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eaiaiaiaoi/venv/lib/python2.7/site-packages/scipy/linalg/tests/test_solvers.py

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from __future__ import division, print_function, absolute_import
import os
import numpy as np
from numpy.testing import assert_array_almost_equal
import pytest
from pytest import raises as assert_raises
from scipy.linalg import solve_sylvester
from scipy.linalg import solve_continuous_lyapunov, solve_discrete_lyapunov
from scipy.linalg import solve_continuous_are, solve_discrete_are
from scipy.linalg import block_diag, solve, LinAlgError
def _load_data(name):
"""
Load npz data file under data/
Returns a copy of the data, rather than keeping the npz file open.
"""
filename = os.path.join(os.path.abspath(os.path.dirname(__file__)),
'data', name)
with np.load(filename) as f:
return dict(f.items())
class TestSolveLyapunov(object):
cases = [
(np.array([[1, 2], [3, 4]]),
np.array([[9, 10], [11, 12]])),
# a, q all complex.
(np.array([[1.0+1j, 2.0], [3.0-4.0j, 5.0]]),
np.array([[2.0-2j, 2.0+2j], [-1.0-1j, 2.0]])),
# a real; q complex.
(np.array([[1.0, 2.0], [3.0, 5.0]]),
np.array([[2.0-2j, 2.0+2j], [-1.0-1j, 2.0]])),
# a complex; q real.
(np.array([[1.0+1j, 2.0], [3.0-4.0j, 5.0]]),
np.array([[2.0, 2.0], [-1.0, 2.0]])),
# An example from Kitagawa, 1977
(np.array([[3, 9, 5, 1, 4], [1, 2, 3, 8, 4], [4, 6, 6, 6, 3],
[1, 5, 2, 0, 7], [5, 3, 3, 1, 5]]),
np.array([[2, 4, 1, 0, 1], [4, 1, 0, 2, 0], [1, 0, 3, 0, 3],
[0, 2, 0, 1, 0], [1, 0, 3, 0, 4]])),
# Companion matrix example. a complex; q real; a.shape[0] = 11
(np.array([[0.100+0.j, 0.091+0.j, 0.082+0.j, 0.073+0.j, 0.064+0.j,
0.055+0.j, 0.046+0.j, 0.037+0.j, 0.028+0.j, 0.019+0.j,
0.010+0.j],
[1.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j,
0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j,
0.000+0.j],
[0.000+0.j, 1.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j,
0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j,
0.000+0.j],
[0.000+0.j, 0.000+0.j, 1.000+0.j, 0.000+0.j, 0.000+0.j,
0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j,
0.000+0.j],
[0.000+0.j, 0.000+0.j, 0.000+0.j, 1.000+0.j, 0.000+0.j,
0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j,
0.000+0.j],
[0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j, 1.000+0.j,
0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j,
0.000+0.j],
[0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j,
1.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j,
0.000+0.j],
[0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j,
0.000+0.j, 1.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j,
0.000+0.j],
[0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j,
0.000+0.j, 0.000+0.j, 1.000+0.j, 0.000+0.j, 0.000+0.j,
0.000+0.j],
[0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j,
0.000+0.j, 0.000+0.j, 0.000+0.j, 1.000+0.j, 0.000+0.j,
0.000+0.j],
[0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j,
0.000+0.j, 0.000+0.j, 0.000+0.j, 0.000+0.j, 1.000+0.j,
0.000+0.j]]),
np.eye(11)),
# https://github.com/scipy/scipy/issues/4176
(np.matrix([[0, 1], [-1/2, -1]]),
(np.matrix([0, 3]).T * np.matrix([0, 3]).T.T)),
# https://github.com/scipy/scipy/issues/4176
(np.matrix([[0, 1], [-1/2, -1]]),
(np.array(np.matrix([0, 3]).T * np.matrix([0, 3]).T.T))),
]
def test_continuous_squareness_and_shape(self):
nsq = np.ones((3, 2))
sq = np.eye(3)
assert_raises(ValueError, solve_continuous_lyapunov, nsq, sq)
assert_raises(ValueError, solve_continuous_lyapunov, sq, nsq)
assert_raises(ValueError, solve_continuous_lyapunov, sq, np.eye(2))
def check_continuous_case(self, a, q):
x = solve_continuous_lyapunov(a, q)
assert_array_almost_equal(
np.dot(a, x) + np.dot(x, a.conj().transpose()), q)
def check_discrete_case(self, a, q, method=None):
x = solve_discrete_lyapunov(a, q, method=method)
assert_array_almost_equal(
np.dot(np.dot(a, x), a.conj().transpose()) - x, -1.0*q)
def test_cases(self):
for case in self.cases:
self.check_continuous_case(case[0], case[1])
self.check_discrete_case(case[0], case[1])
self.check_discrete_case(case[0], case[1], method='direct')
self.check_discrete_case(case[0], case[1], method='bilinear')
def test_solve_continuous_are():
mat6 = _load_data('carex_6_data.npz')
mat15 = _load_data('carex_15_data.npz')
mat18 = _load_data('carex_18_data.npz')
mat19 = _load_data('carex_19_data.npz')
mat20 = _load_data('carex_20_data.npz')
