e-04  1e-04\n",
      " 4:  8.2097e-01  8.2080e-01  2e-04  9e-07  1e-06\n",
      " 5:  8.2092e-01  8.2092e-01  2e-06  9e-09  1e-08\n",
      " 6:  8.2092e-01  8.2092e-01  2e-08  9e-11  1e-10\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  7.8208e-01 -2.4852e-01  1e+01  3e+00  4e+00\n",
      " 1:  8.8914e-01  2.2798e-01  9e-01  9e-02  1e-01\n",
      " 2:  9.3123e-01  8.6097e-01  8e-02  4e-03  6e-03\n",
      " 3:  9.2293e-01  9.1857e-01  4e-03  1e-04  1e-04\n",
      " 4:  9.2176e-01  9.2160e-01  2e-04  9e-07  1e-06\n",
      " 5:  9.2172e-01  9.2172e-01  2e-06  9e-09  1e-08\n",
      " 6:  9.2172e-01  9.2172e-01  2e-08  9e-11  1e-10\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  8.7135e-01 -1.5480e-01  1e+01  3e+00  4e+00\n",
      " 1:  9.7700e-01  3.3289e-01  1e+00  1e-01  2e-01\n",
      " 2:  1.0467e+00  9.6734e-01  9e-02  5e-03  6e-03\n",
      " 3:  1.0363e+00  1.0312e+00  5e-03  1e-04  1e-04\n",
      " 4:  1.0349e+00  1.0347e+00  2e-04  9e-07  1e-06\n",
      " 5:  1.0348e+00  1.0348e+00  2e-06  9e-09  1e-08\n",
      " 6:  1.0348e+00  1.0348e+00  2e-08  9e-11  1e-10\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  9.7178e-01 -5.0003e-02  1e+01  3e+00  4e+00\n",
      " 1:  1.0753e+00  4.4924e-01  1e+00  2e-01  2e-01\n",
      " 2:  1.1751e+00  1.0886e+00  9e-02  4e-03  5e-03\n",
      " 3:  1.1633e+00  1.1579e+00  6e-03  8e-05  1e-04\n",
      " 4:  1.1618e+00  1.1616e+00  2e-04  7e-07  9e-07\n",
      " 5:  1.1617e+00  1.1617e+00  2e-06  7e-09  9e-09\n",
      " 6:  1.1617e+00  1.1617e+00  2e-08  7e-11  9e-11\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.0847e+00  6.7211e-02  1e+01  3e+00  4e+00\n",
      " 1:  1.1856e+00  5.7828e-01  1e+00  2e-01  2e-01\n",
      " 2:  1.3144e+00  1.2141e+00  1e-01  5e-03  7e-03\n",
      " 3:  1.3058e+00  1.2996e+00  6e-03  1e-04  2e-04\n",
      " 4:  1.3041e+00  1.3039e+00  2e-04  1e-06  1e-06\n",
      " 5:  1.3041e+00  1.3041e+00  2e-06  1e-08  1e-08\n",
      " 6:  1.3041e+00  1.3041e+00  2e-08  1e-10  1e-10\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.2118e+00  1.9837e-01  1e+01  3e+00  4e+00\n",
      " 1:  1.3093e+00  7.2144e-01  1e+00  2e-01  3e-01\n",
      " 2:  1.4617e+00  1.3397e+00  2e-01  1e-02  2e-02\n",
      " 3:  1.4657e+00  1.4580e+00  8e-03  3e-04  3e-04\n",
      " 4:  1.4639e+00  1.4636e+00  3e-04  2e-06  3e-06\n",
      " 5:  1.4638e+00  1.4638e+00  3e-06  2e-08  3e-08\n",
      " 6:  1.4638e+00  1.4638e+00  3e-08  2e-10  3e-10\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.3547e+00  3.4517e-01  1e+01  3e+00  4e+00\n",
      " 1:  1.4484e+00  8.8030e-01  1e+00  2e-01  3e-01\n",
      " 2:  1.6248e+00  1.4902e+00  2e-01  2e-02  3e-02\n",
      " 3:  1.6455e+00  1.6360e+00  1e-02  3e-04  4e-04\n",
      " 4:  1.6432e+00  1.6428e+00  4e-04  2e-06  3e-06\n",
      " 5:  1.6431e+00  1.6431e+00  4e-06  2e-08  3e-08\n",
      " 6:  1.6431e+00  1.6431e+00  4e-08  2e-10  3e-10\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.5153e+00  5.0955e-01  1e+01  3e+00  4e+00\n",
      " 1:  1.6049e+00  1.0567e+00  1e+00  3e-01  4e-01\n",
      " 2:  1.8088e+00  1.6641e+00  2e-01  3e-02  4e-02\n",
      " 3:  1.8480e+00  1.8352e+00  1e-02  2e-04  3e-04\n",
      " 4:  1.8443e+00  1.8438e+00  5e-04  2e-06  3e-06\n",
      " 5:  1.8442e+00  1.8442e+00  6e-06  2e-08  3e-08\n",
      " 6:  1.8442e+00  1.8442e+00  6e-08  2e-10  3e-10\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.6958e+00  6.9365e-01  1e+01  3e+00  4e+00\n",
      " 1:  1.7810e+00  1.2526e+00  2e+00  3e-01  4e-01\n",
      " 2:  2.0166e+00  1.8625e+00  3e-01  4e-02  5e-02\n",
      " 3:  2.0763e+00  2.0584e+00  2e-02  1e-16  3e-15\n",
      " 4:  2.0700e+00  2.0693e+00  7e-04  8e-17  1e-15\n",
      " 5:  2.0698e+00  2.0698e+00  7e-06  2e-16  1e-15\n",
      " 6:  2.0698e+00  2.0698e+00  7e-08  4e-17  1e-15\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.8988e+00  8.9990e-01  1e+01  3e+00  4e+00\n",
      " 1:  1.9792e+00  1.4705e+00  2e+00  3e-01  4e-01\n",
      " 2:  2.2508e+00  2.0885e+00  3e-01  5e-02  6e-02\n",
      " 3:  2.3332e+00  2.3064e+00  3e-02  2e-16  8e-15\n",
      " 4:  2.3233e+00  2.3224e+00  9e-04  1e-16  2e-15\n",
      " 5:  2.3230e+00  2.3230e+00  9e-06  1e-16  2e-15\n",
      " 6:  2.3230e+00  2.3230e+00  9e-08  1e-16  2e-15\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.1267e+00  1.1310e+00  1e+01  3e+00  4e+00\n",
      " 1:  2.2023e+00  1.7128e+00  2e+00  4e-01  5e-01\n",
      " 2:  2.5153e+00  2.3443e+00  4e-01  6e-02  7e-02\n",
      " 3:  2.6229e+00  2.5836e+00  4e-02  1e-16  2e-15\n",
      " 4:  2.6075e+00  2.6063e+00  1e-03  3e-17  3e-15\n",
      " 5:  2.6070e+00  2.6070e+00  1e-05  1e-16  2e-15\n",
      " 6:  2.6070e+00  2.6070e+00  1e-07  3e-17  3e-15\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.3829e+00  1.3901e+00  1e+01  3e+00  4e+00\n",
      " 1:  2.4536e+00  1.9827e+00  2e+00  4e-01  5e-01\n",
      " 2:  2.8144e+00  2.6333e+00  4e-01  6e-02  8e-02\n",
      " 3:  2.9498e+00  2.8934e+00  6e-02  1e-16  2e-14\n",
      " 4:  2.9265e+00  2.9249e+00  2e-03  6e-17  3e-15\n",
      " 5:  2.9258e+00  2.9258e+00  2e-05  2e-16  3e-15\n",
      " 6:  2.9258e+00  2.9258e+00  2e-07  8e-17  3e-15\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.6705e+00  1.6804e+00  1e+01  3e+00  4e+00\n",
      " 1:  2.7364e+00  2.2834e+00  2e+00  4e-01  5e-01\n",
      " 2:  3.1531e+00  2.9591e+00  5e-01  7e-02  9e-02\n",
      " 3:  3.3192e+00  3.2394e+00  8e-02  2e-16  1e-14\n",
      " 4:  3.2845e+00  3.2823e+00  2e-03  1e-16  4e-15\n",
      " 5:  3.2834e+00  3.2834e+00  2e-05  2e-16  5e-15\n",
      " 6:  3.2834e+00  3.2834e+00  2e-07  4e-17  6e-15\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.9936e+00  2.0060e+00  1e+01  3e+00  4e+00\n",
      " 1:  3.0546e+00  2.6188e+00  2e+00  4e-01  6e-01\n",
      " 2:  3.5373e+00  3.3261e+00  6e-01  8e-02  1e-01\n",
      " 3:  3.7370e+00  3.6257e+00  1e-01  3e-17  3e-14\n",
      " 4:  3.6866e+00  3.6833e+00  3e-03  1e-16  4e-15\n",
      " 5:  3.6847e+00  3.6846e+00  3e-05  2e-16  3e-15\n",
      " 6:  3.6846e+00  3.6846e+00  3e-07  8e-17  5e-15\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  3.3564e+00  2.3711e+00  1e+01  3e+00  4e+00\n",
      " 1:  3.4127e+00  2.9931e+00  2e+00  4e-01  6e-01\n",
      " 2:  3.9735e+00  3.7391e+00  7e-01  8e-02  1e-01\n",
      " 3:  4.2107e+00  4.0562e+00  2e-01  2e-16  2e-14\n",
      " 4:  4.1384e+00  4.1332e+00  5e-03  1e-16  5e-15\n",
      " 5:  4.1349e+00  4.1348e+00  5e-05  1e-16  5e-15\n",
      " 6:  4.1348e+00  4.1348e+00  5e-07  3e-17  4e-15\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  3.7636e+00  2.7805e+00  1e+01  3e+00  4e+00\n",
      " 1:  3.8155e+00  3.4112e+00  2e+00  5e-01  6e-01\n",
      " 2:  4.4697e+00  4.2036e+00  8e-01  9e-02  1e-01\n",
      " 3:  4.7494e+00  4.5350e+00  2e-01  2e-16  9e-15\n",
      " 4:  4.6465e+00  4.6380e+00  8e-03  1e-16  4e-15\n",
      " 5:  4.6401e+00  4.6400e+00  9e-05  1e-16  3e-15\n",
      " 6:  4.6400e+00  4.6400e+00  9e-07  8e-17  4e-15\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  4.2208e+00  3.2396e+00  1e+01  3e+00  4e+00\n",
      " 1:  4.2685e+00  3.8785e+00  2e+00  5e-01  6e-01\n",
      " 2:  5.0351e+00  4.7256e+00  9e-01  1e-01  1e-01\n",
      " 3:  5.3649e+00  5.0655e+00  3e-01  2e-16  7e-15\n",
      " 4:  5.2186e+00  5.2042e+00  1e-02  2e-16  7e-15\n",
      " 5:  5.2069e+00  5.2067e+00  2e-04  8e-17  4e-15\n",
      " 6:  5.2068e+00  5.2068e+00  2e-06  8e-17  7e-15\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  4.7341e+00  3.7546e+00  1e+01  3e+00  4e+00\n",
      " 1:  4.7776e+00  4.4010e+00  2e+00  5e-01  6e-01\n",
      " 2:  5.6806e+00  5.3119e+00  1e+00  1e-01  1e-01\n",
      " 3:  6.0680e+00  5.6494e+00  4e-01  8e-04  1e-03\n",
      " 4:  5.8637e+00  5.8391e+00  2e-02  1e-05  2e-05\n",
      " 5:  5.8430e+00  5.8427e+00  3e-04  1e-07  2e-07\n",
      " 6:  5.8427e+00  5.8427e+00  3e-06  1e-09  2e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  5.3101e+00  4.3323e+00  1e+01  3e+00  4e+00\n",
      " 1:  5.3499e+00  4.9857e+00  2e+00  5e-01  7e-01\n",
      " 2:  6.4194e+00  5.9695e+00  1e+00  1e-01  1e-01\n",
      " 3:  6.8735e+00  6.2895e+00  6e-01  3e-03  4e-03\n",
      " 4:  6.5923e+00  6.5506e+00  4e-02  4e-05  5e-05\n",
      " 5:  6.5568e+00  6.5562e+00  6e-04  4e-07  6e-07\n",
      " 6:  6.5562e+00  6.5562e+00  6e-06  4e-09  6e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  5.9566e+00  4.9803e+00  1e+01  3e+00  4e+00\n",
