def dense_options(default_solver='solve',
default_least_squares_solver='least_squares_lstsq',
least_squares_lstsq_rcond=-1.):
"""Returns |solver_options| (with default values) for dense |NumPy| matricies.
Parameters
----------
default_solver
Default dense solver to use (solve, least_squares_lstsq, generic_lgmres,
least_squares_generic_lsmr, least_squares_generic_lsqr).
default_least_squares_solver
Default solver to use for least squares problems (least_squares_lstsq,
least_squares_generic_lsmr, least_squares_generic_lsqr).
least_squares_lstsq_rcond
See :func:`numpy.linalg.lstsq`.
Returns
-------
A tuple of possible values for |solver_options|.
"""
assert default_least_squares_solver.startswith('least_squares')
opts = (('solve', {
'type': 'solve'
}), ('least_squares_lstsq', {
'type': 'least_squares_lstsq',
'rcond': least_squares_lstsq_rcond
}))
opts = OrderedDict(opts)
opts.update(genericsolvers.options())
def_opt = opts.pop(default_solver)
if default_least_squares_solver != default_solver:
def_ls_opt = opts.pop(default_least_squares_solver)
ordered_opts = OrderedDict(
((default_solver, def_opt), (default_least_squares_solver,
def_ls_opt)))
else:
ordered_opts = OrderedDict(((default_solver, def_opt), ))
ordered_opts.update(opts)
return ordered_opts
def dense_options(default_solver='solve',
default_least_squares_solver='least_squares_lstsq',
least_squares_lstsq_rcond=-1.):
"""Returns |solver_options| (with default values) for dense |NumPy| matricies.
Parameters
----------
default_solver
Default dense solver to use (solve, least_squares_lstsq, generic_lgmres,
least_squares_generic_lsmr, least_squares_generic_lsqr).
default_least_squares_solver
Default solver to use for least squares problems (least_squares_lstsq,
least_squares_generic_lsmr, least_squares_generic_lsqr).
least_squares_lstsq_rcond
See :func:`numpy.linalg.lstsq`.
Returns
-------
A tuple of possible values for |solver_options|.
"""
assert default_least_squares_solver.startswith('least_squares')
opts = (('solve', {'type': 'solve'}),
('least_squares_lstsq', {'type': 'least_squares_lstsq',
'rcond': least_squares_lstsq_rcond}))
opts = OrderedDict(opts)
opts.update(genericsolvers.options())
def_opt = opts.pop(default_solver)
if default_least_squares_solver != default_solver:
def_ls_opt = opts.pop(default_least_squares_solver)
ordered_opts = OrderedDict(((default_solver, def_opt),
(default_least_squares_solver, def_ls_opt)))
else:
ordered_opts = OrderedDict(((default_solver, def_opt),))
ordered_opts.update(opts)
return ordered_opts
def sparse_options(default_solver='spsolve',
default_least_squares_solver='least_squares_lsmr' if HAVE_SCIPY_LSMR else 'least_squares_generic_lsmr',
bicgstab_tol=1e-15,
bicgstab_maxiter=None,
spilu_drop_tol=1e-4,
spilu_fill_factor=10,
spilu_drop_rule='basic,area',
spilu_permc_spec='COLAMD',
spsolve_permc_spec='COLAMD',
spsolve_keep_factorization=True,
lgmres_tol=1e-5,
lgmres_maxiter=1000,
lgmres_inner_m=39,
lgmres_outer_k=3,
least_squares_lsmr_damp=0.0,
least_squares_lsmr_atol=1e-6,
least_squares_lsmr_btol=1e-6,
least_squares_lsmr_conlim=1e8,
least_squares_lsmr_maxiter=None,
least_squares_lsmr_show=False,
least_squares_lsqr_damp=0.0,
least_squares_lsqr_atol=1e-6,
least_squares_lsqr_btol=1e-6,
least_squares_lsqr_conlim=1e8,
least_squares_lsqr_iter_lim=None,
least_squares_lsqr_show=False,
pyamg_tol=1e-5,
pyamg_maxiter=400,
pyamg_verb=False,
pyamg_rs_strength=('classical', {'theta': 0.25}),
pyamg_rs_CF='RS',
pyamg_rs_presmoother=('gauss_seidel', {'sweep': 'symmetric'}),
pyamg_rs_postsmoother=('gauss_seidel', {'sweep': 'symmetric'}),
pyamg_rs_max_levels=10,
pyamg_rs_max_coarse=500,
pyamg_rs_coarse_solver='pinv2',
pyamg_rs_cycle='V',
pyamg_rs_accel=None,
pyamg_rs_tol=1e-5,
pyamg_rs_maxiter=100,
pyamg_sa_symmetry='hermitian',
pyamg_sa_strength='symmetric',
pyamg_sa_aggregate='standard',
pyamg_sa_smooth=('jacobi', {'omega': 4.0/3.0}),
pyamg_sa_presmoother=('block_gauss_seidel', {'sweep': 'symmetric'}),
pyamg_sa_postsmoother=('block_gauss_seidel', {'sweep': 'symmetric'}),
pyamg_sa_improve_candidates=[('block_gauss_seidel', {'sweep': 'symmetric', 'iterations': 4}), None],
pyamg_sa_max_levels=10,
pyamg_sa_max_coarse=500,
pyamg_sa_diagonal_dominance=False,
pyamg_sa_coarse_solver='pinv2',
pyamg_sa_cycle='V',
pyamg_sa_accel=None,
pyamg_sa_tol=1e-5,
pyamg_sa_maxiter=100):
"""Returns |solver_options| (with default values) for sparse |NumPy| matricies.