cases = [
# Carex examples taken from (with default parameters):
# [1] P.BENNER, A.J. LAUB, V. MEHRMANN: 'A Collection of Benchmark
# Examples for the Numerical Solution of Algebraic Riccati
# Equations II: Continuous-Time Case', Tech. Report SPC 95_23,
# Fak. f. Mathematik, TU Chemnitz-Zwickau (Germany), 1995.
#
# The format of the data is (a, b, q, r, knownfailure), where
# knownfailure is None if the test passes or a string
# indicating the reason for failure.
#
# Test Case 0: carex #1
(np.diag([1.], 1),
np.array([[0], [1]]),
block_diag(1., 2.),
1,
None),
# Test Case 1: carex #2
(np.array([[4, 3], [-4.5, -3.5]]),
np.array([[1], [-1]]),
np.array([[9, 6], [6, 4.]]),
1,
None),
# Test Case 2: carex #3
(np.array([[0, 1, 0, 0],
[0, -1.89, 0.39, -5.53],
[0, -0.034, -2.98, 2.43],
[0.034, -0.0011, -0.99, -0.21]]),
np.array([[0, 0], [0.36, -1.6], [-0.95, -0.032], [0.03, 0]]),
np.array([[2.313, 2.727, 0.688, 0.023],
[2.727, 4.271, 1.148, 0.323],
[0.688, 1.148, 0.313, 0.102],
[0.023, 0.323, 0.102, 0.083]]),
np.eye(2),
None),
# Test Case 3: carex #4
(np.array([[-0.991, 0.529, 0, 0, 0, 0, 0, 0],
[0.522, -1.051, 0.596, 0, 0, 0, 0, 0],
[0, 0.522, -1.118, 0.596, 0, 0, 0, 0],
[0, 0, 0.522, -1.548, 0.718, 0, 0, 0],
[0, 0, 0, 0.922, -1.64, 0.799, 0, 0],
[0, 0, 0, 0, 0.922, -1.721, 0.901, 0],
[0, 0, 0, 0, 0, 0.922, -1.823, 1.021],
[0, 0, 0, 0, 0, 0, 0.922, -1.943]]),
np.array([[3.84, 4.00, 37.60, 3.08, 2.36, 2.88, 3.08, 3.00],
[-2.88, -3.04, -2.80, -2.32, -3.32, -3.82, -4.12, -3.96]]
).T * 0.001,
np.array([[1.0, 0.0, 0.0, 0.0, 0.5, 0.0, 0.0, 0.1],
[0.0, 1.0, 0.0, 0.0, 0.1, 0.0, 0.0, 0.0],
[0.0, 0.0, 1.0, 0.0, 0.0, 0.5, 0.0, 0.0],
[0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0],
[0.5, 0.1, 0.0, 0.0, 0.1, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.5, 0.0, 0.0, 0.1, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1, 0.0],
[0.1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1]]),
np.eye(2),
None),
# Test Case 4: carex #5
(np.array(
[[-4.019, 5.120, 0., 0., -2.082, 0., 0., 0., 0.870],
[-0.346, 0.986, 0., 0., -2.340, 0., 0., 0., 0.970],
[-7.909, 15.407, -4.069, 0., -6.450, 0., 0., 0., 2.680],
[-21.816, 35.606, -0.339, -3.870, -17.800, 0., 0., 0., 7.390],
[-60.196, 98.188, -7.907, 0.340, -53.008, 0., 0., 0., 20.400],
[0, 0, 0, 0, 94.000, -147.200, 0., 53.200, 0.],
[0, 0, 0, 0, 0, 94.000, -147.200, 0, 0],
[0, 0, 0, 0, 0, 12.800, 0.000, -31.600, 0],