      " 1:  5.9928e+00  5.6401e+00  2e+00  5e-01  7e-01\n",
      " 2:  7.2670e+00  6.7059e+00  1e+00  1e-01  2e-01\n",
      " 3:  7.8127e+00  6.9854e+00  9e-01  5e-03  7e-03\n",
      " 4:  7.4174e+00  7.3475e+00  7e-02  7e-05  9e-05\n",
      " 5:  7.3582e+00  7.3567e+00  1e-03  7e-07  1e-06\n",
      " 6:  7.3568e+00  7.3568e+00  1e-05  7e-09  1e-08\n",
      " 7:  7.3568e+00  7.3568e+00  1e-07  7e-11  1e-10\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  6.6822e+00  5.7072e+00  1e+01  3e+00  4e+00\n",
      " 1:  6.7151e+00  6.3730e+00  2e+00  5e-01  7e-01\n",
      " 2:  8.2420e+00  7.5291e+00  2e+00  1e-01  2e-01\n",
      " 3:  8.9222e+00  7.7294e+00  1e+00  8e-03  1e-02\n",
      " 4:  8.3544e+00  8.2395e+00  1e-01  6e-05  8e-05\n",
      " 5:  8.2583e+00  8.2549e+00  3e-03  7e-07  9e-07\n",
      " 6:  8.2552e+00  8.2551e+00  3e-05  7e-09  9e-09\n",
      " 7:  8.2551e+00  8.2551e+00  3e-07  7e-11  9e-11\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  7.4965e+00  6.5227e+00  1e+01  3e+00  4e+00\n",
      " 1:  7.5263e+00  7.1939e+00  2e+00  5e-01  7e-01\n",
      " 2:  9.3667e+00  8.4471e+00  2e+00  1e-01  2e-01\n",
      " 3:  1.0254e+01  8.5033e+00  2e+00  1e-02  1e-02\n",
      " 4:  9.4237e+00  9.2337e+00  2e-01  8e-17  1e-14\n",
      " 5:  9.2702e+00  9.2625e+00  8e-03  1e-16  5e-15\n",
      " 6:  9.2631e+00  9.2630e+00  8e-05  4e-17  1e-14\n",
      " 7:  9.2630e+00  9.2630e+00  8e-07  1e-16  1e-14\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  8.4103e+00  7.4376e+00  1e+01  3e+00  4e+00\n",
      " 1:  8.4373e+00  8.1138e+00  2e+00  5e-01  7e-01\n",
      " 2:  1.0667e+01  9.4681e+00  3e+00  1e-01  2e-01\n",
      " 3:  1.1884e+01  9.2719e+00  3e+00  1e-02  2e-02\n",
      " 4:  1.0658e+01  1.0328e+01  3e-01  1e-16  1e-14\n",
      " 5:  1.0410e+01  1.0393e+01  2e-02  6e-17  1e-14\n",
      " 6:  1.0394e+01  1.0394e+01  2e-04  1e-16  1e-14\n",
      " 7:  1.0394e+01  1.0394e+01  2e-06  1e-16  1e-14\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  9.4358e+00  8.4640e+00  1e+01  3e+00  4e+00\n",
      " 1:  9.4601e+00  9.1447e+00  2e+00  5e-01  7e-01\n",
      " 2:  1.2176e+01  1.0600e+01  3e+00  2e-01  2e-01\n",
      " 3:  1.3923e+01  9.9731e+00  5e+00  2e-02  3e-02\n",
      " 4:  1.2091e+01  1.1520e+01  6e-01  3e-17  1e-14\n",
      " 5:  1.1695e+01  1.1660e+01  4e-02  1e-16  9e-15\n",
      " 6:  1.1663e+01  1.1663e+01  4e-04  1e-16  1e-14\n",
      " 7:  1.1663e+01  1.1663e+01  4e-06  8e-17  2e-14\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.0586e+01  9.6156e+00  1e+01  3e+00  4e+00\n",
      " 1:  1.0608e+01  1.0300e+01  2e+00  6e-01  7e-01\n",
      " 2:  1.3928e+01  1.1850e+01  4e+00  2e-01  2e-01\n",
      " 3:  1.6533e+01  1.0504e+01  7e+00  3e-02  3e-02\n",
      " 4:  1.3770e+01  1.2779e+01  1e+00  1e-16  2e-14\n",
      " 5:  1.3150e+01  1.3080e+01  7e-02  6e-17  1e-14\n",
      " 6:  1.3087e+01  1.3086e+01  1e-03  6e-17  1e-14\n",
      " 7:  1.3086e+01  1.3086e+01  1e-05  1e-16  2e-14\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.1878e+01  1.0908e+01  1e+01  3e+00  4e+00\n",
      " 1:  1.1897e+01  1.1596e+01  2e+00  6e-01  7e-01\n",
      " 2:  1.5968e+01  1.3226e+01  5e+00  2e-01  2e-01\n",
      " 3:  1.9940e+01  1.0707e+01  1e+01  3e-02  4e-02\n",
      " 4:  1.5757e+01  1.4024e+01  2e+00  8e-17  3e-14\n",
      " 5:  1.4808e+01  1.4667e+01  1e-01  2e-16  1e-14\n",
      " 6:  1.4687e+01  1.4684e+01  3e-03  9e-17  2e-14\n",
      " 7:  1.4684e+01  1.4684e+01  3e-05  8e-17  2e-14\n",
      " 8:  1.4684e+01  1.4684e+01  3e-07  1e-16  1e-14\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.3326e+01  1.2357e+01  1e+01  3e+00  4e+00\n",
      " 1:  1.3344e+01  1.3049e+01  2e+00  6e-01  7e-01\n",
      " 2:  1.8345e+01  1.4735e+01  6e+00  2e-01  2e-01\n",
      " 3:  2.4459e+01  1.0347e+01  2e+01  4e-02  5e-02\n",
      " 4:  1.8138e+01  1.5024e+01  3e+00  3e-17  5e-14\n",
      " 5:  1.6725e+01  1.6416e+01  3e-01  3e-17  2e-14\n",
      " 6:  1.6485e+01  1.6475e+01  1e-02  2e-16  2e-14\n",
      " 7:  1.6476e+01  1.6476e+01  1e-04  6e-17  2e-14\n",
      " 8:  1.6476e+01  1.6476e+01  1e-06  1e-16  1e-14\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.4952e+01  1.3983e+01  1e+01  3e+00  4e+00\n",
      " 1:  1.4968e+01  1.4678e+01  2e+00  6e-01  8e-01\n",
      " 2:  2.1112e+01  1.6382e+01  8e+00  2e-01  2e-01\n",
      " 3:  3.0508e+01  9.1020e+00  2e+01  5e-02  6e-02\n",
      " 4:  2.1039e+01  1.5072e+01  6e+00  8e-17  2e-13\n",
      " 5:  1.8939e+01  1.8310e+01  6e-01  1e-16  4e-14\n",
      " 6:  1.8511e+01  1.8481e+01  3e-02  3e-17  8e-15\n",
      " 7:  1.8487e+01  1.8487e+01  3e-04  1e-16  2e-14\n",
      " 8:  1.8487e+01  1.8487e+01  3e-06  1e-16  1e-14\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -1.1163e-02 -1.0367e+00  1e+00  1e-16  3e+00\n",
      " 1: -1.2055e-02 -4.6686e-02  3e-02  4e-17  1e-01\n",
      " 2: -3.4205e-02 -4.0258e-02  6e-03  3e-16  3e-03\n",
      " 3: -3.5770e-02 -3.6394e-02  6e-04  2e-16  2e-04\n",
      " 4: -3.6266e-02 -3.6301e-02  4e-05  2e-16  3e-17\n",
      " 5: -3.6298e-02 -3.6299e-02  4e-07  2e-16  5e-17\n",
      " 6: -3.6299e-02 -3.6299e-02  4e-09  5e-17  1e-17\n",
      "Optimal solution found.\n"
     ]
    },
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] File weak_risks_returns.csv does not exist: 'weak_risks_returns.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-6123ee489c74>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     42\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     43\u001b[0m \u001b[1;31m# 11 & 12. Compare to original\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 44\u001b[1;33m \u001b[0mweak_EF\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'weak_risks_returns.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     45\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mweak_EF\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Risks'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mweak_EF\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Returns'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'g-o'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     46\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[0;32m    674\u001b[0m         )\n\u001b[0;32m    675\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 676\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    677\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    678\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    446\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    447\u001b[0m     \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 448\u001b[1;33m     \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    449\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    450\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m    878\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    879\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 880\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    881\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    882\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m   1112\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"c\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1113\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"c\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1114\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1115\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1116\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"python\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m   1889\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"usecols\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1890\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1891\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1892\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1893\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] File weak_risks_returns.csv does not exist: 'weak_risks_returns.csv'"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from rf import return_portfolios, optimal_portfolio\n",
    "import numpy as np\n",
    "\n",
    "# 10. Change the path variable\n",
    "path = 'stock_data2.csv'\n",
    "\n",
    "# Load the stock data\n",
    "stock_data = pd.read_csv(path)\n",
    "\n",
    "# Find the quarterly for each period\n",
