diff --git a/examples/notebooks/borinot_arm.ipynb b/examples/notebooks/borinot_arm.ipynb
index 9f2584ccf315d147f988b8599fdf67c29e08f8d8..33b03b1df2bcf050d624b592317e3f44b8506721 100644
--- a/examples/notebooks/borinot_arm.ipynb
+++ b/examples/notebooks/borinot_arm.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 76,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
@@ -44,4055 +44,4055 @@
},
{
"cell_type": "code",
- "execution_count": 80,
+ "execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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+ "[[2.20068115]\n",
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+ "[[2.4861716 ]\n",
+ " [0.92333418]]\n",
+ "[[2.60821958]\n",
+ " [0.93409011]]\n",
+ "[[2.71483825]\n",
+ " [0.94560186]]\n",
+ "[[2.80504432]\n",
+ " [0.95880031]]\n",
+ "[[2.87807671]\n",
+ " [0.97433595]]\n",
+ "[[2.93341342]\n",
+ " [0.99254967]]\n",
+ "[[2.97078061]\n",
+ " [1.01345773]]\n",
+ "[[2.99015385]\n",
+ " [1.03675104]]\n",
+ "[[2.9917515 ]\n",
+ " [1.06180875]]\n",
+ "[[2.9760207 ]\n",
+ " [1.08772537]]\n",
+ "[[2.94361654]\n",
+ " [1.11335014]]\n",
+ "[[2.89537539]\n",
+ " [1.13733712]]\n",
+ "[[2.83228331]\n",
+ " [1.15820385]]\n",
+ "[[2.75544094]\n",
+ " [1.17439621]]\n",
+ "[[2.66602615]\n",
+ " [1.18435695]]\n",
+ "[[2.56525585]\n",
+ " [1.18659515]]\n",
+ "[[2.45434848]\n",
+ " [1.17975368]]\n",
+ "[[2.33448862]\n",
+ " [1.16267219]]\n",
+ "[[2.20679507]\n",
+ " [1.13444267]]\n",
+ "[[2.07229358]\n",
+ " [1.09445556]]\n",
+ "[[1.93189544]\n",
+ " [1.0424342 ]]\n",
+ "[[1.78638261]\n",
+ " [0.97845605]]\n"
]
}
],
@@ -4122,7 +4122,7 @@
},
{
"cell_type": "code",
- "execution_count": 67,
+ "execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
@@ -4134,20 +4134,20 @@
" frame=state, \n",
" ref=state.zero, \n",
" nu=1,\n",
- " activation=ActivationModelWeightedQuad(np.np.array([1]*4+[0.001]*2))) \n",
+ " activation=ActivationModelWeightedQuad(np.array([1]*4+[0.001]*2))) \n",
"\n",
"runningCostModel = CostModelSum(robot.model, nu=1)\n",
"terminalCostModel = CostModelSum(robot.model, nu=1)\n",
"\n",
"runningCostModel.addCost(name=\"regx\", weight=1e-6, cost=xRegCost)\n",
"runningCostModel.addCost(name=\"regu\", weight=1e-6, cost=uRegCost)\n",
- "runningCostModel.addCost(name=\"pend\", weight=0.1, cost=xPendCost)\n",
+ "runningCostModel.addCost(name=\"pend\", weight=1, cost=xPendCost)\n",
"terminalCostModel.addCost(name=\"ori2\", weight=100, cost=xPendCost)"
]
},
{
"cell_type": "code",
- "execution_count": 68,
+ "execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
@@ -4158,7 +4158,7 @@
},
{
"cell_type": "code",
- "execution_count": 69,
+ "execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
@@ -4168,135 +4168,14 @@
"\n",
"# For this optimal control problem, we define 250 knots (or running action\n",
"# models) plus a terminal knot\n",
- "T = 100\n",
- "x0 = np.np.array([3.14, 0, 0., 0. ])\n",
+ "T = 50\n",
+ "x0 = np.array([3.14, 0, 0., 0. ])\n",
"problem = ShootingProblem(x0, [runningModel] * T, terminalModel)"
]
},
{
"cell_type": "code",
- "execution_count": 74,
- "metadata": {},
- "outputs": [
- {
- "ename": "NameError",
- "evalue": "name 'array' is not defined",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m<ipython-input-74-dbb70293a1df>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m xs = [np.array([3.14, 0. , 0. , 0. ]),\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m \u001b[0;36m3.71059542\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m2.05541894\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m11.41190835\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m41.10837881\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m \u001b[0;36m3.76668242\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m2.83395101\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.12174006\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m15.5706414\u001b[0m \u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m \u001b[0;36m3.89056434\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m2.99656298\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2.47763835\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m3.25223937\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m \u001b[0;36m3.71635446\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m3.12552479\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m3.48419748\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m2.57923624\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mNameError\u001b[0m: name 'array' is not defined"
- ]
- }
- ],
- "source": [
- "xs = [np.np.array([3.14, 0. , 0. , 0. ]),\n",
- " np.array([ 3.71059542, -2.05541894, 11.41190835, -41.10837881]),\n",
- " np.array([ 3.76668242, -2.83395101, 1.12174006, -15.5706414 ]),\n",
- " np.array([ 3.89056434, -2.99656298, 2.47763835, -3.25223937]),\n",
- " np.array([ 3.71635446, -3.12552479, -3.48419748, -2.57923624]),\n",
- " np.array([ 3.5244489 , -2.92584309, -3.83811128, 3.99363411]),\n",
- " np.array([ 2.92679904, -2.97209156, -11.95299728, -0.92496958]),\n",