[0, 0, 0, 0, 12.800, 0.000, 0.000, 18.800, -31.600]]),
np.array([[0.010, -0.011, -0.151],
[0.003, -0.021, 0.000],
[0.009, -0.059, 0.000],
[0.024, -0.162, 0.000],
[0.068, -0.445, 0.000],
[0.000, 0.000, 0.000],
[0.000, 0.000, 0.000],
[0.000, 0.000, 0.000],
[0.000, 0.000, 0.000]]),
np.eye(9),
np.eye(3),
None),
# Test Case 5: carex #6
(mat6['A'], mat6['B'], mat6['Q'], mat6['R'], None),
# Test Case 6: carex #7
(np.array([[1, 0], [0, -2.]]),
np.array([[1e-6], [0]]),
np.ones((2, 2)),
1.,
'Bad residual accuracy'),
# Test Case 7: carex #8
(block_diag(-0.1, -0.02),
np.array([[0.100, 0.000], [0.001, 0.010]]),
np.array([[100, 1000], [1000, 10000]]),
np.ones((2, 2)) + block_diag(1e-6, 0),
None),
# Test Case 8: carex #9
(np.array([[0, 1e6], [0, 0]]),
np.array([[0], [1.]]),
np.eye(2),
1.,
None),
# Test Case 9: carex #10
(np.array([[1.0000001, 1], [1., 1.0000001]]),
np.eye(2),
np.eye(2),
np.eye(2),
None),
# Test Case 10: carex #11
(np.array([[3, 1.], [4, 2]]),
np.array([[1], [1]]),
np.array([[-11, -5], [-5, -2.]]),
1.,
None),
# Test Case 11: carex #12
(np.array([[7000000., 2000000., -0.],
[2000000., 6000000., -2000000.],
[0., -2000000., 5000000.]]) / 3,
np.eye(3),
np.array([[1., -2., -2.], [-2., 1., -2.], [-2., -2., 1.]]).dot(
np.diag([1e-6, 1, 1e6])).dot(
np.array([[1., -2., -2.], [-2., 1., -2.], [-2., -2., 1.]])) / 9,
np.eye(3) * 1e6,
'Bad Residual Accuracy'),
# Test Case 12: carex #13
(np.array([[0, 0.4, 0, 0],
[0, 0, 0.345, 0],
[0, -0.524e6, -0.465e6, 0.262e6],
[0, 0, 0, -1e6]]),
np.array([[0, 0, 0, 1e6]]).T,
np.diag([1, 0, 1, 0]),
1.,
None),
# Test Case 13: carex #14
(np.array([[-1e-6, 1, 0, 0],
[-1, -1e-6, 0, 0],
[0, 0, 1e-6, 1],
[0, 0, -1, 1e-6]]),
np.ones((4, 1)),
np.ones((4, 4)),
1.,
None),
# Test Case 14: carex #15
(mat15['A'], mat15['B'], mat15['Q'], mat15['R'], None),
# Test Case 15: carex #16
(np.eye(64, 64, k=-1) + np.eye(64, 64)*(-2.) + np.rot90(
block_diag(1, np.zeros((62, 62)), 1)) + np.eye(64, 64, k=1),
np.eye(64),
np.eye(64),
np.eye(64),
None),
# Test Case 16: carex #17
(np.diag(np.ones((20, )), 1),
np.flipud(np.eye(21, 1)),
np.eye(21, 1) * np.eye(21, 1).T,
1,
'Bad Residual Accuracy'),
# Test Case 17: carex #18
(mat18['A'], mat18['B'], mat18['Q'], mat18['R'], None),
# Test Case 18: carex #19
(mat19['A'], mat19['B'], mat19['Q'], mat19['R'],
'Bad Residual Accuracy'),
# Test Case 19: carex #20
(mat20['A'], mat20['B'], mat20['Q'], mat20['R'],
'Bad Residual Accuracy')
]
