def test_bench_krylovschursparse_dense(benchmark,
n=N_BENCHMARK,
m=M_BENCHMARK):
if not pcca.HAS_SLEPC:
return
from scipy import sparse
T = utils.randompropagator(n)
T = sparse.csr_matrix(T)
solver = pcca.KrylovSchur(onseperation="continue")
benchmark(solver.solve, T, m)
def test_krylovschur(n=30, m=5, N=100):
if pcca.HAS_SLEPC:
for i in range(N):
A = utils.randompropagator(n, reversible=False)
try:
S = pcca.scipyschur(A, m, onseperation="error")
except RuntimeError:
continue
K = pcca.krylovschur(A, m)
R = np.linalg.matrix_rank(np.concatenate([S, K], axis=1), tol=1e-6)
assert R == m
def test_scipyschur(n=10, m=3):
T = utils.randompropagator(n)
massmatrix = np.diag(np.ones(n))
solver = pcca.ScipySchur()
X1 = solver.solve(T, m)
X2 = solver.solve(T, m, massmatrix)
# check if X1 and X2 span the same space
tol = 1e-12
assert np.linalg.matrix_rank(np.hstack([X1, X2]), tol) == m
def test_bench_krylovschur(benchmark, n=N_BENCHMARK, m=M_BENCHMARK):
if not pcca.HAS_SLEPC:
return
T = utils.randompropagator(n)
solver = pcca.KrylovSchur(onseperation="continue")
benchmark(solver.solve, T, m)
def test_bench_scipyqz(benchmark, n=N_BENCHMARK, m=M_BENCHMARK):
T = utils.randompropagator(n)
massmatrix = np.diag(np.ones(n))
solver = pcca.ScipySchur()
benchmark(solver.solve, T, m, massmatrix)
def test_bench_scipyschur(benchmark, n=N_BENCHMARK, m=M_BENCHMARK):
T = utils.randompropagator(n)
solver = pcca.ScipySchur()
benchmark(solver.solve, T, m)
def test_random():
T = utils.randompropagator(10)
m = pcca.pcca(T, 3)
assert utils.is_rowstochastic(m)