- " np.array([ 2.338523 , -2.87566759, -11.76552081, 1.92847956]),\n",
- " np.array([ 1.95375756, -2.56138768, -7.69530875, 6.28559803]),\n",
- " np.array([ 1.77584689, -2.1567364 , -3.55821333, 8.09302574]),\n",
- " np.array([ 1.72344537, -1.86560016, -1.04803039, 5.82272469]),\n",
- " np.array([ 1.367794 , -1.5842915 , -7.11302751, 5.62617331]),\n",
- " np.array([ 1.34280047, -1.42985981, -0.49987048, 3.08863373]),\n",
- " np.array([ 1.44705705, -1.50587472, 2.08513161, -1.52029819]),\n",
- " np.array([ 1.54391907, -1.58254265, 1.93724031, -1.53335868]),\n",
- " np.array([ 1.51479801, -1.53731134, -0.58242123, 0.90462621]),\n",
- " np.array([ 1.44800478, -1.46329318, -1.33586448, 1.48036335]),\n",
- " np.array([ 1.40542619, -1.41846823, -0.85157192, 0.89649887]),\n",
- " np.array([ 1.36151447, -1.37544602, -0.8782344 , 0.86044414]),\n",
- " np.array([ 1.29170425, -1.29740573, -1.39620442, 1.56080594]),\n",
- " np.array([ 1.21684746, -1.2021926 , -1.49713579, 1.90426261]),\n",
- " np.array([ 1.16579769, -1.13015334, -1.02099531, 1.44078521]),\n",
- " np.array([ 1.18899947, -1.18593751, 0.4640355 , -1.11568352]),\n",
- " np.array([ 1.18923678, -1.19305194, 0.00474629, -0.14228851]),\n",
- " np.array([ 1.18450234, -1.19285596, -0.09468873, 0.00391948]),\n",
- " np.array([ 1.16938901, -1.17758861, -0.30226665, 0.30534706]),\n",
- " np.array([ 1.14860889, -1.1556876 , -0.41560239, 0.43802026]),\n",
- " np.array([ 1.12381992, -1.12905946, -0.49577947, 0.53256269]),\n",
- " np.array([ 1.09763939, -1.10139022, -0.5236106 , 0.55338486]),\n",
- " np.array([ 1.07129065, -1.07415824, -0.52697478, 0.54463952]),\n",
- " np.array([ 1.04520189, -1.04775071, -0.52177524, 0.52815074]),\n",
- " np.array([ 1.01925473, -1.02176134, -0.51894314, 0.51978733]),\n",
- " np.array([ 0.99326029, -0.99576573, -0.51988888, 0.51991216]),\n",
- " np.array([ 0.96713226, -0.96957085, -0.52256045, 0.52389762]),\n",
- " np.array([ 0.94089059, -0.94318632, -0.52483347, 0.52769065]),\n",
- " np.array([ 0.91460743, -0.91671151, -0.52566331, 0.52949614]),\n",
- " np.array([ 0.88836448, -0.89025679, -0.52485899, 0.52909444]),\n",
- " np.array([ 0.8622333 , -0.86391161, -0.52262358, 0.52690367]),\n",
- " np.array([ 0.83627113, -0.83774103, -0.51924341, 0.52341162]),\n",
- " np.array([ 0.81052314, -0.81179221, -0.51495979, 0.51897629]),\n",
- " np.array([ 0.78502583, -0.7861013 , -0.50994604, 0.51381814]),\n",
- " np.array([ 0.75980973, -0.76069794, -0.50432205, 0.50806732]),\n",
- " np.array([ 0.73490108, -0.73560765, -0.49817305, 0.50180573]),\n",
- " np.array([ 0.71032295, -0.71085309, -0.49156268, 0.49509113]),\n",
- " np.array([ 0.68609592, -0.68645468, -0.48454062, 0.48796838]),\n",
- " np.array([ 0.66223857, -0.66243097, -0.4771469 , 0.48047417]),\n",
- " np.array([ 0.63876784, -0.638799 , -0.46941458, 0.47263926]),\n",
- " np.array([ 0.61569926, -0.61557451, -0.46137163, 0.46448992]),\n",
- " np.array([ 0.59304714, -0.59277205, -0.45304233, 0.45604915]),\n",
- " np.array([ 0.57082472, -0.57040516, -0.44444851, 0.44733776]),\n",
- " np.array([ 0.5490442 , -0.5484864 , -0.43561042, 0.43837527]),\n",
- " np.array([ 0.52771682, -0.52702737, -0.42654753, 0.42918064]),\n",
- " np.array([ 0.50685287, -0.50603872, -0.41727905, 0.41977288]),\n",
- " np.array([ 0.48646165, -0.48553015, -0.40782438, 0.41017143]),\n",
- " np.array([ 0.46655148, -0.46551033, -0.39820344, 0.40039651]),\n",
- " np.array([ 0.44712963, -0.44598686, -0.38843682, 0.39046924]),\n",
- " np.array([ 0.42820234, -0.42696627, -0.37854593, 0.38041178]),\n",
- " np.array([ 0.40977469, -0.40845391, -0.36855301, 0.37024729]),\n",
- " np.array([ 0.39185063, -0.39045392, -0.35848111, 0.35999991]),\n",
- " np.array([ 0.37443293, -0.37296918, -0.34835401, 0.34969464]),\n",
- " np.array([ 0.35752313, -0.35600132, -0.33819611, 0.33935717]),\n",
- " np.array([ 0.34112151, -0.33955064, -0.32803223, 0.32901368]),\n",
- " np.array([ 0.32522714, -0.32361611, -0.31788744, 0.31869062]),\n",
- " np.array([ 0.3098378 , -0.30819539, -0.30778687, 0.30841447]),\n",
- " np.array([ 0.29495002, -0.29328481, -0.29775549, 0.29821151]),\n",
- " np.array([ 0.28055913, -0.27887943, -0.28781785, 0.28810754]),\n",
- " np.array([ 0.26665924, -0.26497305, -0.27799791, 0.27812764]),\n",
- " np.array([ 0.2532433 , -0.25155825, -0.26831881, 0.26829597]),\n",
- " np.array([ 0.24030316, -0.23862648, -0.25880271, 0.25863553]),\n",
- " np.array([ 0.22782963, -0.22616808, -0.24947056, 0.249168 ]),\n",
- " np.array([ 0.21581253, -0.2141724 , -0.240342 , 0.23991356]),\n",
- " np.array([ 0.20424077, -0.20262786, -0.23143524, 0.23089078]),\n",
- " np.array([ 0.19310242, -0.19152203, -0.22276693, 0.22211657]),\n",
- " np.array([ 0.18238482, -0.18084173, -0.21435216, 0.21360604]),\n",
- " np.array([ 0.1720746 , -0.1705731 , -0.20620435, 0.20537255]),\n",
- " np.array([ 0.16215783, -0.16070172, -0.1983353 , 0.19742769]),\n",
- " np.array([ 0.15262007, -0.15121265, -0.19075521, 0.18978131]),\n",
- " np.array([ 0.14344644, -0.14209057, -0.18347268, 0.1824416 ]),\n",