# Makes the minimum precision requirements customized to the test.
# Here numbers represent the number of decimals that agrees with zero
# matrix when the solution x is plugged in to the equation.
#
# res = array([[8e-3,1e-16],[1e-16,1e-20]]) --> min_decimal[k] = 2
#
# If the test is failing use "None" for that entry.
#
min_decimal = (14, 12, 13, 14, 11, 6, None, 5, 7, 14, 14,
None, 9, 14, 13, 14, None, 12, None, None)
def _test_factory(case, dec):
"""Checks if 0 = XA + A'X - XB(R)^{-1} B'X + Q is true"""
a, b, q, r, knownfailure = case
if knownfailure:
pytest.xfail(reason=knownfailure)
x = solve_continuous_are(a, b, q, r)
res = x.dot(a) + a.conj().T.dot(x) + q
out_fact = x.dot(b)
res -= out_fact.dot(solve(np.atleast_2d(r), out_fact.conj().T))
assert_array_almost_equal(res, np.zeros_like(res), decimal=dec)
for ind, case in enumerate(cases):
_test_factory(case, min_decimal[ind])
def test_solve_discrete_are():
cases = [
# Darex examples taken from (with default parameters):
# [1] P.BENNER, A.J. LAUB, V. MEHRMANN: 'A Collection of Benchmark
# Examples for the Numerical Solution of Algebraic Riccati
# Equations II: Discrete-Time Case', Tech. Report SPC 95_23,
# Fak. f. Mathematik, TU Chemnitz-Zwickau (Germany), 1995.
# [2] T. GUDMUNDSSON, C. KENNEY, A.J. LAUB: 'Scaling of the
# Discrete-Time Algebraic Riccati Equation to Enhance Stability
# of the Schur Solution Method', IEEE Trans.Aut.Cont., vol.37(4)
#
# The format of the data is (a, b, q, r, knownfailure), where
# knownfailure is None if the test passes or a string
# indicating the reason for failure.
#
# TEST CASE 0 : Complex a; real b, q, r
(np.array([[2, 1-2j], [0, -3j]]),
np.array([[0], [1]]),
np.array([[1, 0], [0, 2]]),
np.array([[1]]),
None),
# TEST CASE 1 :Real a, q, r; complex b
(np.array([[2, 1], [0, -1]]),
np.array([[-2j], [1j]]),
np.array([[1, 0], [0, 2]]),
np.array([[1]]),
None),
# TEST CASE 2 : Real a, b; complex q, r
(np.array([[3, 1], [0, -1]]),
np.array([[1, 2], [1, 3]]),
np.array([[1, 1+1j], [1-1j, 2]]),
np.array([[2, -2j], [2j, 3]]),
None),
# TEST CASE 3 : User-reported gh-2251 (Trac #1732)
(np.array([[0.63399379, 0.54906824, 0.76253406],
[0.5404729, 0.53745766, 0.08731853],
[0.27524045, 0.84922129, 0.4681622]]),
np.array([[0.96861695], [0.05532739], [0.78934047]]),
np.eye(3),
np.eye(1),
None),
# TEST CASE 4 : darex #1
(np.array([[4, 3], [-4.5, -3.5]]),
np.array([[1], [-1]]),
np.array([[9, 6], [6, 4]]),
np.array([[1]]),
None),
# TEST CASE 5 : darex #2
(np.array([[0.9512, 0], [0, 0.9048]]),
np.array([[4.877, 4.877], [-1.1895, 3.569]]),
np.array([[0.005, 0], [0, 0.02]]),
np.array([[1/3, 0], [0, 3]]),
None),
# TEST CASE 6 : darex #3
(np.array([[2, -1], [1, 0]]),
np.array([[1], [0]]),
np.array([[0, 0], [0, 1]]),
np.array([[0]]),
None),
# TEST CASE 7 : darex #4 (skipped the gen. Ric. term S)
(np.array([[0, 1], [0, -1]]),
np.array([[1, 0], [2, 1]]),
np.array([[-4, -4], [-4, 7]]) * (1/11),
np.array([[9, 3], [3, 1]]),
None),
# TEST CASE 8 : darex #5
(np.array([[0, 1], [0, 0]]),
np.array([[0], [1]]),
np.array([[1, 2], [2, 4]]),
np.array([[1]]),
None),
# TEST CASE 9 : darex #6
(np.array([[0.998, 0.067, 0, 0],
[-.067, 0.998, 0, 0],
[0, 0, 0.998, 0.153],
[0, 0, -.153, 0.998]]),
np.array([[0.0033, 0.0200],