    "selected=list(stock_data.columns[1:])\n",
    "returns_quarterly = stock_data[selected].pct_change()\n",
    "\n",
    "# Find the expected returns \n",
    "expected_returns = returns_quarterly.mean()\n",
    "\n",
    "# Find the covariance \n",
    "cov_quarterly = returns_quarterly.cov()\n",
    "\n",
    "# Find a set of random portfolios\n",
    "random_portfolios = return_portfolios(expected_returns, cov_quarterly) \n",
    "\n",
    "# Plot the set of random portfolios\n",
    "#random_portfolios.plot.scatter(x='Volatility', y='Returns', fontsize=12)\n",
    "\n",
    "# Calculate the set of portfolios on the EF\n",
    "weights, returns, risks = optimal_portfolio(returns_quarterly[1:])\n",
    "\n",
    "# Plot the set of portfolios on the EF\n",
    "plt.plot(risks, returns, 'y-o')\n",
    "plt.ylabel('Expected Returns',fontsize=14)\n",
    "plt.xlabel('Volatility (Std. Deviation)',fontsize=14)\n",
    "plt.title('Efficient Frontier', fontsize=24)\n",
    "\n",
    "\n",
    "# Compare the set of portfolios on the EF to the \n",
    "single_asset_std=np.sqrt(np.diagonal(cov_quarterly))\n",
    "plt.scatter(single_asset_std,expected_returns,marker='X',color='red',s=200)\n",
    "\n",
    "\n",
    "# 11 & 12. Compare to original\n",
    "weak_EF = pd.read_csv('weak_risks_returns.csv')\n",
    "plt.plot(weak_EF['Risks'], weak_EF['Returns'], 'g-o')\n",
    "\n",
    "strong_EF = pd.read_csv('strong_risks_returns.csv')\n",
    "plt.plot(strong_EF['Risks'], strong_EF['Returns'], 'k-x')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PFE    0.015530\n",
      "TGT    0.014783\n",
      "M      0.021962\n",
      "VZ     0.004856\n",
      "JPM    0.034781\n",
      "MRO   -0.008010\n",
      "KO    -0.002050\n",
      "PG     0.004132\n",
      "CVS    0.016124\n",
      "HPQ   -0.000237\n",
      "dtype: float64\n",
      "          PFE       TGT         M        VZ       JPM       MRO        KO  \\\n",
      "PFE  0.010005  0.001738  0.005978  0.004071  0.007016  0.009721  0.002086   \n",
      "TGT  0.001738  0.012874  0.015762  0.002188  0.007805  0.002239  0.002534   \n",
      "M    0.005978  0.015762  0.038909  0.002804  0.016439  0.015110  0.005189   \n",
      "VZ   0.004071  0.002188  0.002804  0.006958 -0.000407  0.005276  0.002586   \n",
      "JPM  0.007016  0.007805  0.016439 -0.000407  0.023837  0.013848  0.002979   \n",
      "MRO  0.009721  0.002239  0.015110  0.005276  0.013848  0.043166 -0.001516   \n",
      "KO   0.002086  0.002534  0.005189  0.002586  0.002979 -0.001516  0.011021   \n",
      "PG   0.004297  0.003148  0.005853  0.002094  0.006364  0.002834  0.001255   \n",
      "CVS  0.004300  0.005384  0.012501  0.002052  0.005041  0.003689  0.003223   \n",
      "HPQ  0.006996  0.007774  0.018937  0.003076  0.011523  0.015733  0.005824   \n",
      "\n",
      "           PG       CVS       HPQ  \n",
      "PFE  0.004297  0.004300  0.006996  \n",
      "TGT  0.003148  0.005384  0.007774  \n",
      "M    0.005853  0.012501  0.018937  \n",
      "VZ   0.002094  0.002052  0.003076  \n",
      "JPM  0.006364  0.005041  0.011523  \n",
      "MRO  0.002834  0.003689  0.015733  \n",
      "KO   0.001255  0.003223  0.005824  \n",
      "PG   0.006715  0.003827  0.006734  \n",
      "CVS  0.003827  0.011374  0.008161  \n",
      "HPQ  0.006734  0.008161  0.040514  \n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -3.2410e-03 -1.0209e+00  1e+00  8e-17  3e+00\n",
      " 1: -3.4456e-03 -3.0860e-02  3e-02  2e-16  7e-02\n",
      " 2: -1.6310e-02 -2.1002e-02  5e-03  1e-16  4e-18\n",
      " 3: -1.9973e-02 -2.0060e-02  9e-05  1e-16  1e-17\n",
      " 4: -2.0016e-02 -2.0017e-02  9e-07  8e-17  1e-17\n",
      " 5: -2.0016e-02 -2.0016e-02  9e-09  1e-16  1e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -3.1701e-03 -1.0207e+00  1e+00  2e-16  3e+00\n",
      " 1: -3.3741e-03 -3.0726e-02  3e-02  7e-17  7e-02\n",
      " 2: -1.6031e-02 -2.0753e-02  5e-03  2e-16  8e-18\n",
      " 3: -1.9734e-02 -1.9823e-02  9e-05  4e-16  1e-17\n",
      " 4: -1.9778e-02 -1.9779e-02  9e-07  5e-17  9e-18\n",
      " 5: -1.9779e-02 -1.9779e-02  9e-09  1e-16  2e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -3.0906e-03 -1.0206e+00  1e+00  1e-16  3e+00\n",
      " 1: -3.2940e-03 -3.0575e-02  3e-02  1e-16  7e-02\n",
      " 2: -1.5723e-02 -2.0475e-02  5e-03  1e-16  2e-17\n",
      " 3: -1.9465e-02 -1.9557e-02  9e-05  2e-17  9e-18\n",
      " 4: -1.9512e-02 -1.9513e-02  9e-07  1e-16  7e-18\n",
      " 5: -1.9512e-02 -1.9512e-02  9e-09  3e-16  1e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -3.0015e-03 -1.0204e+00  1e+00  8e-17  3e+00\n",
      " 1: -3.2042e-03 -3.0407e-02  3e-02  1e-16  7e-02\n",
      " 2: -1.5384e-02 -2.0165e-02  5e-03  2e-16  2e-17\n",
      " 3: -1.9163e-02 -1.9259e-02  1e-04  1e-16  8e-18\n",
      " 4: -1.9213e-02 -1.9214e-02  1e-06  2e-16  1e-17\n",
      " 5: -1.9214e-02 -1.9214e-02  1e-08  3e-16  5e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -2.9017e-03 -1.0202e+00  1e+00  2e-16  3e+00\n",
      " 1: -3.1037e-03 -3.0217e-02  3e-02  8e-17  7e-02\n",
      " 2: -1.5012e-02 -1.9819e-02  5e-03  6e-17  2e-17\n",
      " 3: -1.8819e-02 -1.8925e-02  1e-04  6e-17  2e-17\n",
      " 4: -1.8878e-02 -1.8879e-02  1e-06  1e-16  5e-18\n",
      " 5: -1.8878e-02 -1.8878e-02  1e-08  1e-16  8e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -2.7897e-03 -1.0200e+00  1e+00  2e-16  3e+00\n",
      " 1: -2.9910e-03 -3.0005e-02  3e-02  9e-17  7e-02\n",
      " 2: -1.4605e-02 -1.9434e-02  5e-03  2e-16  5e-18\n",
      " 3: -1.8425e-02 -1.8552e-02  1e-04  1e-16  5e-18\n",
      " 4: -1.8501e-02 -1.8503e-02  1e-06  1e-16  8e-18\n",
      " 5: -1.8502e-02 -1.8502e-02  1e-08  1e-16  2e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -2.6643e-03 -1.0198e+00  1e+00  1e-16  3e+00\n",
      " 1: -2.8648e-03 -2.9767e-02  3e-02  4e-17  7e-02\n",
      " 2: -1.4161e-02 -1.9006e-02  5e-03  1e-16  1e-17\n",
      " 3: -1.7980e-02 -1.8133e-02  2e-04  2e-16  9e-18\n",
      " 4: -1.8079e-02 -1.8081e-02  2e-06  3e-17  1e-17\n",
      " 5: -1.8080e-02 -1.8080e-02  2e-08  9e-17  1e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -2.5238e-03 -1.0195e+00  1e+00  2e-16  3e+00\n",
      " 1: -2.7234e-03 -2.9501e-02  3e-02  6e-17  7e-02\n",
      " 2: -1.3678e-02 -1.8531e-02  5e-03  2e-16  1e-17\n",
      " 3: -1.7477e-02 -1.7663e-02  2e-04  2e-16  1e-17\n",
      " 4: -1.7605e-02 -1.7607e-02  2e-06  1e-16  6e-18\n",
      " 5: -1.7606e-02 -1.7606e-02  2e-08  2e-16  4e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -2.3663e-03 -1.0192e+00  1e+00  3e-16  3e+00\n",
      " 1: -2.5651e-03 -2.9202e-02  3e-02  1e-16  7e-02\n",
      " 2: -1.3154e-02 -1.8004e-02  5e-03  2e-16  2e-17\n",
      " 3: -1.6910e-02 -1.7136e-02  2e-04  1e-16  1e-17\n",
      " 4: -1.7073e-02 -1.7076e-02  2e-06  1e-16  8e-18\n",
      " 5: -1.7075e-02 -1.7075e-02  2e-08  2e-16  1e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -2.1899e-03 -1.0189e+00  1e+00  3e-17  3e+00\n",
      " 1: -2.3879e-03 -2.8867e-02  3e-02  6e-17  7e-02\n",
      " 2: -1.2590e-02 -1.7422e-02  5e-03  1e-16  1e-17\n",
      " 3: -1.6271e-02 -1.6544e-02  3e-04  3e-16  1e-17\n",
      " 4: -1.6476e-02 -1.6479e-02  3e-06  2e-16  1e-17\n",
      " 5: -1.6479e-02 -1.6479e-02  3e-08  1e-16  4e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -1.9923e-03 -1.0185e+00  1e+00  1e-16  3e+00\n",
      " 1: -2.1896e-03 -2.8491e-02  3e-02  6e-17  7e-02\n",
      " 2: -1.1983e-02 -1.6780e-02  5e-03  1e-16  1e-17\n",
      " 3: -1.5555e-02 -1.5881e-02  3e-04  3e-16  7e-18\n",
      " 4: -1.5806e-02 -1.5810e-02  4e-06  1e-16  4e-18\n",
      " 5: -1.5810e-02 -1.5810e-02  4e-08  1e-16  4e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -1.7712e-03 -1.0181e+00  1e+00  1e-16  3e+00\n",
      " 1: -1.9678e-03 -2.8070e-02  3e-02  9e-17  7e-02\n",
      " 2: -1.1336e-02 -1.6074e-02  5e-03  1e-16  9e-18\n",
      " 3: -1.4758e-02 -1.5139e-02  4e-04  2e-16  1e-17\n",
      " 4: -1.5052e-02 -1.5060e-02  7e-06  3e-16  8e-18\n",
      " 5: -1.5059e-02 -1.5059e-02  7e-08  2e-17  2e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -1.5236e-03 -1.0177e+00  1e+00  1e-16  3e+00\n",
      " 1: -1.7197e-03 -2.7598e-02  3e-02  7e-17  7e-02\n",
      " 2: -1.0648e-02 -1.5302e-02  5e-03  1e-16  7e-18\n",
      " 3: -1.3884e-02 -1.4313e-02  4e-04  1e-16  9e-18\n",
      " 4: -1.4202e-02 -1.4218e-02  2e-05  9e-17  6e-18\n",
