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Commit 44f18827 authored by Dr.李's avatar Dr.李
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delete 2 examples

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notebooks/Linear Optimizer Check.ipynb deleted 100644 → 0
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import datetime as dt\n",
"import numpy as np\n",
"import cvxpy\n",
"from cvxopt import solvers\n",
"from alphamind.portfolio.linearbuilder import linear_build"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"solvers.options['glpk'] = {'msg_lev': 'GLP_MSG_OFF'}"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def time_function(py_callable, n):\n",
" start = dt.datetime.now()\n",
" result = py_callable(n)\n",
" elapsed = (dt.datetime.now() - start).total_seconds()\n",
" return elapsed, result\n",
"\n",
"def cvxpy_lp(n):\n",
" w = cvxpy.Variable(n)\n",
"\n",
" bndl = np.zeros(n)\n",
" bndu = 0.01 * np.ones(n)\n",
" risk_constraints1 = np.ones((n,1))\n",
" risk_constraints2 = np.zeros((n,1))\n",
" risk_constraints2[0][0] = 1.\n",
" risk_constraints2[1][0] = 1.\n",
" risk_constraints = np.concatenate((risk_constraints1, risk_constraints2), axis=1)\n",
"\n",
" curr_risk_exposure = risk_constraints.T @ w\n",
" risk_targets = np.array([1., 0.015])\n",
"\n",
" constraints = [w >= bndl,\n",
" w <= bndu,\n",
" curr_risk_exposure >= risk_targets,\n",
" curr_risk_exposure <= risk_targets]\n",
" \n",
" np.random.seed(1)\n",
" er = np.random.randn(n)\n",
"\n",
" objective = cvxpy.Minimize(-w.T * er)\n",
" prob = cvxpy.Problem(objective, constraints)\n",
" prob.solve(solver='GLPK')\n",
" return w, prob"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Scale(n) time(ms) feval min(x) max(x) sum(x) x(0) + x(1)\n",
"200 18.01 -0.82 0.000000 0.010000 1.000000 0.015\n",
"400 23.02 -1.28 -0.000000 0.010000 1.000000 0.015\n",
"600 42.63 -1.54 -0.000000 0.010000 1.000000 0.015\n",
"800 62.04 -1.63 -0.000000 0.010000 1.000000 0.015\n",
"1000 76.57 -1.72 -0.000000 0.010000 1.000000 0.015\n",
"1200 108.73 -1.81 -0.000000 0.010000 1.000000 0.015\n",
"1400 136.22 -1.90 -0.000000 0.010000 1.000000 0.015\n",
"1600 166.64 -1.96 -0.000000 0.010000 1.000000 0.015\n",
"1800 197.72 -2.03 -0.000000 0.010000 1.000000 0.015\n",
"2000 258.51 -2.06 -0.000000 0.010000 1.000000 0.015\n",
"2200 291.34 -2.07 -0.000000 0.010000 1.000000 0.015\n",
"2400 348.30 -2.13 -0.000000 0.010000 1.000000 0.015\n",
"2600 398.31 -2.14 -0.000000 0.010000 1.000000 0.015\n",
"2800 462.13 -2.16 -0.000000 0.010000 1.000000 0.015\n",
"3000 547.84 -2.19 -0.000000 0.010000 1.000000 0.015\n"
]
}
],
"source": [
"print(\"{0:<8}{1:>12}{2:>12}{3:>12}{4:>12}{5:>12}{6:>15}\".format('Scale(n)', 'time(ms)', 'feval', 'min(x)', 'max(x)', 'sum(x)', 'x(0) + x(1)'))\n",
"\n",
"for n in range(200, 3200, 200):\n",
" elapsed, result = time_function(cvxpy_lp, n)\n",
" s = np.array(result[0].value).flatten()\n",
" print(\"{0:<8}{1:>12.2f}{2:>12.2f}{3:>12f}{4:>12f}{5:>12f}{6:>15}\".format(n, elapsed*1000, result[1].value, s.min(), s.max(), s.sum(), s[0] + s[1]))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def clp_lp(n):\n",
" np.random.seed(1)\n",
" er = np.random.randn(n)\n",
"\n",
" bndl = np.zeros(n)\n",
" bndu = 0.01 * np.ones(n)\n",
" risk_constraints1 = np.ones((n,1))\n",
" risk_constraints2 = np.zeros((n,1))\n",
" risk_constraints2[0][0] = 1.\n",
" risk_constraints2[1][0] = 1.\n",
" risk_constraints = np.concatenate((risk_constraints1, risk_constraints2), axis=1)\n",