[0.1000, -.0007],
[0.0400, 0.0073],
[-.0028, 0.1000]]),
np.array([[1.87, 0, 0, -0.244],
[0, 0.744, 0.205, 0],
[0, 0.205, 0.589, 0],
[-0.244, 0, 0, 1.048]]),
np.eye(2),
None),
# TEST CASE 10 : darex #7
(np.array([[0.984750, -.079903, 0.0009054, -.0010765],
[0.041588, 0.998990, -.0358550, 0.0126840],
[-.546620, 0.044916, -.3299100, 0.1931800],
[2.662400, -.100450, -.9245500, -.2632500]]),
np.array([[0.0037112, 0.0007361],
[-.0870510, 9.3411e-6],
[-1.198440, -4.1378e-4],
[-3.192700, 9.2535e-4]]),
np.eye(4)*1e-2,
np.eye(2),
None),
# TEST CASE 11 : darex #8
(np.array([[-0.6000000, -2.2000000, -3.6000000, -5.4000180],
[1.0000000, 0.6000000, 0.8000000, 3.3999820],
[0.0000000, 1.0000000, 1.8000000, 3.7999820],
[0.0000000, 0.0000000, 0.0000000, -0.9999820]]),
np.array([[1.0, -1.0, -1.0, -1.0],
[0.0, 1.0, -1.0, -1.0],
[0.0, 0.0, 1.0, -1.0],
[0.0, 0.0, 0.0, 1.0]]),
np.array([[2, 1, 3, 6],
[1, 2, 2, 5],
[3, 2, 6, 11],
[6, 5, 11, 22]]),
np.eye(4),
None),
# TEST CASE 12 : darex #9
(np.array([[95.4070, 1.9643, 0.3597, 0.0673, 0.0190],
[40.8490, 41.3170, 16.0840, 4.4679, 1.1971],
[12.2170, 26.3260, 36.1490, 15.9300, 12.3830],
[4.1118, 12.8580, 27.2090, 21.4420, 40.9760],
[0.1305, 0.5808, 1.8750, 3.6162, 94.2800]]) * 0.01,
np.array([[0.0434, -0.0122],
[2.6606, -1.0453],
[3.7530, -5.5100],
[3.6076, -6.6000],
[0.4617, -0.9148]]) * 0.01,
np.eye(5),
np.eye(2),
None),
# TEST CASE 13 : darex #10
(np.kron(np.eye(2), np.diag([1, 1], k=1)),
np.kron(np.eye(2), np.array([[0], [0], [1]])),
np.array([[1, 1, 0, 0, 0, 0],
[1, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, -1, 0],
[0, 0, 0, -1, 1, 0],
[0, 0, 0, 0, 0, 0]]),
np.array([[3, 0], [0, 1]]),
None),
# TEST CASE 14 : darex #11
(0.001 * np.array(
[[870.1, 135.0, 11.59, .5014, -37.22, .3484, 0, 4.242, 7.249],
[76.55, 897.4, 12.72, 0.5504, -40.16, .3743, 0, 4.53, 7.499],
[-127.2, 357.5, 817, 1.455, -102.8, .987, 0, 11.85, 18.72],
[-363.5, 633.9, 74.91, 796.6, -273.5, 2.653, 0, 31.72, 48.82],
[-960, 1645.9, -128.9, -5.597, 71.42, 7.108, 0, 84.52, 125.9],
[-664.4, 112.96, -88.89, -3.854, 84.47, 13.6, 0, 144.3, 101.6],
[-410.2, 693, -54.71, -2.371, 66.49, 12.49, .1063, 99.97, 69.67],
[-179.9, 301.7, -23.93, -1.035, 60.59, 22.16, 0, 213.9, 35.54],
[-345.1, 580.4, -45.96, -1.989, 105.6, 19.86, 0, 219.1, 215.2]]),
np.array([[4.7600, -0.5701, -83.6800],
[0.8790, -4.7730, -2.7300],
[1.4820, -13.1200, 8.8760],
[3.8920, -35.1300, 24.8000],
[10.3400, -92.7500, 66.8000],
[7.2030, -61.5900, 38.3400],
[4.4540, -36.8300, 20.2900],
[1.9710, -15.5400, 6.9370],
[3.7730, -30.2800, 14.6900]]) * 0.001,
np.diag([50, 0, 0, 0, 50, 0, 0, 0, 0]),
np.eye(3),
None),
# TEST CASE 15 : darex #12 - numerically least accurate example
(np.array([[0, 1e6], [0, 0]]),
np.array([[0], [1]]),
np.eye(2),
np.array([[1]]),
None),
# TEST CASE 16 : darex #13
(np.array([[16, 10, -2],
[10, 13, -8],
[-2, -8, 7]]) * (1/9),
np.eye(3),
1e6 * np.eye(3),
1e6 * np.eye(3),
None),
# TEST CASE 17 : darex #14
(np.array([[1 - 1/1e8, 0, 0, 0],
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0]]),
np.array([[1e-08], [0], [0], [0]]),
np.diag([0, 0, 0, 1]),
np.array([[0.25]]),
None),
# TEST CASE 18 : darex #15
(np.eye(100, k=1),
np.flipud(np.eye(100, 1)),
np.eye(100),
np.array([[1]]),
None)
]