- " np.array([ 0.1346217 , -0.13331981, -0.17649481, 0.17541513]),\n",
- " np.array([ 0.12613034, -0.12488446, -0.16982728, 0.16870702]),\n",
- " np.array([ 0.11795661, -0.11676841, -0.16347443, 0.162321 ]),\n",
- " np.array([ 0.11008464, -0.10895544, -0.1574394 , 0.15625955]),\n",
- " np.array([ 0.10249843, -0.10142923, -0.1517242 , 0.15052409]),\n",
- " np.array([ 0.09518194, -0.09417348, -0.14632985, 0.14511507]),\n",
- " np.array([ 0.08811912, -0.08717187, -0.14125652, 0.14003219]),\n",
- " np.array([ 0.08129394, -0.08040814, -0.13650357, 0.13527455]),\n",
- " np.array([ 0.07469045, -0.0738661 , -0.13206966, 0.13084086]),\n",
- " np.array([ 0.06829281, -0.06752961, -0.12795279, 0.12672967]),\n",
- " np.array([ 0.0620853 , -0.06138263, -0.12415021, 0.12293963]),\n",
- " np.array([ 0.05605239, -0.05540914, -0.12065827, 0.11946986]),\n",
- " np.array([ 0.05017879, -0.04959312, -0.11747205, 0.11632045]),\n",
- " np.array([ 0.04444955, -0.04391845, -0.11458473, 0.11349339]),\n",
- " np.array([ 0.03885023, -0.03836872, -0.11198653, 0.11099465]),\n",
- " np.array([ 0.03336706, -0.03292676, -0.10966334, 0.10883912]),\n",
- " np.array([ 0.02798731, -0.02757362, -0.10759493, 0.10706279]),\n",
- " np.array([ 0.02269973, -0.02228629, -0.10575156, 0.10574658]),\n",
- " np.array([ 0.01749586, -0.01703493, -0.10407742, 0.10502727]),\n",
- " np.array([ 0.01237456, -0.01178555, -0.10242597, 0.10498748]),\n",
- " np.array([ 0.00735098, -0.00651136, -0.10047168, 0.10548388]),\n",
- " np.array([ 0.00245966, -0.00117719, -0.09782643, 0.10668329]),\n",
- " np.array([-0.00194355, 0.0032169 , -0.08806415, 0.08788188])]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 70,
+ "execution_count": 19,
"metadata": {
"scrolled": true
},
@@ -4306,447 +4185,231 @@
"output_type": "stream",
"text": [
"iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
- " 0 1.23861e-01 1.86586e+02 -1.56928e+06 1.00000e+02 1.00000e+02 0.0312 0\n",
- " 1 2.87954e-01 1.72339e+03 -1.52928e+06 1.00000e+02 1.00000e+02 0.0156 0\n",
- " 2 5.39916e-01 3.56269e+03 -1.49132e+06 1.00000e+02 1.00000e+02 0.0156 0\n",
- " 3 8.96508e-01 7.01725e+03 -1.45873e+06 1.00000e+02 1.00000e+02 0.0156 0\n",
- " 4 1.37132e+00 1.33917e+04 -1.43153e+06 1.00000e+02 1.00000e+02 0.0156 0\n",
- " 5 1.96286e+00 2.45857e+04 -1.40971e+06 1.00000e+02 1.00000e+02 0.0156 0\n",
- " 6 2.63553e+00 4.20907e+04 -1.39320e+06 1.00000e+02 1.00000e+02 0.0156 0\n",
- " 7 3.31405e+00 6.44937e+04 -1.38188e+06 1.00000e+02 1.00000e+02 0.0156 0\n",
- " 8 4.61426e+00 8.64758e+04 -1.33121e+06 1.00000e+02 1.00000e+02 0.0312 0\n",
- " 9 4.32704e+00 1.36846e+05 -1.46865e+06 1.00000e+02 1.00000e+02 0.0156 0\n",
+ " 0 2.83982e+01 9.27808e+00 -3.85416e+05 1.00000e+01 1.00000e+01 0.0312 0\n",
+ " 1 6.95634e+01 1.20638e+06 -4.07604e+05 1.00000e+01 1.00000e+01 0.0156 0\n",
+ " 2 1.59378e+02 1.68478e+06 -4.41648e+05 1.00000e+01 1.00000e+01 0.0156 0\n",
+ " 3 1.81440e+02 2.44201e+06 -5.21164e+05 1.00000e+01 1.00000e+01 0.0078 0\n",
+ " 4 2.20343e+02 2.25083e+06 -5.73673e+05 1.00000e+01 1.00000e+01 0.0156 0\n",
+ " 5 2.26017e+02 2.08305e+06 -8.98806e+05 1.00000e+01 1.00000e+01 0.0039 0\n",
+ " 6 2.26691e+02 2.05176e+06 -1.02668e+06 1.00000e+01 1.00000e+01 0.0039 0\n",
+ " 7 2.19081e+02 2.11255e+06 -1.19586e+06 1.00000e+01 1.00000e+01 0.0039 0\n",
+ " 8 2.14579e+02 2.53114e+06 -1.45523e+06 1.00000e+02 1.00000e+02 0.0020 0\n",
+ " 9 2.07156e+02 2.36532e+06 -1.59605e+07 1.00000e+02 1.00000e+02 0.0039 0\n",
"iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
- " 10 4.17093e+00 1.36816e+05 -1.52823e+06 1.00000e+02 1.00000e+02 0.0078 0\n",
- " 11 3.97099e+00 1.38023e+05 -1.47497e+06 1.00000e+02 1.00000e+02 0.0078 0\n",
- " 12 3.95794e+00 1.43984e+05 -1.45162e+06 1.00000e+02 1.00000e+02 0.0078 0\n",
- " 13 4.65072e+00 1.33650e+05 -1.44111e+06 1.00000e+02 1.00000e+02 0.0078 0\n",
- " 14 4.76588e+00 1.58157e+05 -1.40792e+06 1.00000e+02 1.00000e+02 0.0078 0\n",
- " 15 4.87966e+00 1.58820e+05 -1.36980e+06 1.00000e+02 1.00000e+02 0.0078 0\n",
- " 16 5.09447e+00 1.59441e+05 -1.29127e+06 1.00000e+02 1.00000e+02 0.0156 0\n",
- " 17 5.54177e+00 1.61417e+05 -1.15753e+06 1.00000e+02 1.00000e+02 0.0156 0\n",
- " 18 5.81175e+00 1.71170e+05 -9.70149e+05 1.00000e+02 1.00000e+02 0.0156 0\n",
- " 19 5.98966e+00 1.74643e+05 -8.08422e+05 1.00000e+02 1.00000e+02 0.0156 0\n",
+ " 10 2.17055e+02 1.66913e+06 -2.25723e+07 1.00000e+03 1.00000e+03 0.0020 0\n",
+ " 11 2.45549e+02 6.16016e+06 -2.59431e+08 1.00000e+03 1.00000e+03 0.0039 0\n",
+ " 12 2.48770e+02 8.06116e+06 -3.10413e+08 1.00000e+04 1.00000e+04 0.0020 0\n",
+ " 13 2.52721e+02 4.32358e+08 -2.95084e+09 1.00000e+05 1.00000e+05 0.0020 0\n",
+ " 14 2.56272e+02 3.85908e+10 -2.67943e+10 1.00000e+06 1.00000e+06 0.0020 0\n",
+ " 15 2.59190e+02 3.87690e+12 -2.36756e+11 1.00000e+07 1.00000e+07 0.0020 0\n",
+ " 16 2.74200e+02 4.08787e+14 -2.05662e+12 1.00000e+07 1.00000e+07 0.0039 0\n",
+ " 17 2.82143e+02 1.50162e+15 -1.55727e+12 1.00000e+07 1.00000e+07 0.0039 0\n",
+ " 18 2.85922e+02 2.40069e+15 -1.18457e+12 1.00000e+07 1.00000e+07 0.0039 0\n",