      " 5: -1.4217e-02 -1.4217e-02  2e-07  6e-17  4e-18\n",
      " 6: -1.4217e-02 -1.4217e-02  2e-09  7e-17  4e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -1.2465e-03 -1.0171e+00  1e+00  3e-17  3e+00\n",
      " 1: -1.4423e-03 -2.7070e-02  3e-02  6e-17  7e-02\n",
      " 2: -9.9237e-03 -1.4460e-02  5e-03  3e-16  1e-17\n",
      " 3: -1.2948e-02 -1.3401e-02  5e-04  2e-16  1e-17\n",
      " 4: -1.3245e-02 -1.3275e-02  3e-05  1e-16  9e-18\n",
      " 5: -1.3270e-02 -1.3272e-02  1e-06  5e-17  4e-18\n",
      " 6: -1.3272e-02 -1.3272e-02  1e-08  3e-16  4e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -9.3656e-04 -1.0165e+00  1e+00  3e-17  3e+00\n",
      " 1: -1.1323e-03 -2.6478e-02  3e-02  7e-17  7e-02\n",
      " 2: -9.1659e-03 -1.3549e-02  4e-03  1e-16  2e-17\n",
      " 3: -1.1979e-02 -1.2413e-02  4e-04  1e-16  4e-18\n",
      " 4: -1.2227e-02 -1.2251e-02  2e-05  3e-16  7e-18\n",
      " 5: -1.2236e-02 -1.2236e-02  9e-07  2e-16  6e-18\n",
      " 6: -1.2236e-02 -1.2236e-02  9e-09  2e-16  4e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -5.8993e-04 -1.0159e+00  1e+00  3e-17  3e+00\n",
      " 1: -7.8618e-04 -2.5816e-02  3e-02  7e-17  7e-02\n",
      " 2: -8.3802e-03 -1.2569e-02  4e-03  8e-17  1e-17\n",
      " 3: -1.1020e-02 -1.1379e-02  4e-04  1e-16  6e-18\n",
      " 4: -1.1244e-02 -1.1249e-02  4e-06  1e-16  8e-18\n",
      " 5: -1.1247e-02 -1.1247e-02  4e-08  2e-16  6e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -2.0243e-04 -1.0152e+00  1e+00  6e-17  3e+00\n",
      " 1: -3.9975e-04 -2.5074e-02  2e-02  6e-17  7e-02\n",
      " 2: -7.5737e-03 -1.1524e-02  4e-03  1e-16  6e-18\n",
      " 3: -1.0088e-02 -1.0367e-02  3e-04  1e-16  1e-17\n",
      " 4: -1.0318e-02 -1.0321e-02  3e-06  1e-16  9e-18\n",
      " 5: -1.0321e-02 -1.0321e-02  3e-08  8e-17  4e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.3057e-04 -1.0143e+00  1e+00  6e-17  3e+00\n",
      " 1:  3.1347e-05 -2.4244e-02  2e-02  6e-17  6e-02\n",
      " 2: -6.7196e-03 -1.0504e-02  4e-03  1e-16  4e-04\n",
      " 3: -9.1069e-03 -9.4817e-03  4e-04  1e-16  3e-05\n",
      " 4: -9.4395e-03 -9.4435e-03  4e-06  2e-16  3e-07\n",
      " 5: -9.4430e-03 -9.4430e-03  4e-08  2e-16  3e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  7.1417e-04 -1.0134e+00  1e+00  0e+00  3e+00\n",
      " 1:  5.1196e-04 -2.3315e-02  2e-02  2e-16  6e-02\n",
      " 2: -5.8592e-03 -9.4635e-03  4e-03  1e-16  9e-04\n",
      " 3: -8.3563e-03 -8.6320e-03  3e-04  1e-16  4e-05\n",
      " 4: -8.6002e-03 -8.6031e-03  3e-06  1e-16  4e-07\n",
      " 5: -8.6027e-03 -8.6028e-03  3e-08  1e-16  4e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.2540e-03 -1.0124e+00  1e+00  3e-17  3e+00\n",
      " 1:  1.0474e-03 -2.2277e-02  2e-02  7e-17  6e-02\n",
      " 2: -5.0149e-03 -8.3953e-03  3e-03  7e-17  1e-03\n",
      " 3: -7.6692e-03 -7.8662e-03  2e-04  2e-16  4e-18\n",
      " 4: -7.7875e-03 -7.7896e-03  2e-06  2e-16  6e-18\n",
      " 5: -7.7888e-03 -7.7888e-03  2e-08  9e-17  4e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.8563e-03 -1.0112e+00  1e+00  6e-17  3e+00\n",
      " 1:  1.6434e-03 -2.1116e-02  2e-02  1e-16  6e-02\n",
      " 2: -4.1732e-03 -7.3864e-03  3e-03  2e-16  2e-03\n",
      " 3: -6.8423e-03 -7.0944e-03  3e-04  2e-16  9e-18\n",
      " 4: -6.9887e-03 -6.9914e-03  3e-06  1e-16  4e-18\n",
      " 5: -6.9904e-03 -6.9904e-03  3e-08  1e-16  4e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.5278e-03 -1.0099e+00  1e+00  8e-17  3e+00\n",
      " 1:  2.3061e-03 -1.9818e-02  2e-02  1e-16  6e-02\n",
      " 2: -3.1980e-03 -6.8498e-03  4e-03  1e-16  4e-03\n",
      " 3: -6.0025e-03 -6.3201e-03  3e-04  6e-17  7e-18\n",
      " 4: -6.1944e-03 -6.1981e-03  4e-06  1e-16  7e-18\n",
      " 5: -6.1968e-03 -6.1969e-03  4e-08  2e-16  6e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  3.2759e-03 -1.0085e+00  1e+00  4e-16  3e+00\n",
      " 1:  3.0424e-03 -1.8369e-02  2e-02  1e-16  6e-02\n",
      " 2: -2.3128e-03 -6.2332e-03  4e-03  1e-16  6e-03\n",
      " 3: -5.1657e-03 -5.5214e-03  4e-04  1e-16  1e-17\n",
      " 4: -5.3944e-03 -5.3989e-03  4e-06  2e-16  7e-18\n",
      " 5: -5.3977e-03 -5.3977e-03  4e-08  1e-16  1e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  4.1087e-03 -1.0069e+00  1e+00  3e-17  3e+00\n",
      " 1:  3.8594e-03 -1.6767e-02  2e-02  8e-17  6e-02\n",
      " 2: -1.6428e-03 -5.4888e-03  4e-03  3e-16  6e-03\n",
      " 3: -4.3438e-03 -4.6794e-03  3e-04  3e-16  8e-18\n",
      " 4: -4.5785e-03 -4.5832e-03  5e-06  2e-16  8e-18\n",
      " 5: -4.5823e-03 -4.5823e-03  5e-08  1e-16  6e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  5.0350e-03 -1.0071e+00  1e+00  3e-17  3e+00\n",
      " 1:  4.7660e-03 -1.6936e-02  2e-02  1e-16  6e-02\n",
      " 2: -7.9083e-04 -4.7441e-03  4e-03  1e-16  5e-03\n",
      " 3: -3.4708e-03 -3.8352e-03  4e-04  1e-16  8e-18\n",
      " 4: -3.7342e-03 -3.7408e-03  7e-06  1e-16  7e-18\n",
      " 5: -3.7398e-03 -3.7399e-03  7e-08  2e-16  5e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  6.0643e-03 -1.0073e+00  1e+00  8e-17  3e+00\n",
      " 1:  5.7695e-03 -1.7112e-02  2e-02  9e-17  6e-02\n",
      " 2:  1.8420e-05 -3.9506e-03  4e-03  2e-16  4e-03\n",
      " 3: -2.5703e-03 -2.9501e-03  4e-04  5e-17  6e-18\n",
      " 4: -2.8505e-03 -2.8600e-03  1e-05  1e-16  4e-18\n",
      " 5: -2.8591e-03 -2.8592e-03  1e-07  2e-16  7e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  7.2069e-03 -1.0075e+00  1e+00  6e-17  3e+00\n",
      " 1:  6.8789e-03 -1.7290e-02  2e-02  7e-17  6e-02\n",
      " 2:  8.0377e-04 -3.0788e-03  4e-03  2e-16  3e-03\n",
      " 3: -1.6344e-03 -2.0151e-03  4e-04  1e-16  3e-18\n",
      " 4: -1.9155e-03 -1.9295e-03  1e-05  1e-16  2e-17\n",
      " 5: -1.9285e-03 -1.9286e-03  2e-07  2e-16  5e-18\n",
      " 6: -1.9286e-03 -1.9286e-03  2e-09  1e-16  3e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  8.4738e-03 -1.0077e+00  1e+00  3e-16  3e+00\n",
      " 1:  8.1033e-03 -1.7467e-02  3e-02  1e-16  7e-02\n",
      " 2:  1.5887e-03 -2.1178e-03  4e-03  4e-17  1e-17\n",
      " 3: -6.5376e-04 -1.0221e-03  4e-04  1e-16  4e-18\n",
      " 4: -9.1787e-04 -9.3730e-04  2e-05  6e-17  7e-18\n",
      " 5: -9.3533e-04 -9.3577e-04  4e-07  1e-16  8e-18\n",
      " 6: -9.3575e-04 -9.3576e-04  4e-09  6e-17  4e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  9.8768e-03 -1.0080e+00  1e+00  2e-16  3e+00\n",
      " 1:  9.4524e-03 -1.7638e-02  3e-02  8e-17  7e-02\n",
      " 2:  2.6159e-03 -1.6889e-03  4e-03  1e-16  2e-17\n",
      " 3:  4.5173e-04  1.8685e-05  4e-04  2e-16  3e-18\n",
      " 4:  1.5514e-04  1.2632e-04  3e-05  1e-16  7e-18\n",
      " 5:  1.3477e-04  1.3227e-04  3e-06  8e-17  8e-18\n",
      " 6:  1.3260e-04  1.3252e-04  8e-08  2e-16  6e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.1428e-02 -1.0082e+00  1e+00  2e-16  3e+00\n",
      " 1:  1.0936e-02 -1.7797e-02  3e-02  7e-17  8e-02\n",
      " 2:  3.7067e-03 -1.2353e-03  5e-03  1e-16  8e-18\n",
      " 3:  1.6464e-03  1.1070e-03  5e-04  1e-16  1e-17\n",
      " 4:  1.3022e-03  1.2669e-03  4e-05  2e-16  6e-18\n",
      " 5:  1.2857e-03  1.2828e-03  3e-06  1e-16  1e-17\n",
      " 6:  1.2853e-03  1.2852e-03  9e-08  1e-16  5e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.3142e-02 -1.0085e+00  1e+00  8e-17  3e+00\n",
      " 1:  1.2565e-02 -1.7935e-02  3e-02  8e-17  8e-02\n",
      " 2:  4.8771e-03 -7.2680e-04  6e-03  7e-17  1e-17\n",
      " 3:  2.9049e-03  2.2725e-03  6e-04  1e-16  4e-18\n",
      " 4:  2.5238e-03  2.4880e-03  4e-05  1e-16  8e-18\n",
      " 5:  2.5074e-03  2.5066e-03  8e-07  6e-17  3e-18\n",
      " 6:  2.5072e-03  2.5072e-03  8e-09  2e-16  1e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.5032e-02 -1.0087e+00  1e+00  3e-17  3e+00\n",
      " 1:  1.4350e-02 -1.8042e-02  3e-02  1e-16  9e-02\n",
      " 2:  6.1435e-03 -1.3403e-04  6e-03  1e-16  3e-17\n",
      " 3:  4.2415e-03  3.5313e-03  7e-04  2e-16  1e-17\n",
      " 4:  3.8304e-03  3.7950e-03  4e-05  2e-16  7e-18\n",
      " 5:  3.8097e-03  3.8093e-03  4e-07  9e-17  6e-18\n",
      " 6:  3.8094e-03  3.8094e-03  4e-09  9e-17  1e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.7112e-02 -1.0089e+00  1e+00  1e-16  3e+00\n",
      " 1:  1.6302e-02 -1.8104e-02  3e-02  1e-16  9e-02\n",
      " 2:  7.5226e-03  5.7200e-04  7e-03  1e-16  2e-17\n",
      " 3:  5.6714e-03  4.9018e-03  8e-04  1e-16  9e-18\n",
      " 4:  5.2361e-03  5.1996e-03  4e-05  2e-16  2e-17\n",
      " 5:  5.2095e-03  5.2091e-03  4e-07  1e-16  9e-18\n",