# Makes the minimum precision requirements customized to the test.
# Here numbers represent the number of decimals that agrees with zero
# matrix when the solution x is plugged in to the equation.
#
# res = array([[8e-3,1e-16],[1e-16,1e-20]]) --> min_decimal[k] = 2
#
# If the test is failing use "None" for that entry.
#
min_decimal = (12, 14, 13, 14, 13, 16, 18, 14, 15, 13,
14, 13, 13, 14, 12, 2, 5, 6, 10)
def _test_factory(case, dec):
"""Checks if X = A'XA-(A'XB)(R+B'XB)^-1(B'XA)+Q) is true"""
a, b, q, r, knownfailure = case
if knownfailure:
pytest.xfail(reason=knownfailure)
x = solve_discrete_are(a, b, q, r)
res = a.conj().T.dot(x.dot(a)) - x + q
res -= a.conj().T.dot(x.dot(b)).dot(
solve(r+b.conj().T.dot(x.dot(b)), b.conj().T).dot(x.dot(a))
)
assert_array_almost_equal(res, np.zeros_like(res), decimal=dec)
for ind, case in enumerate(cases):
_test_factory(case, min_decimal[ind])
# An infeasible example taken from https://arxiv.org/abs/1505.04861v1
A = np.triu(np.ones((3, 3)))
A[0, 1] = -1
B = np.array([[1, 1, 0], [0, 0, 1]]).T
Q = -2*np.ones_like(A) + np.diag([8, -1, -1.9])
R = np.diag([-10, 0.1])
assert_raises(LinAlgError, solve_continuous_are, A, B, Q, R)
def test_solve_generalized_continuous_are():
cases = [
# Two random examples differ by s term
# in the absence of any literature for demanding examples.
(np.array([[2.769230e-01, 8.234578e-01, 9.502220e-01],
[4.617139e-02, 6.948286e-01, 3.444608e-02],
[9.713178e-02, 3.170995e-01, 4.387444e-01]]),
np.array([[3.815585e-01, 1.868726e-01],
[7.655168e-01, 4.897644e-01],
[7.951999e-01, 4.455862e-01]]),
np.eye(3),
np.eye(2),
np.array([[6.463130e-01, 2.760251e-01, 1.626117e-01],
[7.093648e-01, 6.797027e-01, 1.189977e-01],
[7.546867e-01, 6.550980e-01, 4.983641e-01]]),
np.zeros((3, 2)),
None),
(np.array([[2.769230e-01, 8.234578e-01, 9.502220e-01],
[4.617139e-02, 6.948286e-01, 3.444608e-02],
[9.713178e-02, 3.170995e-01, 4.387444e-01]]),
np.array([[3.815585e-01, 1.868726e-01],
[7.655168e-01, 4.897644e-01],
[7.951999e-01, 4.455862e-01]]),
np.eye(3),
np.eye(2),
np.array([[6.463130e-01, 2.760251e-01, 1.626117e-01],
[7.093648e-01, 6.797027e-01, 1.189977e-01],
[7.546867e-01, 6.550980e-01, 4.983641e-01]]),
np.ones((3, 2)),
None)
]
min_decimal = (10, 10)
def _test_factory(case, dec):
"""Checks if X = A'XA-(A'XB)(R+B'XB)^-1(B'XA)+Q) is true"""
a, b, q, r, e, s, knownfailure = case
if knownfailure:
pytest.xfail(reason=knownfailure)
x = solve_continuous_are(a, b, q, r, e, s)
res = a.conj().T.dot(x.dot(e)) + e.conj().T.dot(x.dot(a)) + q
out_fact = e.conj().T.dot(x).dot(b) + s
res -= out_fact.dot(solve(np.atleast_2d(r), out_fact.conj().T))
assert_array_almost_equal(res, np.zeros_like(res), decimal=dec)
for ind, case in enumerate(cases):
_test_factory(case, min_decimal[ind])
def test_solve_generalized_discrete_are():
mat20170120 = _load_data('gendare_20170120_data.npz')