Parameters
----------
default_solver
Default sparse solver to use (spsolve, bicgstab, bicgstab_spilu, pyamg,
pyamg_rs, pyamg_sa, generic_lgmres, least_squares_lsmr, least_squares_lsqr).
default_least_squares_solver
Default solver to use for least squares problems (least_squares_lsmr,
least_squares_lsqr).
bicgstab_tol
See :func:`scipy.sparse.linalg.bicgstab`.
bicgstab_maxiter
See :func:`scipy.sparse.linalg.bicgstab`.
spilu_drop_tol
See :func:`scipy.sparse.linalg.spilu`.
spilu_fill_factor
See :func:`scipy.sparse.linalg.spilu`.
spilu_drop_rule
See :func:`scipy.sparse.linalg.spilu`.
spilu_permc_spec
See :func:`scipy.sparse.linalg.spilu`.
spsolve_permc_spec
See :func:`scipy.sparse.linalg.spsolve`.
lgmres_tol
See :func:`scipy.sparse.linalg.lgmres`.
lgmres_maxiter
See :func:`scipy.sparse.linalg.lgmres`.
lgmres_inner_m
See :func:`scipy.sparse.linalg.lgmres`.
lgmres_outer_k
See :func:`scipy.sparse.linalg.lgmres`.
least_squares_lsmr_damp
See :func:`scipy.sparse.linalg.lsmr`.
least_squares_lsmr_atol
See :func:`scipy.sparse.linalg.lsmr`.
least_squares_lsmr_btol
See :func:`scipy.sparse.linalg.lsmr`.
least_squares_lsmr_conlim
See :func:`scipy.sparse.linalg.lsmr`.
least_squares_lsmr_maxiter
See :func:`scipy.sparse.linalg.lsmr`.
least_squares_lsmr_show
See :func:`scipy.sparse.linalg.lsmr`.
least_squares_lsqr_damp
See :func:`scipy.sparse.linalg.lsqr`.
least_squares_lsqr_atol
See :func:`scipy.sparse.linalg.lsqr`.
least_squares_lsqr_btol
See :func:`scipy.sparse.linalg.lsqr`.
least_squares_lsqr_conlim
See :func:`scipy.sparse.linalg.lsqr`.
least_squares_lsqr_iter_lim
See :func:`scipy.sparse.linalg.lsqr`.
least_squares_lsqr_show
See :func:`scipy.sparse.linalg.lsqr`.
pyamg_tol
Tolerance for `PyAMG <http://pyamg.github.io/>`_ blackbox solver.
pyamg_maxiter
Maximum iterations for `PyAMG <http://pyamg.github.io/>`_ blackbox solver.
pyamg_verb
Verbosity flag for `PyAMG <http://pyamg.github.io/>`_ blackbox solver.
pyamg_rs_strength
Parameter for `PyAMG <http://pyamg.github.io/>`_ Ruge-Stuben solver.
pyamg_rs_CF
Parameter for `PyAMG <http://pyamg.github.io/>`_ Ruge-Stuben solver.
pyamg_rs_presmoother
Parameter for `PyAMG <http://pyamg.github.io/>`_ Ruge-Stuben solver.
pyamg_rs_postsmoother
Parameter for `PyAMG <http://pyamg.github.io/>`_ Ruge-Stuben solver.
pyamg_rs_max_levels
Parameter for `PyAMG <http://pyamg.github.io/>`_ Ruge-Stuben solver.
pyamg_rs_max_coarse
Parameter for `PyAMG <http://pyamg.github.io/>`_ Ruge-Stuben solver.
pyamg_rs_coarse_solver
Parameter for `PyAMG <http://pyamg.github.io/>`_ Ruge-Stuben solver.