      " 6:  5.2092e-03  5.2092e-03  4e-09  1e-16  1e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.9398e-02 -1.0091e+00  1e+00  1e-16  3e+00\n",
      " 1:  1.8432e-02 -1.8105e-02  4e-02  6e-17  1e-01\n",
      " 2:  9.0322e-03  1.4202e-03  8e-03  1e-16  2e-17\n",
      " 3:  7.2117e-03  6.4032e-03  8e-04  1e-16  1e-17\n",
      " 4:  6.7587e-03  6.7185e-03  4e-05  3e-16  2e-17\n",
      " 5:  6.7256e-03  6.7251e-03  5e-07  3e-17  4e-18\n",
      " 6:  6.7251e-03  6.7251e-03  5e-09  1e-16  6e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.1907e-02 -1.0093e+00  1e+00  0e+00  3e+00\n",
      " 1:  2.0750e-02 -1.8026e-02  4e-02  1e-16  1e-01\n",
      " 2:  1.0690e-02  2.4397e-03  8e-03  6e-17  3e-17\n",
      " 3:  8.8818e-03  8.0551e-03  8e-04  1e-16  1e-17\n",
      " 4:  8.4187e-03  8.3711e-03  5e-05  1e-16  6e-18\n",
      " 5:  8.3780e-03  8.3772e-03  7e-07  1e-16  1e-17\n",
      " 6:  8.3773e-03  8.3773e-03  7e-09  1e-16  1e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.4654e-02 -1.0094e+00  1e+00  3e-16  3e+00\n",
      " 1:  2.3270e-02 -1.7844e-02  4e-02  7e-17  1e-01\n",
      " 2:  1.2517e-02  3.6603e-03  9e-03  1e-16  2e-17\n",
      " 3:  1.0703e-02  9.8778e-03  8e-04  1e-16  1e-17\n",
      " 4:  1.0237e-02  1.0180e-02  6e-05  2e-16  2e-17\n",
      " 5:  1.0189e-02  1.0188e-02  1e-06  2e-16  2e-17\n",
      " 6:  1.0188e-02  1.0188e-02  1e-08  1e-16  8e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.7658e-02 -1.0094e+00  1e+00  3e-17  3e+00\n",
      " 1:  2.6002e-02 -1.7530e-02  4e-02  7e-17  1e-01\n",
      " 2:  1.4534e-02  5.1126e-03  9e-03  1e-16  2e-17\n",
      " 3:  1.2699e-02  1.1892e-02  8e-04  1e-16  1e-17\n",
      " 4:  1.2238e-02  1.2171e-02  7e-05  1e-16  2e-17\n",
      " 5:  1.2182e-02  1.2180e-02  1e-06  1e-16  8e-18\n",
      " 6:  1.2180e-02  1.2180e-02  1e-08  1e-16  1e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  3.0935e-02 -1.0093e+00  1e+00  6e-17  3e+00\n",
      " 1:  2.8959e-02 -1.7053e-02  5e-02  9e-17  1e-01\n",
      " 2:  1.6763e-02  6.8280e-03  1e-02  3e-17  4e-17\n",
      " 3:  1.4897e-02  1.4116e-02  8e-04  3e-17  1e-17\n",
      " 4:  1.4446e-02  1.4369e-02  8e-05  1e-16  2e-17\n",
      " 5:  1.4383e-02  1.4381e-02  2e-06  1e-16  1e-17\n",
      " 6:  1.4381e-02  1.4381e-02  2e-08  2e-16  9e-18\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  3.4505e-02 -1.0091e+00  1e+00  1e-16  3e+00\n",
      " 1:  3.2156e-02 -1.6375e-02  5e-02  1e-16  1e-01\n",
      " 2:  1.9231e-02  8.8398e-03  1e-02  1e-16  2e-17\n",
      " 3:  1.7336e-02  1.6511e-02  8e-04  1e-16  3e-17\n",
      " 4:  1.6894e-02  1.6804e-02  9e-05  1e-16  5e-18\n",
      " 5:  1.6824e-02  1.6820e-02  4e-06  2e-16  9e-18\n",
      " 6:  1.6820e-02  1.6820e-02  4e-08  1e-16  1e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  3.8387e-02 -1.0087e+00  1e+00  2e-16  3e+00\n",
      " 1:  3.5608e-02 -1.5455e-02  5e-02  1e-16  1e-01\n",
      " 2:  2.1966e-02  1.1183e-02  1e-02  1e-16  4e-17\n",
      " 3:  2.0038e-02  1.9169e-02  9e-04  2e-16  2e-17\n",
      " 4:  1.9610e-02  1.9509e-02  1e-04  2e-16  2e-17\n",
      " 5:  1.9535e-02  1.9529e-02  6e-06  1e-16  2e-17\n",
      " 6:  1.9530e-02  1.9530e-02  7e-08  3e-16  1e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  4.2603e-02 -1.0081e+00  1e+00  1e-16  3e+00\n",
      " 1:  3.9332e-02 -1.4245e-02  5e-02  1e-16  1e-01\n",
      " 2:  2.4999e-02  1.3893e-02  1e-02  7e-17  6e-17\n",
      " 3:  2.3036e-02  2.2126e-02  9e-04  5e-17  2e-17\n",
      " 4:  2.2628e-02  2.2518e-02  1e-04  2e-16  3e-17\n",
      " 5:  2.2554e-02  2.2544e-02  1e-05  1e-16  1e-17\n",
      " 6:  2.2545e-02  2.2545e-02  1e-07  1e-16  1e-17\n",
      " 7:  2.2545e-02  2.2545e-02  1e-09  6e-17  2e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  4.7175e-02 -1.0072e+00  1e+00  8e-17  3e+00\n",
      " 1:  4.3350e-02 -1.2692e-02  6e-02  7e-17  1e-01\n",
      " 2:  2.8366e-02  1.7011e-02  1e-02  3e-16  6e-17\n",
      " 3:  2.6372e-02  2.5426e-02  9e-04  6e-17  2e-17\n",
      " 4:  2.5990e-02  2.5869e-02  1e-04  3e-17  2e-17\n",
      " 5:  2.5918e-02  2.5904e-02  1e-05  1e-16  6e-18\n",
      " 6:  2.5907e-02  2.5907e-02  5e-07  1e-16  1e-17\n",
      " 7:  2.5907e-02  2.5907e-02  5e-09  1e-16  1e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  5.2130e-02 -1.0061e+00  1e+00  3e-17  3e+00\n",
      " 1:  4.7688e-02 -1.0739e-02  6e-02  8e-17  2e-01\n",
      " 2:  3.2108e-02  2.0579e-02  1e-02  1e-16  5e-17\n",
      " 3:  3.0087e-02  2.9113e-02  1e-03  1e-16  3e-17\n",
      " 4:  2.9734e-02  2.9606e-02  1e-04  2e-16  2e-17\n",
      " 5:  2.9671e-02  2.9654e-02  2e-05  1e-16  2e-17\n",
      " 6:  2.9660e-02  2.9659e-02  2e-06  6e-17  1e-17\n",
      " 7:  2.9659e-02  2.9659e-02  8e-08  2e-16  2e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  5.7495e-02 -1.0045e+00  1e+00  1e-16  3e+00\n",
      " 1:  5.2376e-02 -8.3224e-03  6e-02  8e-17  2e-01\n",
      " 2:  3.6270e-02  2.4643e-02  1e-02  2e-16  7e-17\n",
      " 3:  3.4233e-02  3.3237e-02  1e-03  2e-16  2e-17\n",
      " 4:  3.3909e-02  3.3778e-02  1e-04  1e-16  2e-17\n",
      " 5:  3.3857e-02  3.3839e-02  2e-05  4e-17  2e-17\n",
      " 6:  3.3849e-02  3.3848e-02  2e-06  1e-16  3e-17\n",
      " 7:  3.3849e-02  3.3848e-02  2e-08  2e-16  2e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  6.3305e-02 -1.0025e+00  1e+00  3e-17  3e+00\n",
      " 1:  5.7455e-02 -5.3731e-03  6e-02  2e-16  2e-01\n",
      " 2:  4.0905e-02  2.9254e-02  1e-02  2e-16  5e-17\n",
      " 3:  3.8864e-02  3.7856e-02  1e-03  2e-16  3e-17\n",
      " 4:  3.8570e-02  3.8439e-02  1e-04  2e-16  1e-17\n",
      " 5:  3.8526e-02  3.8511e-02  2e-05  6e-17  2e-17\n",
      " 6:  3.8520e-02  3.8519e-02  1e-06  1e-16  2e-17\n",
      " 7:  3.8519e-02  3.8519e-02  2e-08  2e-16  3e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  6.9599e-02 -1.0000e+00  1e+00  3e-17  3e+00\n",
      " 1:  6.2970e-02 -1.8178e-03  6e-02  9e-17  2e-01\n",
      " 2:  4.6071e-02  3.4470e-02  1e-02  2e-16  7e-17\n",
      " 3:  4.4041e-02  4.3032e-02  1e-03  2e-17  2e-17\n",
      " 4:  4.3775e-02  4.3649e-02  1e-04  2e-16  2e-17\n",
      " 5:  4.3738e-02  4.3726e-02  1e-05  2e-16  2e-17\n",
      " 6:  4.3733e-02  4.3732e-02  1e-06  2e-16  2e-17\n",
      " 7:  4.3732e-02  4.3732e-02  4e-08  1e-16  2e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  7.6422e-02 -9.9692e-01  1e+00  3e-16  3e+00\n",
      " 1:  6.8979e-02  2.4231e-03  7e-02  4e-17  2e-01\n",
      " 2:  5.1837e-02  4.0353e-02  1e-02  8e-17  5e-17\n",
      " 3:  4.9832e-02  4.8833e-02  1e-03  7e-17  4e-17\n",
      " 4:  4.9594e-02  4.9476e-02  1e-04  1e-16  1e-17\n",
      " 5:  4.9562e-02  4.9552e-02  1e-05  2e-16  3e-17\n",
      " 6:  4.9557e-02  4.9556e-02  1e-06  1e-16  5e-17\n",
      " 7:  4.9556e-02  4.9556e-02  8e-08  2e-16  2e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  8.3832e-02 -9.9311e-01  1e+00  2e-16  3e+00\n",
      " 1:  7.5552e-02  7.4352e-03  7e-02  9e-17  2e-01\n",
      " 2:  5.8277e-02  4.6976e-02  1e-02  2e-16  4e-17\n",
      " 3:  5.6316e-02  5.5337e-02  1e-03  1e-16  4e-17\n",
      " 4:  5.6102e-02  5.5995e-02  1e-04  3e-16  4e-17\n",
      " 5:  5.6074e-02  5.6064e-02  1e-05  2e-16  3e-17\n",
      " 6:  5.6069e-02  5.6068e-02  1e-06  4e-17  3e-17\n",
      " 7:  5.6068e-02  5.6068e-02  1e-07  2e-16  3e-17\n",
      " 8:  5.6068e-02  5.6068e-02  3e-09  2e-16  2e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  9.1893e-02 -9.8850e-01  1e+00  3e-17  3e+00\n",
      " 1:  8.2771e-02  1.3311e-02  7e-02  2e-16  2e-01\n",
      " 2:  6.5478e-02  5.4420e-02  1e-02  1e-16  4e-17\n",
      " 3:  6.3577e-02  6.2630e-02  9e-04  3e-16  1e-17\n",
      " 4:  6.3387e-02  6.3291e-02  1e-04  2e-16  4e-17\n",
      " 5:  6.3361e-02  6.3351e-02  1e-05  1e-16  5e-17\n",
      " 6:  6.3356e-02  6.3355e-02  1e-06  8e-17  4e-17\n",
      " 7:  6.3356e-02  6.3356e-02  2e-07  1e-16  3e-17\n",
      " 8:  6.3356e-02  6.3356e-02  2e-08  1e-16  5e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.0069e-01 -9.8298e-01  1e+00  3e-17  3e+00\n",
      " 1:  9.0730e-02  2.0151e-02  7e-02  8e-17  2e-01\n",
      " 2:  7.3537e-02  6.2776e-02  1e-02  2e-16  8e-17\n",
      " 3:  7.1713e-02  7.0807e-02  9e-04  1e-16  7e-17\n",
      " 4:  7.1542e-02  7.1458e-02  8e-05  1e-16  4e-17\n",
      " 5:  7.1519e-02  7.1509e-02  9e-06  6e-17  5e-17\n",
      " 6:  7.1515e-02  7.1514e-02  1e-06  2e-16  2e-17\n",