+ " 19 2.88445e+02 2.69560e+15 -9.11614e+11 1.00000e+07 1.00000e+07 0.0078 0\n",
"iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
- " 20 6.16152e+00 1.76037e+05 -6.72795e+05 1.00000e+02 1.00000e+02 0.0156 0\n",
- " 21 6.42863e+00 1.77806e+05 -5.61106e+05 1.00000e+02 1.00000e+02 0.0156 0\n",
- " 22 6.73548e+00 1.83311e+05 -4.69909e+05 1.00000e+02 1.00000e+02 0.0156 0\n",
- " 23 6.95229e+00 1.90390e+05 -3.94696e+05 1.00000e+02 1.00000e+02 0.0156 0\n",
- " 24 7.31945e+00 1.94028e+05 -3.21455e+05 1.00000e+02 1.00000e+02 0.0312 0\n",
- " 25 7.40508e+00 2.02550e+05 -2.29037e+05 1.00000e+02 1.00000e+02 0.0312 0\n",
- " 26 7.63842e+00 2.02480e+05 -1.66189e+05 1.00000e+02 1.00000e+02 0.0312 0\n",
- " 27 7.96976e+00 2.04366e+05 -1.23731e+05 1.00000e+02 1.00000e+02 0.0312 0\n",
- " 28 8.23922e+00 2.10523e+05 -9.44868e+04 1.00000e+02 1.00000e+02 0.0312 0\n",
- " 29 8.98672e+00 2.14750e+05 -6.88851e+04 1.00000e+02 1.00000e+02 0.0625 0\n",
+ " 20 2.91453e+02 2.42330e+15 -5.79848e+11 1.00000e+07 1.00000e+07 0.0078 0\n",
+ " 21 2.94692e+02 2.50455e+15 -4.00647e+11 1.00000e+07 1.00000e+07 0.0078 0\n",
+ " 22 2.99026e+02 3.30433e+15 -2.95408e+11 1.00000e+07 1.00000e+07 0.0078 0\n",
+ " 23 3.02757e+02 1.24877e+16 -2.28527e+11 1.00000e+07 1.00000e+07 0.0078 0\n",
+ " 24 3.04889e+02 4.43691e+15 -1.83230e+11 1.00000e+07 1.00000e+07 0.0078 0\n",
+ " 25 3.05726e+02 4.33095e+15 -1.50974e+11 1.00000e+07 1.00000e+07 0.0078 0\n",
+ " 26 3.07955e+02 3.23622e+15 -1.27075e+11 1.00000e+07 1.00000e+07 0.0078 0\n",
+ " 27 3.10313e+02 2.53741e+15 -1.08786e+11 1.00000e+07 1.00000e+07 0.0078 0\n",
+ " 28 3.12410e+02 2.25850e+15 -9.44176e+10 1.00000e+07 1.00000e+07 0.0078 0\n",
+ " 29 3.15447e+02 2.04771e+15 -8.15636e+10 1.00000e+07 1.00000e+07 0.0156 0\n",
"iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
- " 30 9.22857e+00 2.32228e+05 -4.50699e+04 1.00000e+02 1.00000e+02 0.0625 0\n",
- " 31 9.39012e+00 2.33438e+05 -3.13395e+04 1.00000e+02 1.00000e+02 0.0625 0\n",
- " 32 9.60129e+00 2.33613e+05 -1.97340e+04 1.00000e+02 1.00000e+02 0.1250 0\n",
- " 33 9.61976e+00 2.33014e+05 -8.01319e+03 1.00000e+02 1.00000e+02 0.2500 0\n",
- " 34 9.86093e+00 2.25240e+05 8.37669e-03 1.00000e+02 1.00000e+02 0.5000 0\n",
- " 35 9.92731e+00 2.19184e+05 -9.94228e+02 1.00000e+02 1.00000e+02 0.2500 0\n",
- " 36 9.76790e+00 2.16211e+05 7.23548e-03 1.00000e+02 1.00000e+02 0.5000 0\n",
- " 37 9.79297e+00 2.02314e+05 1.26367e-01 1.00000e+02 1.00000e+02 0.5000 0\n",
- " 38 9.75768e+00 1.93974e+05 1.17514e-01 1.00000e+02 1.00000e+02 0.5000 0\n",
- " 39 9.71158e+00 1.85253e+05 1.12120e-01 1.00000e+02 1.00000e+02 0.5000 0\n",
+ " 30 3.20767e+02 1.71853e+15 -6.42512e+10 1.00000e+07 1.00000e+07 0.0156 0\n",
+ " 31 3.24924e+02 1.73009e+15 -5.21326e+10 1.00000e+07 1.00000e+07 0.0156 0\n",
+ " 32 3.28408e+02 1.85535e+15 -4.32293e+10 1.00000e+07 1.00000e+07 0.0156 0\n",
+ " 33 3.30399e+02 2.18508e+15 -3.64437e+10 1.00000e+07 1.00000e+07 0.0156 0\n",
+ " 34 3.30158e+02 3.01798e+15 -3.11217e+10 1.00000e+07 1.00000e+07 0.0156 0\n",
+ " 35 3.31341e+02 5.44947e+15 -2.72829e+10 1.00000e+07 1.00000e+07 0.0078 0\n",
+ " 36 3.32504e+02 9.06935e+15 -2.54378e+10 1.00000e+07 1.00000e+07 0.0078 0\n",
+ " 37 3.34891e+02 2.05998e+16 -2.37600e+10 1.00000e+07 1.00000e+07 0.0078 0\n",
+ " 38 3.32239e+02 9.83463e+16 -2.22233e+10 1.00000e+07 1.00000e+07 0.0078 0\n",
+ " 39 3.32678e+02 1.60644e+17 -2.08275e+10 1.00000e+07 1.00000e+07 0.0078 0\n",
"iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
- " 40 9.66298e+00 1.76815e+05 1.07005e-01 1.00000e+02 1.00000e+02 0.5000 0\n",
- " 41 9.61461e+00 1.68674e+05 1.02097e-01 1.00000e+02 1.00000e+02 0.5000 0\n",
- " 42 9.56752e+00 1.60855e+05 9.74035e-02 1.00000e+02 1.00000e+02 0.5000 0\n",
- " 43 9.47799e+00 1.53372e+05 1.74671e-01 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 44 9.39568e+00 1.39259e+05 8.44952e-02 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 45 9.32081e+00 1.26418e+05 7.68473e-02 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 46 9.25268e+00 1.14764e+05 6.99156e-02 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 47 9.19065e+00 1.04188e+05 6.36339e-02 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 48 9.13416e+00 9.45950e+04 5.79420e-02 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 49 9.08269e+00 8.58949e+04 5.27850e-02 1.00000e+01 1.00000e+01 1.0000 1\n",
+ " 40 3.33193e+02 6.71628e+16 -1.95422e+10 1.00000e+07 1.00000e+07 0.0078 0\n",
+ " 41 3.34101e+02 3.48634e+16 -1.83590e+10 1.00000e+07 1.00000e+07 0.0078 0\n",
+ " 42 3.36775e+02 1.64924e+16 -1.69933e+10 1.00000e+07 1.00000e+07 0.0156 0\n",
+ " 43 3.39209e+02 3.27752e+15 -1.50687e+10 1.00000e+07 1.00000e+07 0.0156 0\n",
+ " 44 3.41330e+02 1.47937e+15 -1.34126e+10 1.00000e+07 1.00000e+07 0.0156 0\n",
+ " 45 1.96754e+06 9.58405e+14 -1.16229e+10 1.00000e+07 1.00000e+07 0.0312 0\n",
+ " 46 1.96755e+06 1.55207e+15 -9.35168e+09 1.00000e+07 1.00000e+07 0.0312 0\n",
+ " 47 1.96755e+06 1.41239e+15 -7.07277e+09 1.00000e+07 1.00000e+07 0.0625 0\n",