cases = [
# Two random examples differ by s term
# in the absence of any literature for demanding examples.
(np.array([[2.769230e-01, 8.234578e-01, 9.502220e-01],
[4.617139e-02, 6.948286e-01, 3.444608e-02],
[9.713178e-02, 3.170995e-01, 4.387444e-01]]),
np.array([[3.815585e-01, 1.868726e-01],
[7.655168e-01, 4.897644e-01],
[7.951999e-01, 4.455862e-01]]),
np.eye(3),
np.eye(2),
np.array([[6.463130e-01, 2.760251e-01, 1.626117e-01],
[7.093648e-01, 6.797027e-01, 1.189977e-01],
[7.546867e-01, 6.550980e-01, 4.983641e-01]]),
np.zeros((3, 2)),
None),
(np.array([[2.769230e-01, 8.234578e-01, 9.502220e-01],
[4.617139e-02, 6.948286e-01, 3.444608e-02],
[9.713178e-02, 3.170995e-01, 4.387444e-01]]),
np.array([[3.815585e-01, 1.868726e-01],
[7.655168e-01, 4.897644e-01],
[7.951999e-01, 4.455862e-01]]),
np.eye(3),
np.eye(2),
np.array([[6.463130e-01, 2.760251e-01, 1.626117e-01],
[7.093648e-01, 6.797027e-01, 1.189977e-01],
[7.546867e-01, 6.550980e-01, 4.983641e-01]]),
np.ones((3, 2)),
None),
# user-reported (under PR-6616) 20-Jan-2017
# tests against the case where E is None but S is provided
(mat20170120['A'],
mat20170120['B'],
mat20170120['Q'],
mat20170120['R'],
None,
mat20170120['S'],
None),
]
min_decimal = (11, 11, 16)
def _test_factory(case, dec):
"""Checks if X = A'XA-(A'XB)(R+B'XB)^-1(B'XA)+Q) is true"""
a, b, q, r, e, s, knownfailure = case
if knownfailure:
pytest.xfail(reason=knownfailure)
x = solve_discrete_are(a, b, q, r, e, s)
if e is None:
e = np.eye(a.shape[0])
if s is None:
s = np.zeros_like(b)
res = a.conj().T.dot(x.dot(a)) - e.conj().T.dot(x.dot(e)) + q
res -= (a.conj().T.dot(x.dot(b)) + s).dot(
solve(r+b.conj().T.dot(x.dot(b)),
(b.conj().T.dot(x.dot(a)) + s.conj().T)
)
)
assert_array_almost_equal(res, np.zeros_like(res), decimal=dec)
for ind, case in enumerate(cases):
_test_factory(case, min_decimal[ind])
def test_are_validate_args():
def test_square_shape():
nsq = np.ones((3, 2))
sq = np.eye(3)
for x in (solve_continuous_are, solve_discrete_are):
assert_raises(ValueError, x, nsq, 1, 1, 1)
assert_raises(ValueError, x, sq, sq, nsq, 1)
assert_raises(ValueError, x, sq, sq, sq, nsq)
assert_raises(ValueError, x, sq, sq, sq, sq, nsq)
def test_compatible_sizes():
nsq = np.ones((3, 2))
sq = np.eye(4)
for x in (solve_continuous_are, solve_discrete_are):
assert_raises(ValueError, x, sq, nsq, 1, 1)
assert_raises(ValueError, x, sq, sq, sq, sq, sq, nsq)
assert_raises(ValueError, x, sq, sq, np.eye(3), sq)
assert_raises(ValueError, x, sq, sq, sq, np.eye(3))
assert_raises(ValueError, x, sq, sq, sq, sq, np.eye(3))
def test_symmetry():
nsym = np.arange(9).reshape(3, 3)
sym = np.eye(3)
for x in (solve_continuous_are, solve_discrete_are):