      " 7:  7.1515e-02  7.1515e-02  2e-07  1e-16  4e-17\n",
      " 8:  7.1515e-02  7.1515e-02  2e-08  2e-16  3e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.1030e-01 -9.7644e-01  1e+00  1e-16  3e+00\n",
      " 1:  9.9543e-02  2.8067e-02  7e-02  1e-16  2e-01\n",
      " 2:  8.2564e-02  7.2148e-02  1e-02  3e-17  1e-16\n",
      " 3:  8.0831e-02  7.9976e-02  9e-04  1e-16  3e-17\n",
      " 4:  8.0678e-02  8.0604e-02  7e-05  2e-16  6e-17\n",
      " 5:  8.0656e-02  8.0648e-02  9e-06  1e-16  4e-17\n",
      " 6:  8.0654e-02  8.0652e-02  1e-06  1e-16  4e-17\n",
      " 7:  8.0653e-02  8.0653e-02  1e-07  1e-16  5e-17\n",
      " 8:  8.0653e-02  8.0653e-02  3e-09  1e-16  5e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.2086e-01 -9.6874e-01  1e+00  1e-16  3e+00\n",
      " 1:  1.0934e-01  3.7181e-02  7e-02  1e-16  2e-01\n",
      " 2:  9.2682e-02  8.2652e-02  1e-02  1e-16  5e-17\n",
      " 3:  9.1052e-02  9.0255e-02  8e-04  1e-16  4e-17\n",
      " 4:  9.0915e-02  9.0849e-02  7e-05  6e-17  6e-17\n",
      " 5:  9.0895e-02  9.0887e-02  8e-06  1e-16  2e-17\n",
      " 6:  9.0893e-02  9.0892e-02  1e-06  2e-16  4e-17\n",
      " 7:  9.0893e-02  9.0893e-02  6e-08  2e-16  3e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.3248e-01 -9.5974e-01  1e+00  3e-16  3e+00\n",
      " 1:  1.2025e-01  4.7630e-02  7e-02  2e-16  2e-01\n",
      " 2:  1.0403e-01  9.4422e-02  1e-02  1e-16  5e-17\n",
      " 3:  1.0251e-01  1.0178e-01  7e-04  2e-16  6e-17\n",
      " 4:  1.0239e-01  1.0233e-01  6e-05  6e-17  4e-17\n",
      " 5:  1.0237e-01  1.0236e-01  7e-06  1e-16  6e-17\n",
      " 6:  1.0237e-01  1.0237e-01  8e-07  1e-16  7e-17\n",
      " 7:  1.0237e-01  1.0237e-01  2e-08  1e-16  7e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.4532e-01 -9.4930e-01  1e+00  7e-17  3e+00\n",
      " 1:  1.3246e-01  5.9567e-02  7e-02  1e-16  2e-01\n",
      " 2:  1.1676e-01  1.0761e-01  9e-03  1e-16  7e-17\n",
      " 3:  1.1536e-01  1.1470e-01  7e-04  2e-16  5e-17\n",
      " 4:  1.1525e-01  1.1520e-01  5e-05  1e-16  8e-17\n",
      " 5:  1.1524e-01  1.1523e-01  7e-06  6e-17  7e-17\n",
      " 6:  1.1523e-01  1.1523e-01  6e-07  1e-16  5e-17\n",
      " 7:  1.1523e-01  1.1523e-01  8e-09  2e-16  4e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.5955e-01 -9.3724e-01  1e+00  7e-17  3e+00\n",
      " 1:  1.4614e-01  7.3167e-02  7e-02  2e-16  2e-01\n",
      " 2:  1.3105e-01  1.2237e-01  9e-03  2e-16  9e-17\n",
      " 3:  1.2978e-01  1.2918e-01  6e-04  1e-16  8e-17\n",
      " 4:  1.2967e-01  1.2962e-01  5e-05  2e-16  7e-17\n",
      " 5:  1.2966e-01  1.2965e-01  6e-06  1e-16  7e-17\n",
      " 6:  1.2966e-01  1.2966e-01  4e-07  1e-16  6e-17\n",
      " 7:  1.2966e-01  1.2966e-01  4e-09  1e-16  1e-16\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.7537e-01 -9.2337e-01  1e+00  6e-17  3e+00\n",
      " 1:  1.6150e-01  8.8624e-02  7e-02  2e-16  2e-01\n",
      " 2:  1.4709e-01  1.3892e-01  8e-03  7e-17  1e-16\n",
      " 3:  1.4594e-01  1.4540e-01  5e-04  1e-16  8e-17\n",
      " 4:  1.4585e-01  1.4580e-01  4e-05  1e-16  6e-17\n",
      " 5:  1.4584e-01  1.4583e-01  5e-06  1e-16  9e-17\n",
      " 6:  1.4584e-01  1.4584e-01  2e-07  1e-16  8e-17\n",
      " 7:  1.4584e-01  1.4584e-01  2e-09  1e-16  5e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.9300e-01 -9.0750e-01  1e+00  3e-16  3e+00\n",
      " 1:  1.7876e-01  1.0616e-01  7e-02  1e-16  2e-01\n",
      " 2:  1.6509e-01  1.5744e-01  8e-03  2e-16  1e-16\n",
      " 3:  1.6407e-01  1.6360e-01  5e-04  1e-16  1e-16\n",
      " 4:  1.6399e-01  1.6395e-01  4e-05  2e-16  3e-17\n",
      " 5:  1.6398e-01  1.6397e-01  5e-06  1e-16  1e-16\n",
      " 6:  1.6398e-01  1.6398e-01  1e-07  1e-16  1e-16\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.1271e-01 -8.8938e-01  1e+00  1e-17  3e+00\n",
      " 1:  1.9819e-01  1.2602e-01  7e-02  1e-16  2e-01\n",
      " 2:  1.8531e-01  1.7819e-01  7e-03  1e-16  1e-16\n",
      " 3:  1.8441e-01  1.8400e-01  4e-04  1e-16  7e-17\n",
      " 4:  1.8433e-01  1.8430e-01  4e-05  2e-16  8e-17\n",
      " 5:  1.8433e-01  1.8432e-01  4e-06  6e-17  7e-17\n",
      " 6:  1.8433e-01  1.8433e-01  7e-08  1e-16  4e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.3476e-01 -8.6876e-01  1e+00  2e-16  3e+00\n",
      " 1:  2.2006e-01  1.4850e-01  7e-02  6e-17  2e-01\n",
      " 2:  2.0801e-01  2.0143e-01  7e-03  2e-17  9e-17\n",
      " 3:  2.0723e-01  2.0687e-01  4e-04  1e-16  1e-16\n",
      " 4:  2.0716e-01  2.0712e-01  3e-05  1e-16  2e-16\n",
      " 5:  2.0715e-01  2.0715e-01  3e-06  1e-16  1e-16\n",
      " 6:  2.0715e-01  2.0715e-01  4e-08  1e-16  1e-16\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.5950e-01 -8.4535e-01  1e+00  4e-16  3e+00\n",
      " 1:  2.4468e-01  1.7389e-01  7e-02  3e-17  2e-01\n",
      " 2:  2.3350e-01  2.2747e-01  6e-03  6e-17  1e-16\n",
      " 3:  2.3282e-01  2.3252e-01  3e-04  2e-16  1e-16\n",
      " 4:  2.3276e-01  2.3273e-01  3e-05  1e-16  5e-17\n",
      " 5:  2.3275e-01  2.3275e-01  2e-06  6e-17  6e-17\n",
      " 6:  2.3275e-01  2.3275e-01  3e-08  6e-17  6e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.8726e-01 -8.1880e-01  1e+00  0e+00  3e+00\n",
      " 1:  2.7242e-01  2.0256e-01  7e-02  2e-16  2e-01\n",
      " 2:  2.6212e-01  2.5663e-01  5e-03  2e-16  1e-16\n",
      " 3:  2.6153e-01  2.6128e-01  3e-04  2e-16  1e-16\n",
      " 4:  2.6148e-01  2.6146e-01  3e-05  2e-16  2e-16\n",
      " 5:  2.6148e-01  2.6148e-01  2e-06  2e-16  8e-17\n",
      " 6:  2.6148e-01  2.6148e-01  2e-08  1e-16  2e-16\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  3.1846e-01 -7.8875e-01  1e+00  2e-16  3e+00\n",
      " 1:  3.0367e-01  2.3491e-01  7e-02  8e-17  2e-01\n",
      " 2:  2.9425e-01  2.8930e-01  5e-03  6e-17  3e-16\n",
      " 3:  2.9375e-01  2.9353e-01  2e-04  1e-16  2e-16\n",
      " 4:  2.9370e-01  2.9368e-01  2e-05  1e-16  9e-17\n",
      " 5:  2.9370e-01  2.9370e-01  1e-06  3e-17  8e-17\n",
      " 6:  2.9370e-01  2.9370e-01  1e-08  1e-16  1e-16\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  3.5353e-01 -7.5476e-01  1e+00  8e-17  3e+00\n",
      " 1:  3.3886e-01  2.7136e-01  7e-02  8e-17  2e-01\n",
      " 2:  3.3031e-01  3.2589e-01  4e-03  1e-17  2e-16\n",
      " 3:  3.2989e-01  3.2970e-01  2e-04  6e-17  2e-16\n",
      " 4:  3.2986e-01  3.2984e-01  2e-05  1e-16  2e-16\n",
      " 5:  3.2985e-01  3.2985e-01  6e-07  1e-16  2e-16\n",
      " 6:  3.2985e-01  3.2985e-01  6e-09  2e-16  7e-17\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  3.9296e-01 -7.1637e-01  1e+00  2e-16  3e+00\n",
      " 1:  3.7848e-01  3.1241e-01  7e-02  2e-16  2e-01\n",
      " 2:  3.7080e-01  3.6689e-01  4e-03  1e-16  2e-16\n",
      " 3:  3.7045e-01  3.7027e-01  2e-04  5e-17  1e-16\n",
      " 4:  3.7041e-01  3.7040e-01  2e-05  1e-16  2e-16\n",
      " 5:  3.7041e-01  3.7041e-01  4e-07  1e-16  1e-16\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  4.3733e-01 -6.7303e-01  1e+00  3e-16  3e+00\n",
      " 1:  4.2309e-01  3.5862e-01  6e-02  1e-16  2e-01\n",
      " 2:  4.1624e-01  4.1281e-01  3e-03  1e-16  6e-05\n",
      " 3:  4.1594e-01  4.1579e-01  2e-04  1e-16  1e-06\n",
      " 4:  4.1592e-01  4.1591e-01  1e-05  1e-16  2e-16\n",
      " 5:  4.1592e-01  4.1592e-01  2e-07  6e-17  1e-16\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  4.8722e-01 -6.2413e-01  1e+00  1e-16  3e+00\n",
      " 1:  4.7329e-01  4.1057e-01  6e-02  1e-16  2e-01\n",
      " 2:  4.6724e-01  4.6416e-01  3e-03  6e-17  5e-04\n",
      " 3:  4.6699e-01  4.6686e-01  1e-04  2e-16  8e-06\n",
      " 4:  4.6697e-01  4.6696e-01  1e-05  2e-16  2e-16\n",
      " 5:  4.6697e-01  4.6697e-01  1e-07  1e-16  1e-16\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  5.4335e-01 -5.6899e-01  1e+00  2e-16  3e+00\n",
      " 1:  5.2976e-01  4.6896e-01  6e-02  1e-16  2e-01\n",
      " 2:  5.2448e-01  5.2174e-01  3e-03  6e-17  8e-04\n",
      " 3:  5.2427e-01  5.2414e-01  1e-04  2e-16  1e-05\n",
      " 4:  5.2425e-01  5.2425e-01  8e-06  2e-16  4e-08\n",
      " 5:  5.2425e-01  5.2425e-01  1e-07  2e-16  3e-10\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  6.0647e-01 -5.0685e-01  1e+00  5e-16  3e+00\n",
      " 1:  5.9329e-01  5.3454e-01  6e-02  1e-16  2e-01\n",
      " 2:  5.8871e-01  5.8629e-01  2e-03  6e-17  1e-03\n",
      " 3:  5.8854e-01  5.8842e-01  1e-04  2e-16  2e-05\n",
      " 4:  5.8852e-01  5.8852e-01  6e-06  1e-16  1e-07\n",
      " 5:  5.8852e-01  5.8852e-01  7e-08  3e-16  1e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  6.7746e-01 -4.3682e-01  1e+00  2e-16  3e+00\n",