" risk_target = np.array([1., 0.015]), np.array([1., 0.015])\n",
" \n",
" result = linear_build(er, bndl, bndu, risk_constraints, risk_target)\n",
" return result"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Scale(n) time(ms) feval min(x) max(x) sum(x) x(0) + x(1)\n",
"200 19.01 -0.82 0.000000 0.010000 1.000000 0.015\n",
"400 1.00 -1.28 0.000000 0.010000 1.000000 0.015\n",
"600 2.00 -1.54 0.000000 0.010000 1.000000 0.015\n",
"800 3.00 -1.63 0.000000 0.010000 1.000000 0.015\n",
"1000 2.00 -1.72 0.000000 0.010000 1.000000 0.015\n",
"1200 3.00 -1.81 0.000000 0.010000 1.000000 0.015\n",
"1400 2.02 -1.90 0.000000 0.010000 1.000000 0.015\n",
"1600 2.02 -1.96 0.000000 0.010000 1.000000 0.015\n",
"1800 1.98 -2.03 0.000000 0.010000 1.000000 0.015\n",
"2000 2.02 -2.06 0.000000 0.010000 1.000000 0.015\n",
"2200 2.00 -2.07 0.000000 0.010000 1.000000 0.015\n",
"2400 2.00 -2.13 0.000000 0.010000 1.000000 0.015\n",
"2600 3.00 -2.14 0.000000 0.010000 1.000000 0.015\n",
"2800 3.02 -2.16 0.000000 0.010000 1.000000 0.015\n",
"3000 3.00 -2.19 0.000000 0.010000 1.000000 0.015\n"
]
}
],
"source": [
"print(\"{0:<8}{1:>12}{2:>12}{3:>12}{4:>12}{5:>12}{6:>15}\".format('Scale(n)', 'time(ms)', 'feval', 'min(x)', 'max(x)', 'sum(x)', 'x(0) + x(1)'))\n",
"\n",
"for n in range(200, 3200, 200):\n",
" elapsed, result = time_function(clp_lp, n)\n",
" s = result[2]\n",
" print(\"{0:<8}{1:>12.2f}{2:>12.2f}{3:>12f}{4:>12f}{5:>12f}{6:>15}\".format(n, elapsed*1000, result[1], s.min(), s.max(), s.sum(), s[0] + s[1]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"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": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
notebooks/Quadratic Optimizer Check.ipynb deleted 100644 → 0
View file @ a004afbf
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from cvxpy import *\n",
"from cvxopt import *\n",
"from alphamind.cython.optimizers import QPOptimizer"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data Preparing\n",
"--------------------------"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"risk_penlty = 0.5"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sec_cov_values_full = np.genfromtxt('sec_cov_values.csv', delimiter=',')\n",
"signal_full = np.genfromtxt('signal.csv', delimiter=',')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"n = 200\n",
"\n",
"sec_cov_values = sec_cov_values_full[:n, :n]\n",
"signal = signal_full[:n]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Optimizing Weights\n",
"-------------------------------------"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 121 ms\n"
]
}
],
"source": [
"%%time\n",
"w = Variable(n)\n",
"\n",
"lbound = 0.\n",
"ubound = 1. / n * 20\n",
"\n",
"objective = Minimize(risk_penlty * quad_form(w, sec_cov_values) - signal * w)\n",
"constraints = [w >= lbound,\n",
" w <= ubound,\n",
" sum_entries(w) == 1,]\n",
"\n",
"prob = Problem(objective, constraints)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"ECOS 2.0.4 - (C) embotech GmbH, Zurich Switzerland, 2012-15. Web: www.embotech.com/ECOS\n",
"\n",
"It pcost dcost gap pres dres k/t mu step sigma IR | BT\n",
" 0 -2.559e-02 -3.082e+01 +3e+03 9e-01 8e-05 1e+00 7e+00 --- --- 1 1 - | - - \n",
" 1 -2.473e+01 -2.541e+01 +7e+01 1e-01 2e-06 1e-02 2e-01 0.9779 1e-04 1 2 2 | 0 0\n",
" 2 -1.412e+00 -1.437e+00 +3e+00 5e-03 6e-08 5e-04 7e-03 0.9598 2e-03 2 3 3 | 0 0\n",
" 3 -1.019e+00 -1.035e+00 +2e+00 3e-03 4e-08 1e-03 5e-03 0.4692 3e-01 3 4 4 | 0 0\n",