+ " 48 1.96755e+06 1.19413e+15 -4.71613e+09 1.00000e+07 1.00000e+07 0.0625 0\n",
+ " 49 1.96755e+06 1.05834e+15 -3.22724e+09 1.00000e+07 1.00000e+07 0.0625 0\n",
"iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
- " 50 9.03577e+00 7.80067e+04 4.81132e-02 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 51 8.99296e+00 7.08562e+04 4.38813e-02 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 52 8.95388e+00 6.43758e+04 4.00482e-02 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 53 8.91818e+00 5.85038e+04 3.65766e-02 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 54 8.88553e+00 5.31841e+04 3.34326e-02 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 55 8.85566e+00 4.83657e+04 3.05855e-02 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 56 8.82831e+00 4.40020e+04 2.80073e-02 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 57 8.80322e+00 4.00507e+04 2.56728e-02 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 58 8.78019e+00 3.64735e+04 2.35589e-02 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 59 8.75903e+00 3.32353e+04 2.16450e-02 1.00000e+01 1.00000e+01 1.0000 1\n",
+ " 50 1.96755e+06 9.38407e+14 -1.93985e+09 1.00000e+07 1.00000e+07 0.1250 0\n",
+ " 51 1.96756e+06 7.32415e+14 -1.01845e+09 1.00000e+07 1.00000e+07 0.1250 0\n",
+ " 52 1.96756e+06 5.61397e+14 -6.11271e+08 1.00000e+07 1.00000e+07 0.1250 0\n",
+ " 53 1.96755e+06 4.23222e+14 -2.76927e+08 1.00000e+07 1.00000e+07 0.2500 0\n",
+ " 54 1.96755e+06 2.44829e+14 -1.61554e+08 1.00000e+07 1.00000e+07 0.2500 0\n",
+ " 55 1.96755e+06 1.41363e+14 -1.02799e+08 1.00000e+07 1.00000e+07 0.2500 0\n",
+ " 56 1.96755e+06 7.97816e+13 -6.45227e+07 1.00000e+07 1.00000e+07 0.2500 0\n",
+ " 57 1.96755e+06 4.40816e+13 4.54441e+04 1.00000e+07 1.00000e+07 0.5000 0\n",
+ " 58 1.96755e+06 1.12670e+13 -2.69749e+02 1.00000e+07 1.00000e+07 0.5000 0\n",
+ " 59 1.96755e+06 2.74090e+12 5.89423e+06 1.00000e+06 1.00000e+06 1.0000 1\n",
"iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
- " 60 8.73955e+00 3.03045e+04 1.99122e-02 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 61 8.72160e+00 2.76522e+04 1.83433e-02 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 62 8.70503e+00 2.52524e+04 1.69229e-02 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 63 8.68972e+00 2.30811e+04 1.56369e-02 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 64 8.67554e+00 2.11170e+04 1.44726e-02 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 65 8.66239e+00 1.93405e+04 1.34186e-02 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 66 8.65016e+00 1.77339e+04 1.24643e-02 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 67 8.63878e+00 1.62811e+04 1.16003e-02 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 68 8.62816e+00 1.49675e+04 1.08182e-02 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 69 8.61823e+00 1.37799e+04 1.01100e-02 1.00000e+01 1.00000e+01 1.0000 1\n",
+ " 60 1.96755e+06 5.33750e+08 3.89237e-01 1.00000e+05 1.00000e+05 1.0000 1\n",
+ " 61 1.96754e+06 5.33723e+08 3.89244e+00 1.00000e+04 1.00000e+04 1.0000 1\n",
+ " 62 1.96751e+06 5.33447e+08 3.89311e+01 1.00000e+03 1.00000e+03 1.0000 1\n",
+ " 63 1.96712e+06 5.30469e+08 3.89989e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
+ " 64 1.96673e+06 5.29894e+08 3.89982e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
+ " 65 1.96634e+06 5.29334e+08 3.89978e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
+ " 66 1.96595e+06 5.28788e+08 3.89975e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
+ " 67 1.96556e+06 5.28257e+08 3.89973e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
+ " 68 1.96517e+06 5.27740e+08 3.89973e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
+ " 69 1.96478e+06 5.27238e+08 3.89975e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
"iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
- " 70 8.60893e+00 1.27065e+04 9.46882e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 71 8.60019e+00 1.17363e+04 8.88830e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 72 8.59196e+00 1.08595e+04 8.36267e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 73 8.58420e+00 1.00673e+04 7.88673e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 74 8.57686e+00 9.35153e+03 7.45576e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 75 8.56989e+00 8.70497e+03 7.06549e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 76 8.56327e+00 8.12098e+03 6.71206e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 77 8.55697e+00 7.59359e+03 6.39197e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 78 8.55094e+00 7.11738e+03 6.10205e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 79 8.54518e+00 6.68745e+03 5.83945e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
+ " 70 1.96439e+06 5.26749e+08 3.89977e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
+ " 71 1.96400e+06 5.26274e+08 3.89981e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
+ " 72 1.96361e+06 5.25812e+08 3.89986e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