      " 1:  6.6471e-01  6.0816e-01  6e-02  1e-16  2e-01\n",
      " 2:  6.6079e-01  6.5867e-01  2e-03  1e-16  1e-03\n",
      " 3:  6.6064e-01  6.6054e-01  1e-04  1e-16  2e-05\n",
      " 4:  6.6063e-01  6.6063e-01  5e-06  1e-16  2e-07\n",
      " 5:  6.6063e-01  6.6063e-01  5e-08  8e-17  2e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  7.5726e-01 -3.5796e-01  1e+00  4e-17  3e+00\n",
      " 1:  7.4499e-01  6.9075e-01  5e-02  1e-16  1e-01\n",
      " 2:  7.4167e-01  7.3983e-01  2e-03  1e-16  1e-03\n",
      " 3:  7.4154e-01  7.4146e-01  8e-05  1e-16  2e-05\n",
      " 4:  7.4154e-01  7.4153e-01  3e-06  2e-16  2e-07\n",
      " 5:  7.4154e-01  7.4154e-01  3e-08  2e-16  2e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  8.4696e-01 -2.6918e-01  1e+00  2e-16  3e+00\n",
      " 1:  8.3522e-01  7.8337e-01  5e-02  1e-16  1e-01\n",
      " 2:  8.3242e-01  8.3083e-01  2e-03  1e-16  1e-03\n",
      " 3:  8.3232e-01  8.3225e-01  7e-05  6e-17  2e-05\n",
      " 4:  8.3231e-01  8.3231e-01  2e-06  2e-16  2e-07\n",
      " 5:  8.3231e-01  8.3231e-01  2e-08  1e-16  2e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  9.4778e-01 -1.6926e-01  1e+00  2e-16  3e+00\n",
      " 1:  9.3658e-01  8.8721e-01  5e-02  1e-16  1e-01\n",
      " 2:  9.3425e-01  9.3289e-01  1e-03  1e-16  1e-03\n",
      " 3:  9.3417e-01  9.3411e-01  6e-05  1e-16  2e-05\n",
      " 4:  9.3416e-01  9.3416e-01  1e-06  1e-16  2e-07\n",
      " 5:  9.3416e-01  9.3416e-01  1e-08  2e-16  2e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.0611e+00 -5.6841e-02  1e+00  1e-17  3e+00\n",
      " 1:  1.0504e+00  1.0036e+00  5e-02  7e-17  1e-01\n",
      " 2:  1.0485e+00  1.0474e+00  1e-03  2e-16  1e-03\n",
      " 3:  1.0484e+00  1.0484e+00  5e-05  1e-16  2e-05\n",
      " 4:  1.0484e+00  1.0484e+00  9e-07  2e-16  2e-07\n",
      " 5:  1.0484e+00  1.0484e+00  9e-09  1e-16  2e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.1883e+00  6.9597e-02  1e+00  7e-17  3e+00\n",
      " 1:  1.1783e+00  1.1340e+00  4e-02  6e-17  1e-01\n",
      " 2:  1.1767e+00  1.1757e+00  1e-03  1e-16  1e-03\n",
      " 3:  1.1767e+00  1.1766e+00  4e-05  2e-16  2e-05\n",
      " 4:  1.1767e+00  1.1767e+00  6e-07  2e-16  2e-07\n",
      " 5:  1.1767e+00  1.1767e+00  6e-09  1e-16  2e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.3313e+00  2.1176e-01  1e+00  3e-16  3e+00\n",
      " 1:  1.3218e+00  1.2801e+00  4e-02  2e-16  1e-01\n",
      " 2:  1.3206e+00  1.3197e+00  8e-04  6e-17  1e-03\n",
      " 3:  1.3205e+00  1.3205e+00  3e-05  6e-17  2e-05\n",
      " 4:  1.3205e+00  1.3205e+00  4e-07  2e-16  2e-07\n",
      " 5:  1.3205e+00  1.3205e+00  4e-09  2e-16  2e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.4918e+00  3.7157e-01  1e+00  2e-16  3e+00\n",
      " 1:  1.4830e+00  1.4438e+00  4e-02  3e-17  1e-01\n",
      " 2:  1.4820e+00  1.4813e+00  7e-04  3e-16  1e-03\n",
      " 3:  1.4819e+00  1.4819e+00  2e-05  1e-16  1e-05\n",
      " 4:  1.4819e+00  1.4819e+00  3e-07  1e-16  1e-07\n",
      " 5:  1.4819e+00  1.4819e+00  3e-09  1e-16  1e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.6721e+00  5.5117e-01  1e+00  2e-16  3e+00\n",
      " 1:  1.6639e+00  1.6271e+00  4e-02  3e-17  1e-01\n",
      " 2:  1.6631e+00  1.6625e+00  6e-04  1e-16  1e-03\n",
      " 3:  1.6631e+00  1.6630e+00  2e-05  1e-16  1e-05\n",
      " 4:  1.6631e+00  1.6631e+00  2e-07  1e-16  1e-07\n",
      " 5:  1.6631e+00  1.6631e+00  2e-09  2e-16  1e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.8746e+00  7.5296e-01  1e+00  1e-16  3e+00\n",
      " 1:  1.8669e+00  1.8326e+00  3e-02  1e-16  9e-02\n",
      " 2:  1.8663e+00  1.8657e+00  5e-04  1e-16  1e-03\n",
      " 3:  1.8663e+00  1.8663e+00  1e-05  2e-16  1e-05\n",
      " 4:  1.8663e+00  1.8663e+00  1e-07  1e-16  1e-07\n",
      " 5:  1.8663e+00  1.8663e+00  1e-09  2e-16  1e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.1019e+00  9.7963e-01  1e+00  1e-17  3e+00\n",
      " 1:  2.0948e+00  2.0628e+00  3e-02  2e-16  9e-02\n",
      " 2:  2.0943e+00  2.0938e+00  5e-04  1e-16  1e-03\n",
      " 3:  2.0943e+00  2.0943e+00  1e-05  2e-16  1e-05\n",
      " 4:  2.0943e+00  2.0943e+00  1e-07  2e-16  1e-07\n",
      " 5:  2.0943e+00  2.0943e+00  1e-09  2e-16  1e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.3570e+00  1.2342e+00  1e+00  2e-16  3e+00\n",
      " 1:  2.3505e+00  2.3207e+00  3e-02  8e-17  8e-02\n",
      " 2:  2.3501e+00  2.3496e+00  5e-04  1e-16  1e-03\n",
      " 3:  2.3501e+00  2.3501e+00  8e-06  6e-17  1e-05\n",
      " 4:  2.3501e+00  2.3501e+00  8e-08  2e-16  1e-07\n",
      " 5:  2.3501e+00  2.3501e+00  8e-10  2e-16  1e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.6434e+00  1.5201e+00  1e+00  1e-17  3e+00\n",
      " 1:  2.6375e+00  2.6097e+00  3e-02  2e-16  7e-02\n",
      " 2:  2.6371e+00  2.6367e+00  5e-04  1e-16  1e-03\n",
      " 3:  2.6371e+00  2.6371e+00  7e-06  2e-16  1e-05\n",
      " 4:  2.6371e+00  2.6371e+00  7e-08  1e-17  1e-07\n",
      " 5:  2.6371e+00  2.6371e+00  7e-10  4e-17  1e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.9649e+00  1.8411e+00  1e+00  2e-17  3e+00\n",
      " 1:  2.9594e+00  2.9336e+00  3e-02  2e-16  7e-02\n",
      " 2:  2.9592e+00  2.9588e+00  4e-04  2e-16  1e-03\n",
      " 3:  2.9592e+00  2.9592e+00  6e-06  6e-17  1e-05\n",
      " 4:  2.9592e+00  2.9592e+00  6e-08  2e-16  1e-07\n",
      " 5:  2.9592e+00  2.9592e+00  6e-10  3e-16  1e-09\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  3.3258e+00  2.2015e+00  1e+00  2e-16  3e+00\n",
      " 1:  3.3208e+00  3.2967e+00  2e-02  6e-17  6e-02\n",
      " 2:  3.3206e+00  3.3202e+00  4e-04  1e-16  9e-04\n",
      " 3:  3.3206e+00  3.3206e+00  5e-06  6e-17  9e-06\n",
      " 4:  3.3206e+00  3.3206e+00  5e-08  1e-16  9e-08\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  3.7307e+00  2.6061e+00  1e+00  1e-16  3e+00\n",
      " 1:  3.7262e+00  3.7038e+00  2e-02  2e-16  6e-02\n",
      " 2:  3.7260e+00  3.7257e+00  4e-04  7e-17  8e-04\n",
      " 3:  3.7260e+00  3.7260e+00  4e-06  1e-16  8e-06\n",
      " 4:  3.7260e+00  3.7260e+00  4e-08  6e-17  8e-08\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  4.1852e+00  3.0602e+00  1e+00  1e-16  3e+00\n",
      " 1:  4.1811e+00  4.1602e+00  2e-02  2e-16  6e-02\n",
      " 2:  4.1809e+00  4.1806e+00  3e-04  6e-17  8e-04\n",
      " 3:  4.1809e+00  4.1809e+00  3e-06  2e-16  8e-06\n",
      " 4:  4.1809e+00  4.1809e+00  3e-08  1e-16  8e-08\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  4.6952e+00  3.5699e+00  1e+00  3e-17  3e+00\n",
      " 1:  4.6915e+00  4.6719e+00  2e-02  2e-16  5e-02\n",
      " 2:  4.6914e+00  4.6911e+00  3e-04  1e-16  7e-04\n",
      " 3:  4.6914e+00  4.6914e+00  3e-06  2e-16  7e-06\n",
      " 4:  4.6914e+00  4.6914e+00  3e-08  2e-17  7e-08\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  5.2676e+00  4.1419e+00  1e+00  2e-16  4e+00\n",
      " 1:  5.2642e+00  5.2458e+00  2e-02  9e-17  5e-02\n",
      " 2:  5.2641e+00  5.2639e+00  3e-04  2e-16  6e-04\n",
      " 3:  5.2641e+00  5.2641e+00  3e-06  1e-16  6e-06\n",
      " 4:  5.2641e+00  5.2641e+00  3e-08  1e-16  6e-08\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  5.9098e+00  4.7839e+00  1e+00  1e-16  4e+00\n",
      " 1:  5.9068e+00  5.8894e+00  2e-02  8e-17  5e-02\n",
      " 2:  5.9067e+00  5.9065e+00  2e-04  1e-16  6e-04\n",
      " 3:  5.9067e+00  5.9067e+00  2e-06  5e-17  6e-06\n",
      " 4:  5.9067e+00  5.9067e+00  2e-08  2e-16  6e-08\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  6.6305e+00  5.5044e+00  1e+00  6e-17  4e+00\n",
      " 1:  6.6278e+00  6.6114e+00  2e-02  1e-16  5e-02\n",
      " 2:  6.6277e+00  6.6275e+00  2e-04  1e-16  5e-04\n",
      " 3:  6.6277e+00  6.6277e+00  2e-06  1e-16  5e-06\n",
      " 4:  6.6277e+00  6.6277e+00  2e-08  1e-16  5e-08\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  7.4392e+00  6.3128e+00  1e+00  3e-16  4e+00\n",
      " 1:  7.4367e+00  7.4212e+00  2e-02  1e-16  4e-02\n",
      " 2:  7.4367e+00  7.4365e+00  2e-04  2e-16  5e-04\n",
      " 3:  7.4367e+00  7.4367e+00  2e-06  2e-16  5e-06\n",
      " 4:  7.4367e+00  7.4367e+00  2e-08  2e-16  5e-08\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  8.3467e+00  7.2201e+00  1e+00  7e-18  4e+00\n",
      " 1:  8.3444e+00  8.3295e+00  1e-02  2e-16  4e-02\n",
      " 2:  8.3444e+00  8.3442e+00  2e-04  1e-16  5e-04\n",
      " 3:  8.3444e+00  8.3444e+00  2e-06  2e-16  5e-06\n",