" 4 -9.719e-01 -9.842e-01 +2e+00 3e-03 3e-08 2e-03 4e-03 0.5240 6e-01 3 4 3 | 0 0\n",
" 5 -3.633e-01 -3.662e-01 +4e-01 7e-04 8e-09 6e-04 1e-03 0.8629 1e-01 3 4 4 | 0 0\n",
" 6 -2.058e-01 -2.066e-01 +1e-01 2e-04 2e-09 2e-04 3e-04 0.8197 1e-01 3 4 4 | 0 0\n",
" 7 -1.579e-01 -1.581e-01 +3e-02 5e-05 6e-10 5e-05 7e-05 0.8711 1e-01 3 3 3 | 0 0\n",
" 8 -1.495e-01 -1.496e-01 +1e-02 2e-05 2e-10 2e-05 3e-05 0.7478 2e-01 2 3 3 | 0 0\n",
" 9 -1.468e-01 -1.468e-01 +7e-03 1e-05 2e-10 1e-05 2e-05 0.7025 5e-01 3 4 4 | 0 0\n",
"10 -1.440e-01 -1.440e-01 +1e-03 2e-06 3e-11 3e-06 4e-06 0.9500 1e-01 2 2 2 | 0 0\n",
"11 -1.434e-01 -1.434e-01 +2e-04 3e-07 4e-12 4e-07 5e-07 0.8916 4e-02 3 4 3 | 0 0\n",
"12 -1.433e-01 -1.433e-01 +2e-05 3e-08 3e-13 3e-08 4e-08 0.9253 3e-03 2 2 2 | 0 0\n",
"13 -1.433e-01 -1.433e-01 +7e-07 2e-09 1e-14 1e-09 2e-09 0.9585 5e-04 2 1 2 | 0 0\n",
"14 -1.433e-01 -1.433e-01 +4e-08 1e-09 9e-16 8e-11 1e-10 0.9563 2e-02 3 2 2 | 0 0\n",
"15 -1.433e-01 -1.433e-01 +1e-09 1e-10 5e-17 3e-12 4e-12 0.9676 8e-04 3 2 2 | 0 0\n",
"\n",
"OPTIMAL (within feastol=1.5e-10, reltol=1.0e-08, abstol=1.5e-09).\n",
"Runtime: 0.184433 seconds.\n",
"\n",
"Wall time: 265 ms\n"
]
},
{
"data": {
"text/plain": [
"-0.14330450116149787"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"prob.solve(verbose=True)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"('optimal', -0.14330450116149787)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prob.status, prob.value"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" pcost dcost gap pres dres k/t\n",
" 0: -2.3359e-02 -3.1007e+01 7e+02 1e+01 9e-03 1e+00\n",
" 1: -1.1146e+01 -1.5653e+01 6e+01 1e+00 1e-03 2e-02\n",
" 2: -1.7778e+00 -2.7042e+00 7e+00 3e-01 3e-04 3e-02\n",
" 3: -3.8072e-01 -5.2347e-01 8e-01 5e-02 4e-05 5e-03\n",
" 4: -2.0231e-01 -2.3403e-01 2e-01 1e-02 9e-06 1e-03\n",
" 5: -1.5540e-01 -1.6212e-01 3e-02 2e-03 2e-06 2e-04\n",
" 6: -1.4464e-01 -1.4561e-01 4e-03 3e-04 3e-07 3e-05\n",
" 7: -1.4358e-01 -1.4376e-01 8e-04 6e-05 5e-08 4e-06\n",
" 8: -1.4340e-01 -1.4346e-01 3e-04 2e-05 2e-08 1e-06\n",
" 9: -1.4333e-01 -1.4334e-01 5e-05 4e-06 4e-09 2e-07\n",
"10: -1.4331e-01 -1.4331e-01 8e-06 6e-07 5e-10 3e-08\n",
"11: -1.4330e-01 -1.4330e-01 4e-07 3e-08 3e-11 1e-09\n",
"12: -1.4330e-01 -1.4330e-01 3e-08 2e-09 7e-12 9e-11\n",
"Optimal solution found.\n",
"Wall time: 195 ms\n"
]
},
{
"data": {
"text/plain": [
"-0.1433045111926663"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"prob.solve(verbose=True, solver='CVXOPT')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"('optimal', -0.1433045111926663)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prob.status, prob.value"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" pcost dcost gap pres dres\n",
" 0: -2.6176e-01 -2.1588e+01 5e+02 2e+01 1e-15\n",
" 1: -2.1701e-01 -1.8470e+01 4e+01 8e-01 2e-15\n",
" 2: -4.4680e-02 -5.2189e+00 6e+00 5e-02 2e-15\n",
" 3: -4.2473e-02 -1.4144e+00 1e+00 1e-02 3e-15\n",
" 4: -9.7185e-02 -3.4512e-01 3e-01 1e-03 2e-15\n",
" 5: -1.2997e-01 -1.8100e-01 5e-02 1e-04 1e-15\n",
" 6: -1.4069e-01 -1.4831e-01 8e-03 1e-05 1e-15\n",
" 7: -1.4300e-01 -1.4373e-01 7e-04 9e-07 1e-15\n",
" 8: -1.4329e-01 -1.4332e-01 3e-05 2e-08 1e-15\n",
" 9: -1.4330e-01 -1.4330e-01 8e-07 3e-10 1e-15\n",
"10: -1.4330e-01 -1.4330e-01 5e-08 6e-12 1e-15\n",
"Optimal solution found.\n",
"Wall time: 55.1 ms\n"
]
}
],
"source": [
"%%time\n",
"P = matrix(sec_cov_values)\n",
"q = -matrix(signal)\n",
"\n",
"G = np.zeros((2*n, n))\n",
"h = np.zeros(2*n)\n",