+ " 73 1.96322e+06 5.25364e+08 3.89992e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
+ " 74 1.96283e+06 5.24929e+08 3.89999e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
+ " 75 1.96244e+06 5.24506e+08 3.90006e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
+ " 76 1.96205e+06 5.24096e+08 3.90015e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
+ " 77 1.96166e+06 5.23698e+08 3.90024e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
+ " 78 1.96127e+06 5.23312e+08 3.90034e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
+ " 79 1.96088e+06 5.22937e+08 3.90044e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
"iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
- " 80 8.53964e+00 6.29936e+03 5.60156e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 81 8.53431e+00 5.94911e+03 5.38604e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 82 8.52918e+00 5.63305e+03 5.19075e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 83 8.52421e+00 5.34789e+03 5.01379e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 84 8.51941e+00 5.09067e+03 4.85341e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 85 8.51474e+00 4.85868e+03 4.70803e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 86 8.51020e+00 4.64950e+03 4.57623e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 87 8.50578e+00 4.46091e+03 4.45672e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 88 8.50147e+00 4.29093e+03 4.34832e-03 1.00000e+01 1.00000e+01 1.0000 1\n"
+ " 80 1.96049e+06 5.22574e+08 3.90055e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
+ " 81 1.96010e+06 5.22222e+08 3.90065e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
+ " 82 1.95971e+06 5.21880e+08 3.90077e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
+ " 83 1.95932e+06 5.21548e+08 3.90088e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
+ " 84 1.95893e+06 5.21227e+08 3.90100e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
+ " 85 1.95854e+06 5.20915e+08 3.90111e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
+ " 86 1.95815e+06 5.20612e+08 3.90122e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
+ " 87 1.95775e+06 5.20318e+08 3.90133e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
+ " 88 1.95736e+06 5.20033e+08 3.90144e+02 1.00000e+02 1.00000e+02 1.0000 1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- " 89 8.49725e+00 4.13776e+03 4.24999e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
+ " 89 1.95697e+06 5.19755e+08 3.90155e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
"iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
- " 90 8.49312e+00 3.99976e+03 4.16077e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 91 8.48907e+00 3.87546e+03 4.07979e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 92 8.48508e+00 3.76352e+03 4.00626e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 93 8.48117e+00 3.66274e+03 3.93949e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 94 8.47731e+00 3.57204e+03 3.87882e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 95 8.47351e+00 3.49042e+03 3.82368e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 96 8.46975e+00 3.41700e+03 3.77354e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 97 8.46604e+00 3.35096e+03 3.72793e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 98 8.46238e+00 3.29159e+03 3.68642e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 99 8.45874e+00 3.23822e+03 3.64861e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- "iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
- " 100 8.45514e+00 3.19026e+03 3.61416e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 101 8.45158e+00 3.14717e+03 3.58274e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 102 8.44804e+00 3.10847e+03 3.55407e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 103 8.44452e+00 3.07371e+03 3.52789e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 104 8.44103e+00 3.04251e+03 3.50396e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 105 8.43756e+00 3.01451e+03 3.48207e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 106 8.43411e+00 2.98938e+03 3.46202e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 107 8.43068e+00 2.96682e+03 3.44363e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 108 8.42726e+00 2.94659e+03 3.42676e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 109 8.42386e+00 2.92843e+03 3.41125e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- "iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
- " 110 8.42047e+00 2.91214e+03 3.39698e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 111 8.41710e+00 2.89752e+03 3.38383e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 112 8.41374e+00 2.88439e+03 3.37170e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 113 8.41039e+00 2.87260e+03 3.36048e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 114 8.40705e+00 2.86201e+03 3.35009e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 115 8.40372e+00 2.85249e+03 3.34045e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 116 8.40039e+00 2.84391e+03 3.33149e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 117 8.39708e+00 2.83619e+03 3.32315e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 118 8.39377e+00 2.82922e+03 3.31536e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 119 8.39047e+00 2.82292e+03 3.30808e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- "iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