      " 4:  8.3444e+00  8.3444e+00  2e-08  1e-16  5e-08\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  9.3649e+00  8.2381e+00  1e+00  1e-17  4e+00\n",
      " 1:  9.3629e+00  9.3486e+00  1e-02  1e-16  4e-02\n",
      " 2:  9.3629e+00  9.3627e+00  2e-04  6e-17  4e-04\n",
      " 3:  9.3629e+00  9.3629e+00  2e-06  1e-16  4e-06\n",
      " 4:  9.3629e+00  9.3629e+00  2e-08  2e-16  4e-08\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.0507e+01  9.3805e+00  1e+00  2e-16  4e+00\n",
      " 1:  1.0506e+01  1.0492e+01  1e-02  2e-16  4e-02\n",
      " 2:  1.0506e+01  1.0505e+01  1e-04  1e-16  4e-04\n",
      " 3:  1.0506e+01  1.0506e+01  1e-06  2e-16  4e-06\n",
      " 4:  1.0506e+01  1.0506e+01  1e-08  1e-16  4e-08\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.1789e+01  1.0662e+01  1e+00  6e-17  4e+00\n",
      " 1:  1.1788e+01  1.1774e+01  1e-02  8e-17  4e-02\n",
      " 2:  1.1788e+01  1.1788e+01  1e-04  3e-16  4e-04\n",
      " 3:  1.1788e+01  1.1788e+01  1e-06  7e-18  4e-06\n",
      " 4:  1.1788e+01  1.1788e+01  1e-08  2e-16  4e-08\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.3228e+01  1.2101e+01  1e+00  2e-16  4e+00\n",
      " 1:  1.3226e+01  1.3213e+01  1e-02  1e-16  4e-02\n",
      " 2:  1.3226e+01  1.3226e+01  1e-04  1e-16  4e-04\n",
      " 3:  1.3226e+01  1.3226e+01  1e-06  1e-16  4e-06\n",
      " 4:  1.3226e+01  1.3226e+01  1e-08  1e-16  4e-08\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.4842e+01  1.3714e+01  1e+00  2e-16  4e+00\n",
      " 1:  1.4840e+01  1.4828e+01  1e-02  2e-16  4e-02\n",
      " 2:  1.4840e+01  1.4840e+01  1e-04  2e-16  4e-04\n",
      " 3:  1.4840e+01  1.4840e+01  1e-06  2e-16  4e-06\n",
      " 4:  1.4840e+01  1.4840e+01  1e-08  1e-16  4e-08\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.6653e+01  1.5525e+01  1e+00  3e-17  4e+00\n",
      " 1:  1.6652e+01  1.6639e+01  1e-02  1e-16  4e-02\n",
      " 2:  1.6652e+01  1.6651e+01  1e-04  2e-16  4e-04\n",
      " 3:  1.6652e+01  1.6652e+01  1e-06  2e-16  4e-06\n",
      " 4:  1.6652e+01  1.6652e+01  1e-08  1e-16  4e-08\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  1.8685e+01  1.7557e+01  1e+00  1e-17  4e+00\n",
      " 1:  1.8684e+01  1.8671e+01  1e-02  1e-16  4e-02\n",
      " 2:  1.8684e+01  1.8684e+01  1e-04  1e-16  4e-04\n",
      " 3:  1.8684e+01  1.8684e+01  1e-06  2e-16  4e-06\n",
      " 4:  1.8684e+01  1.8684e+01  1e-08  1e-16  4e-08\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.0965e+01  1.9837e+01  1e+00  8e-17  4e+00\n",
      " 1:  2.0964e+01  2.0952e+01  1e-02  1e-16  4e-02\n",
      " 2:  2.0964e+01  2.0964e+01  1e-04  2e-16  4e-04\n",
      " 3:  2.0964e+01  2.0964e+01  1e-06  6e-17  4e-06\n",
      " 4:  2.0964e+01  2.0964e+01  1e-08  1e-16  4e-08\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0:  2.3523e+01  2.2395e+01  1e+00  3e-16  4e+00\n",
      " 1:  2.3522e+01  2.3510e+01  1e-02  2e-16  4e-02\n",
      " 2:  2.3522e+01  2.3522e+01  1e-04  1e-16  4e-04\n",
      " 3:  2.3522e+01  2.3522e+01  1e-06  6e-17  4e-06\n",
      " 4:  2.3522e+01  2.3522e+01  1e-08  6e-17  4e-08\n",
      "Optimal solution found.\n",
      "     pcost       dcost       gap    pres   dres\n",
      " 0: -3.7358e-03 -1.0218e+00  1e+00  2e-16  3e+00\n",
      " 1: -3.9449e-03 -3.1795e-02  3e-02  1e-16  7e-02\n",
      " 2: -1.8388e-02 -2.2764e-02  4e-03  1e-16  1e-17\n",
      " 3: -2.1632e-02 -2.1706e-02  7e-05  2e-16  1e-17\n",
      " 4: -2.1668e-02 -2.1669e-02  7e-07  3e-16  6e-18\n",
      " 5: -2.1669e-02 -2.1669e-02  7e-09  6e-17  1e-17\n",
      "Optimal solution found.\n"
     ]
    },
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] File all_ten.csv does not exist: 'all_ten.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-5-34f43382faa7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     47\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     48\u001b[0m \u001b[1;31m# All 10\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 49\u001b[1;33m \u001b[0mall_ten_EF\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'all_ten.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     50\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_ten_EF\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Risks'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mall_ten_EF\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Returns'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'g-o'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     51\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[0;32m    674\u001b[0m         )\n\u001b[0;32m    675\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 676\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    677\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    678\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    446\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    447\u001b[0m     \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 448\u001b[1;33m     \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    449\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    450\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m    878\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    879\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 880\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    881\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    882\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m   1112\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"c\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1113\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"c\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1114\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1115\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1116\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"python\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m   1889\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"usecols\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1890\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1891\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1892\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1893\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] File all_ten.csv does not exist: 'all_ten.csv'"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from rf import return_portfolios, optimal_portfolio\n",
    "import numpy as np\n",
    "\n",
    "path='stock_data3.csv'\n",
    "\n",
    "# 1. Load the stock data\n",
    "stock_data = pd.read_csv(path)\n",
    "selected = list(stock_data.columns[1:])\n",
    "print(stock_data[selected].pct_change().mean())\n",
    "print(stock_data[selected].pct_change().cov())\n",
    "\n",
    "#stock_names = ['PFE', 'TGT', 'M', 'VZ', 'JPM', 'MRO', 'KO', 'PG', 'CVS', 'HPQ']\n",
    "selected = ['KO', 'HPQ', 'M', 'VZ', 'MRO']\n",
    "\n",
    "# 2. Find the quarterly for each period\n",
    "returns_quarterly = stock_data[selected].pct_change()\n",
    "\n",
    "# 3. Find the expected returns \n",
    "expected_returns = returns_quarterly.mean()\n",
    "\n",
    "# 4. Find the covariance \n",
    "cov_quarterly = returns_quarterly.cov()\n",
    "\n",
    "# 5. Find a set of random portfolios\n",
    "random_portfolios = return_portfolios(expected_returns, cov_quarterly) \n",
    "\n",
    "# 6. Plot the set of random portfolios\n",
    "random_portfolios.plot.scatter(x='Volatility', y='Returns', fontsize=12)\n",
    "\n",
    "# 7. Calculate the set of portfolios on the EF\n",
    "weights, returns, risks = optimal_portfolio(returns_quarterly[1:])\n",
    "\n",
    "# 8. Plot the set of portfolios on the EF\n",
    "plt.plot(risks, returns, 'y-o')\n",
    "plt.ylabel('Expected Returns',fontsize=14)\n",
    "plt.xlabel('Volatility (Std. Deviation)',fontsize=14)\n",
    "plt.title('Efficient Frontier', fontsize=24)\n",
    "\n",
    "#REMOVE THIS:\n",
    "#pd.DataFrame({'Risks': risks, 'Returns': returns}).to_csv('all_ten.csv', index=False)\n",
    "\n",
    "# 9. Compare the set of portfolios on the EF to the \n",
    "single_asset_std=np.sqrt(np.diagonal(cov_quarterly))\n",
    "plt.scatter(single_asset_std,expected_returns,marker='X',color='red',s=200)\n",
    "\n",
    "# All 10\n",
    "all_ten_EF = pd.read_csv('all_ten.csv')\n",
    "plt.plot(all_ten_EF['Risks'], all_ten_EF['Returns'], 'g-o')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}