"for i in range(n):\n",
" G[i, i] = 1.\n",
" h[i] = 1. / n * 20\n",
" G[i+n, i] = -1.\n",
" h[i+n] = 0.\n",
" \n",
"G = matrix(G)\n",
"h = matrix(h)\n",
" \n",
"A = np.ones((1, n))\n",
"b = np.ones(1)\n",
"\n",
"A = matrix(A)\n",
"b = matrix(b)\n",
"\n",
"sol = solvers.qp(P, q, G, h, A, b)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 37 ms\n"
]
}
],
"source": [
"%%time\n",
"lbound = np.zeros(n)\n",
"ubound = np.ones(n) * 20 / n\n",
"cons_matrix = np.ones((1, n))\n",
"clb = np.ones(1)\n",
"cub = np.ones(1)\n",
"qpopt = QPOptimizer(signal, sec_cov_values, lbound, ubound, cons_matrix, clb, cub, 1.)\n",
"qpopt.feval()\n",
"qpopt.status()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Performace Timing\n",
"-------------------------"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import datetime as dt"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def time_function(py_callable, n):\n",
" start = dt.datetime.now()\n",
" py_callable(n)\n",
" return (dt.datetime.now() - start).total_seconds()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def cvxpy(n):\n",
" w = Variable(n)\n",
"\n",
" lbound = 0.\n",
" ubound = 0.01\n",
"\n",
" objective = Minimize(risk_penlty * quad_form(w, sec_cov_values) - signal * w)\n",
" constraints = [w >= lbound,\n",
" w <= ubound,\n",
" sum_entries(w) == 1,]\n",
"\n",
" prob = Problem(objective, constraints)\n",
" prob.solve(verbose=False, solver='CVXOPT', display=False)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def cvxopt(n):\n",
" P = matrix(sec_cov_values)\n",
" q = -matrix(signal)\n",
"\n",
" G = np.zeros((2*n, n))\n",
" h = np.zeros(2*n)\n",
" for i in range(n):\n",
" G[i, i] = 1.\n",
" h[i] = 0.01\n",
" G[i+n, i] = -1.\n",
" h[i+n] = 0.\n",
"\n",
" G = matrix(G)\n",
" h = matrix(h)\n",
"\n",
" A = np.ones((1, n))\n",
" b = np.ones(1)\n",
"\n",
" A = matrix(A)\n",
" b = matrix(b)\n",
" \n",
" solvers.options['show_progress'] = False\n",
" sol = solvers.qp(P, q, G, h, A, b)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def ipopt(n):\n",
" lbound = np.zeros(n)\n",
" ubound = np.ones(n) * 0.01\n",
" cons_matrix = np.ones((1, n))\n",
" clb = np.ones(1)\n",
" cub = np.ones(1)\n",
" qpopt = QPOptimizer(signal, sec_cov_values, lbound, ubound, cons_matrix, clb, cub, 1.)\n",
" qpopt.feval()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Scale(n) cvxpy cvxopt ipopt\n",
"200 197.63 45.03 25.02\n",
"400 1029.54 392.71 66.05\n",
"600 2765.32 1290.11 101.07\n",
"800 5906.85 3147.61 123.10\n",
"1000 11623.60 5949.30 181.69\n",
"1200 19327.03 9837.68 279.76\n",
"1400 28894.03 14138.84 529.42\n",
"1600 40706.67 21410.84 550.92\n",
"1800 59016.47 29610.92 737.62\n",
"2000 77980.53 41321.95 818.84\n",
"2200 98591.72 52743.93 1310.13\n",
"2400 139175.23 76199.42 2156.20\n",
"2600 170922.06 89493.43 1591.01\n",
"2800 197469.52 112762.10 1569.06\n",
"3000 246957.32 135002.46 1484.89\n",
"3200 291831.05 160416.66 1919.71\n"
]
}
],
"source": [
"n_steps = list(range(200, 3201, 200))\n",
"cvxpy_times = [None] * len(n_steps)\n",
"cvxopt_times = [None] * len(n_steps)\n",
"ipopt_times = [None] * len(n_steps)\n",
"print(\"{0:<8}{1:>12}{2:>12}{3:>12}\".format('Scale(n)', 'cvxpy', 'cvxopt', 'ipopt'))\n",
"\n",
"for i, n in enumerate(n_steps):\n",
" sec_cov_values = sec_cov_values_full[:n, :n]\n",
" signal = signal_full[:n]\n",
" cvxpy_times[i] = time_function(cvxpy, n) * 1000\n",
" cvxopt_times[i] = time_function(cvxopt, n) * 1000\n",
" ipopt_times[i] = time_function(ipopt, n) * 1000\n",