- " 120 8.38718e+00 2.81721e+03 3.30125e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 121 8.38389e+00 2.81202e+03 3.29484e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 122 8.38061e+00 2.80730e+03 3.28881e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 123 8.37734e+00 2.80298e+03 3.28311e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 124 8.37407e+00 2.79902e+03 3.27772e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 125 8.37080e+00 2.79538e+03 3.27261e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 126 8.36754e+00 2.79200e+03 3.26774e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 127 8.36429e+00 2.78886e+03 3.26311e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 128 8.36104e+00 2.78593e+03 3.25868e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 129 8.35779e+00 2.78316e+03 3.25445e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- "iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
- " 130 8.35455e+00 2.78055e+03 3.25038e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 131 8.35131e+00 2.77806e+03 3.24646e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 132 8.34807e+00 2.77567e+03 3.24268e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 133 8.34484e+00 2.77337e+03 3.23903e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 134 8.34161e+00 2.77113e+03 3.23549e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 135 8.33839e+00 2.76895e+03 3.23206e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 136 8.33517e+00 2.76681e+03 3.22872e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 137 8.33195e+00 2.76470e+03 3.22547e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 138 8.32873e+00 2.76261e+03 3.22229e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 139 8.32552e+00 2.76052e+03 3.21918e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- "iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
- " 140 8.32231e+00 2.75844e+03 3.21614e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 141 8.31911e+00 2.75635e+03 3.21315e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 142 8.31590e+00 2.75425e+03 3.21022e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 143 8.31270e+00 2.75213e+03 3.20733e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 144 8.30951e+00 2.74999e+03 3.20449e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 145 8.30631e+00 2.74782e+03 3.20169e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 146 8.30312e+00 2.74562e+03 3.19892e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 147 8.29993e+00 2.74339e+03 3.19618e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 148 8.29674e+00 2.74112e+03 3.19348e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 149 8.29356e+00 2.73882e+03 3.19080e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- "iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
- " 150 8.29038e+00 2.73648e+03 3.18815e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 151 8.28720e+00 2.73409e+03 3.18552e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 152 8.28402e+00 2.73167e+03 3.18290e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 153 8.28085e+00 2.72920e+03 3.18031e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 154 8.27768e+00 2.72669e+03 3.17774e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 155 8.27451e+00 2.72413e+03 3.17518e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 156 8.27134e+00 2.72153e+03 3.17263e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 157 8.26818e+00 2.71889e+03 3.17010e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 158 8.26502e+00 2.71620e+03 3.16758e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 159 8.26186e+00 2.71347e+03 3.16507e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- "iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
- " 160 8.25871e+00 2.71070e+03 3.16257e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 161 8.25555e+00 2.70788e+03 3.16008e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 162 8.25240e+00 2.70503e+03 3.15760e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 163 8.24925e+00 2.70213e+03 3.15512e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 164 8.24611e+00 2.69919e+03 3.15266e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 165 8.24296e+00 2.69621e+03 3.15020e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 166 8.23982e+00 2.69319e+03 3.14774e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 167 8.23669e+00 2.69013e+03 3.14530e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 168 8.23355e+00 2.68704e+03 3.14285e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 169 8.23042e+00 2.68391e+03 3.14042e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- "iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
