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Tue, May 7, 22:59

matrix.py
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# Copyright (C) 2018-2020 by the RROMPy authors
#
# This file is part of RROMPy.
#
# RROMPy is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# RROMPy is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with RROMPy. If not, see <http://www.gnu.org/licenses/>.
#
import numpy as np
import scipy.sparse as sp
from rrompy.hfengines.scipy_engines import EigenproblemEngine
def test_deterministic():
N = 100
A = sp.spdiags([np.arange(1, 1 + N)], [0], N, N)
B = - sp.eye(N)
f = np.exp(1.j * np.linspace(0, -np.pi, N))
solver = EigenproblemEngine([A, B], f, verbosity = 0)
mu = 10. + .5j
uh = solver.solve(mu)[0]
assert np.isclose(solver.norm(solver.residual(mu, uh)[0], dual = True),
1.088e-15, rtol = 1e-1)
def test_random():
N = 100
np.random.seed(420)
fftB = np.fft.fft(np.eye(N)) * N ** -.5
A = fftB.dot(np.multiply(np.arange(1, 1 + N), fftB.conj()).T)
B = - np.eye(N)
f = np.random.randn(N) + 1.j * np.random.randn(N)
solver = EigenproblemEngine([A, B], f, verbosity = 0)
mu = 1. + .5j
uh = solver.solve(mu)[0]
assert np.isclose(solver.norm(solver.residual(mu, uh)[0], dual = True),
7.18658e-14, rtol = 1e-1)

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