" \n",
" print(\"{0:<8}{1:>12.2f}{2:>12.2f}{3:>12.2f}\".format(n, cvxpy_times[i], cvxopt_times[i], ipopt_times[i]))"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>cvxopt</th>\n",
" <th>cvxpy</th>\n",
" <th>ipopt</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Problem Scale (n)</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>200</th>\n",
" <td>49.039</td>\n",
" <td>207.656</td>\n",
" <td>27.019</td>\n",
" </tr>\n",
" <tr>\n",
" <th>400</th>\n",
" <td>358.899</td>\n",
" <td>976.404</td>\n",
" <td>51.080</td>\n",
" </tr>\n",
" <tr>\n",
" <th>600</th>\n",
" <td>1222.190</td>\n",
" <td>2811.124</td>\n",
" <td>108.764</td>\n",
" </tr>\n",
" <tr>\n",
" <th>800</th>\n",
" <td>3051.363</td>\n",
" <td>5794.853</td>\n",
" <td>121.119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1000</th>\n",
" <td>5858.366</td>\n",
" <td>11435.303</td>\n",
" <td>192.262</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1200</th>\n",
" <td>9806.112</td>\n",
" <td>18855.293</td>\n",
" <td>272.055</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1400</th>\n",
" <td>13928.818</td>\n",
" <td>28437.877</td>\n",
" <td>536.453</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1600</th>\n",
" <td>20971.899</td>\n",
" <td>39959.689</td>\n",
" <td>583.744</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1800</th>\n",
" <td>28056.363</td>\n",
" <td>63106.509</td>\n",
" <td>630.622</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000</th>\n",
" <td>39500.814</td>\n",
" <td>74439.178</td>\n",
" <td>797.706</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2200</th>\n",
" <td>49913.609</td>\n",
" <td>97029.306</td>\n",
" <td>1434.759</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2400</th>\n",
" <td>71256.090</td>\n",
" <td>140969.086</td>\n",
" <td>2002.379</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2600</th>\n",
" <td>88315.444</td>\n",
" <td>159561.312</td>\n",
" <td>1576.816</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2800</th>\n",
" <td>112728.898</td>\n",
" <td>200788.253</td>\n",
" <td>1523.572</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3000</th>\n",
" <td>135712.700</td>\n",
" <td>259485.171</td>\n",
" <td>1641.147</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3200</th>\n",
" <td>175046.983</td>\n",
" <td>314367.647</td>\n",
" <td>2021.378</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" cvxopt cvxpy ipopt\n",
"Problem Scale (n) \n",
"200 49.039 207.656 27.019\n",
"400 358.899 976.404 51.080\n",
"600 1222.190 2811.124 108.764\n",
"800 3051.363 5794.853 121.119\n",
"1000 5858.366 11435.303 192.262\n",
"1200 9806.112 18855.293 272.055\n",
"1400 13928.818 28437.877 536.453\n",
"1600 20971.899 39959.689 583.744\n",
"1800 28056.363 63106.509 630.622\n",
"2000 39500.814 74439.178 797.706\n",
"2200 49913.609 97029.306 1434.759\n",
"2400 71256.090 140969.086 2002.379\n",
"2600 88315.444 159561.312 1576.816\n",
"2800 112728.898 200788.253 1523.572\n",
"3000 135712.700 259485.171 1641.147\n",
"3200 175046.983 314367.647 2021.378"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.DataFrame({'cvxpy': cvxpy_times,\n",
" 'cvxopt': cvxopt_times,\n",
" 'ipopt': ipopt_times},\n",
" index=n_steps)\n",
"df.index.name = 'Problem Scale (n)'\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"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": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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