def test_subsolvers_L2(rng, logger):
pytest.importorskip('scipy', minversion='0.11') # version for lsmr
ref_solver = lstsq.Cholesky()
solvers = [
lstsq.Conjgrad(),
lstsq.BlockConjgrad(),
lstsq.ConjgradScipy(),
lstsq.LSMRScipy()
]
A, B = get_system(m=2000, n=1000, d=10, rng=rng)
sigma = 0.1 * A.max()
with Timer() as t0:
x0, _ = ref_solver(A, B, sigma)
xs = np.zeros((len(solvers), ) + x0.shape)
for i, solver in enumerate(solvers):
with Timer() as t:
xs[i], info = solver(A, B, sigma)
logger.info('solver: %r' % solver)
logger.info('duration: %0.3f', t.duration)
logger.info('duration relative to reference solver: %0.2f',
(t.duration / t0.duration))
logger.info('info: %s', info)
for solver, x in zip(solvers, xs):
assert np.allclose(x0, x, atol=1e-5,
rtol=1e-3), ("Solver %s" % solver.__name__)
def test_subsolvers_L2(rng, allclose):
pytest.importorskip("scipy", minversion="0.11") # version for lsmr
ref_solver = lstsq.Cholesky()
solvers = [
lstsq.Conjgrad(),
lstsq.BlockConjgrad(),
lstsq.ConjgradScipy(),
lstsq.LSMRScipy(),
]
A, B = get_system(m=2000, n=1000, d=10, rng=rng)
sigma = 0.1 * A.max()
with Timer() as t0:
x0, _ = ref_solver(A, B, sigma)
xs = np.zeros((len(solvers), ) + x0.shape)
for i, solver in enumerate(solvers):
with Timer() as t:
xs[i], info = solver(A, B, sigma)
logging.info("solver: %r", solver)
logging.info("duration: %0.3f", t.duration)
logging.info("duration relative to reference solver: %0.2f",
(t.duration / t0.duration))
logging.info("info: %s", info)
for solver, x in zip(solvers, xs):
assert allclose(x0, x, atol=1e-5,
rtol=1e-3), f"Solver {solver.__name__}"
def test_scipy_solvers(rng, allclose):
pytest.importorskip("scipy", minversion="0.11") # version for lsmr
A, b = get_system(1000, 100, 2, rng=rng)
sigma = 0.1 * A.max()
x0, i0 = lstsq.Cholesky()(A, b, sigma)
logging.info("Cholesky rmse=%0.3f", i0["rmses"].mean())
x1, i1 = lstsq.ConjgradScipy()(A, b, sigma)
logging.info(
"ConjgradScipy rmse=%0.3f, itns=%0.1f (%0.1f)",
i1["rmses"].mean(),
i1["iterations"].mean(),
i1["iterations"].std(),
)
x2, i2 = lstsq.LSMRScipy()(A, b, sigma)
logging.info(
"LSMRScipy rmse=%0.3f, itns=%0.1f (%0.1f)",
i2["rmses"].mean(),
i2["iterations"].mean(),
i2["iterations"].std(),
)
assert allclose(x0, x1, atol=2e-5, rtol=1e-3)
assert allclose(x0, x2, atol=2e-5, rtol=1e-3)
def test_scipy_solvers(rng):
pytest.importorskip('scipy', minversion='0.11') # version for lsmr
A, b = get_system(1000, 100, 2, rng=rng)
sigma = 0.1 * A.max()
x0, _ = lstsq.Cholesky()(A, b, sigma)
x1, _ = lstsq.ConjgradScipy()(A, b, sigma)
x2, _ = lstsq.LSMRScipy()(A, b, sigma)
assert np.allclose(x0, x1, atol=2e-5, rtol=1e-3)
assert np.allclose(x0, x2, atol=2e-5, rtol=1e-3)
def test_scipy_solvers(rng, logger):
pytest.importorskip('scipy', minversion='0.11') # version for lsmr
A, b = get_system(1000, 100, 2, rng=rng)
sigma = 0.1 * A.max()
x0, i0 = lstsq.Cholesky()(A, b, sigma)
logger.info("Cholesky rmse=%0.3f" % (i0['rmses'].mean(),))
x1, i1 = lstsq.ConjgradScipy()(A, b, sigma)
logger.info("ConjgradScipy rmse=%0.3f, itns=%0.1f (%0.1f)" % (
i1['rmses'].mean(), i1['iterations'].mean(), i1['iterations'].std()))
x2, i2 = lstsq.LSMRScipy()(A, b, sigma)
logger.info("LSMRScipy rmse=%0.3f, itns=%0.1f (%0.1f)" % (
i2['rmses'].mean(), i2['iterations'].mean(), i2['iterations'].std()))
assert np.allclose(x0, x1, atol=2e-5, rtol=1e-3)
assert np.allclose(x0, x2, atol=2e-5, rtol=1e-3)