- " 170 8.22728e+00 2.68075e+03 3.13799e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 171 8.22415e+00 2.67755e+03 3.13556e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 172 8.22103e+00 2.67431e+03 3.13313e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 173 8.21790e+00 2.67105e+03 3.13071e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 174 8.21478e+00 2.66775e+03 3.12830e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 175 8.21166e+00 2.66442e+03 3.12589e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 176 8.20855e+00 2.66107e+03 3.12348e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 177 8.20543e+00 2.65768e+03 3.12107e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 178 8.20232e+00 2.65427e+03 3.11867e-03 1.00000e+01 1.00000e+01 1.0000 1\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " 179 8.19921e+00 2.65083e+03 3.11627e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- "iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
- " 180 8.19610e+00 2.64737e+03 3.11387e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 181 8.19300e+00 2.64388e+03 3.11148e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 182 8.18989e+00 2.64037e+03 3.10909e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 183 8.18679e+00 2.63683e+03 3.10670e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 184 8.18370e+00 2.63328e+03 3.10431e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 185 8.18060e+00 2.62970e+03 3.10193e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 186 8.17751e+00 2.62611e+03 3.09954e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 187 8.17442e+00 2.62249e+03 3.09716e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 188 8.17133e+00 2.61886e+03 3.09479e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 189 8.16824e+00 2.61521e+03 3.09241e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- "iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
- " 190 8.16516e+00 2.61155e+03 3.09004e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 191 8.16208e+00 2.60787e+03 3.08766e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 192 8.15900e+00 2.60418e+03 3.08529e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 193 8.15592e+00 2.60047e+03 3.08292e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 194 8.15285e+00 2.59675e+03 3.08056e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 195 8.14978e+00 2.59302e+03 3.07819e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 196 8.14671e+00 2.58927e+03 3.07583e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 197 8.14364e+00 2.58552e+03 3.07347e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 198 8.14057e+00 2.58176e+03 3.07111e-03 1.00000e+01 1.00000e+01 1.0000 1\n",
- " 199 8.13751e+00 2.57798e+03 3.06875e-03 1.00000e+01 1.00000e+01 1.0000 1\n"
+ " 90 1.95658e+06 5.19486e+08 3.90165e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
+ " 91 1.95619e+06 5.19224e+08 3.90174e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
+ " 92 1.95580e+06 5.18969e+08 3.90183e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
+ " 93 1.95541e+06 5.18721e+08 3.90191e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
+ " 94 1.95502e+06 5.18479e+08 3.90198e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
+ " 95 1.95463e+06 5.18243e+08 3.90205e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
+ " 96 1.95424e+06 5.18013e+08 3.90210e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
+ " 97 1.95385e+06 5.17789e+08 3.90214e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
+ " 98 1.95346e+06 5.17569e+08 3.90217e+02 1.00000e+02 1.00000e+02 1.0000 1\n",
+ " 99 1.95307e+06 5.17354e+08 3.90219e+02 1.00000e+02 1.00000e+02 1.0000 1\n"
]
},
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" False)"
]
},
- "execution_count": 70,
+ "execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
@@ -4763,7 +4426,7 @@
},
{
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- "execution_count": 71,
+ "execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
@@ -4772,144 +4435,24 @@
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{
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- "source": [
- "ddp.xs"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 58,
+ "execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
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]
},
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{
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\n",
+ "image/png": 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\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7fa29910df50>"
+ "<Figure size 1080x720 with 2 Axes>"
]
},
"metadata": {
@@ -4959,7 +4502,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
- "version": "2.7.12"
+ "version": "2.7.16"
}
},
"nbformat": 4,