assert_raises(ValueError, x, sym, sym, nsym, sym)
assert_raises(ValueError, x, sym, sym, sym, nsym)
def test_singularity():
sing = 1e12 * np.ones((3, 3))
sing[2, 2] -= 1
sq = np.eye(3)
for x in (solve_continuous_are, solve_discrete_are):
assert_raises(ValueError, x, sq, sq, sq, sq, sing)
assert_raises(ValueError, solve_continuous_are, sq, sq, sq, sing)
def test_finiteness():
nm = np.ones((2, 2)) * np.nan
sq = np.eye(2)
for x in (solve_continuous_are, solve_discrete_are):
assert_raises(ValueError, x, nm, sq, sq, sq)
assert_raises(ValueError, x, sq, nm, sq, sq)
assert_raises(ValueError, x, sq, sq, nm, sq)
assert_raises(ValueError, x, sq, sq, sq, nm)
assert_raises(ValueError, x, sq, sq, sq, sq, nm)
assert_raises(ValueError, x, sq, sq, sq, sq, sq, nm)
class TestSolveSylvester(object):
cases = [
# a, b, c all real.
(np.array([[1, 2], [0, 4]]),
np.array([[5, 6], [0, 8]]),
np.array([[9, 10], [11, 12]])),
# a, b, c all real, 4x4. a and b have non-trival 2x2 blocks in their
# quasi-triangular form.
(np.array([[1.0, 0, 0, 0],
[0, 1.0, 2.0, 0.0],
[0, 0, 3.0, -4],
[0, 0, 2, 5]]),
np.array([[2.0, 0, 0, 1.0],
[0, 1.0, 0.0, 0.0],
[0, 0, 1.0, -1],
[0, 0, 1, 1]]),
np.array([[1.0, 0, 0, 0],
[0, 1.0, 0, 0],
[0, 0, 1.0, 0],
[0, 0, 0, 1.0]])),
# a, b, c all complex.
(np.array([[1.0+1j, 2.0], [3.0-4.0j, 5.0]]),
np.array([[-1.0, 2j], [3.0, 4.0]]),
np.array([[2.0-2j, 2.0+2j], [-1.0-1j, 2.0]])),
# a and b real; c complex.
(np.array([[1.0, 2.0], [3.0, 5.0]]),
np.array([[-1.0, 0], [3.0, 4.0]]),
np.array([[2.0-2j, 2.0+2j], [-1.0-1j, 2.0]])),
# a and c complex; b real.
(np.array([[1.0+1j, 2.0], [3.0-4.0j, 5.0]]),
np.array([[-1.0, 0], [3.0, 4.0]]),
np.array([[2.0-2j, 2.0+2j], [-1.0-1j, 2.0]])),
# a complex; b and c real.
(np.array([[1.0+1j, 2.0], [3.0-4.0j, 5.0]]),
np.array([[-1.0, 0], [3.0, 4.0]]),
np.array([[2.0, 2.0], [-1.0, 2.0]])),
# not square matrices, real
(np.array([[8, 1, 6], [3, 5, 7], [4, 9, 2]]),
np.array([[2, 3], [4, 5]]),
np.array([[1, 2], [3, 4], [5, 6]])),
# not square matrices, complex
(np.array([[8, 1j, 6+2j], [3, 5, 7], [4, 9, 2]]),
np.array([[2, 3], [4, 5-1j]]),
np.array([[1, 2j], [3, 4j], [5j, 6+7j]])),
]
def check_case(self, a, b, c):
x = solve_sylvester(a, b, c)
assert_array_almost_equal(np.dot(a, x) + np.dot(x, b), c)
def test_cases(self):
for case in self.cases:
self.check_case(case[0], case[1], case[2])
def test_trivial(self):
a = np.array([[1.0, 0.0], [0.0, 1.0]])
b = np.array([[1.0]])
c = np.array([2.0, 2.0]).reshape(-1, 1)
x = solve_sylvester(a, b, c)
assert_array_almost_equal(x, np.array([1.0, 1.0]).reshape(-1, 1))
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