pyamg_rs_cycle
Parameter for `PyAMG <http://pyamg.github.io/>`_ Ruge-Stuben solver.
pyamg_rs_accel
Parameter for `PyAMG <http://pyamg.github.io/>`_ Ruge-Stuben solver.
pyamg_rs_tol
Parameter for `PyAMG <http://pyamg.github.io/>`_ Ruge-Stuben solver.
pyamg_rs_maxiter
Parameter for `PyAMG <http://pyamg.github.io/>`_ Ruge-Stuben solver.
pyamg_sa_symmetry
Parameter for `PyAMG <http://pyamg.github.io/>`_ Smoothed-Aggregation solver.
pyamg_sa_strength
Parameter for `PyAMG <http://pyamg.github.io/>`_ Smoothed-Aggregation solver.
pyamg_sa_aggregate
Parameter for `PyAMG <http://pyamg.github.io/>`_ Smoothed-Aggregation solver.
pyamg_sa_smooth
Parameter for `PyAMG <http://pyamg.github.io/>`_ Smoothed-Aggregation solver.
pyamg_sa_presmoother
Parameter for `PyAMG <http://pyamg.github.io/>`_ Smoothed-Aggregation solver.
pyamg_sa_postsmoother
Parameter for `PyAMG <http://pyamg.github.io/>`_ Smoothed-Aggregation solver.
pyamg_sa_improve_candidates
Parameter for `PyAMG <http://pyamg.github.io/>`_ Smoothed-Aggregation solver.
pyamg_sa_max_levels
Parameter for `PyAMG <http://pyamg.github.io/>`_ Smoothed-Aggregation solver.
pyamg_sa_max_coarse
Parameter for `PyAMG <http://pyamg.github.io/>`_ Smoothed-Aggregation solver.
pyamg_sa_diagonal_dominance
Parameter for `PyAMG <http://pyamg.github.io/>`_ Smoothed-Aggregation solver.
pyamg_sa_coarse_solver
Parameter for `PyAMG <http://pyamg.github.io/>`_ Smoothed-Aggregation solver.
pyamg_sa_cycle
Parameter for `PyAMG <http://pyamg.github.io/>`_ Smoothed-Aggregation solver.
pyamg_sa_accel
Parameter for `PyAMG <http://pyamg.github.io/>`_ Smoothed-Aggregation solver.
pyamg_sa_tol
Parameter for `PyAMG <http://pyamg.github.io/>`_ Smoothed-Aggregation solver.
pyamg_sa_maxiter
Parameter for `PyAMG <http://pyamg.github.io/>`_ Smoothed-Aggregation solver.
Returns
-------
A tuple of all possible |solver_options|.
"""
assert default_least_squares_solver.startswith('least_squares')
opts = (('bicgstab_spilu', {'type': 'bicgstab_spilu',
'tol': bicgstab_tol,
'maxiter': bicgstab_maxiter,
'spilu_drop_tol': spilu_drop_tol,
'spilu_fill_factor': spilu_fill_factor,
'spilu_drop_rule': spilu_drop_rule,
'spilu_permc_spec': spilu_permc_spec}),
('bicgstab', {'type': 'bicgstab',
'tol': bicgstab_tol,
'maxiter': bicgstab_maxiter}),
('spsolve', {'type': 'spsolve',
'permc_spec': spsolve_permc_spec,
'keep_factorization': spsolve_keep_factorization}),
('lgmres', {'type': 'lgmres',
'tol': lgmres_tol,
'maxiter': lgmres_maxiter,
'inner_m': lgmres_inner_m,
'outer_k': lgmres_outer_k}),
('least_squares_lsqr', {'type': 'least_squares_lsqr',
'damp': least_squares_lsqr_damp,
'atol': least_squares_lsqr_atol,
'btol': least_squares_lsqr_btol,
'conlim': least_squares_lsqr_conlim,
'iter_lim': least_squares_lsqr_iter_lim,
'show': least_squares_lsqr_show}))
if HAVE_SCIPY_LSMR:
opts += (('least_squares_lsmr', {'type': 'least_squares_lsmr',
'damp': least_squares_lsmr_damp,
'atol': least_squares_lsmr_atol,
'btol': least_squares_lsmr_btol,
'conlim': least_squares_lsmr_conlim,
'maxiter': least_squares_lsmr_maxiter,
'show': least_squares_lsmr_show}),)
if HAVE_PYAMG:
opts += (('pyamg', {'type': 'pyamg',
'tol': pyamg_tol,
'maxiter': pyamg_maxiter}),
('pyamg-rs', {'type': 'pyamg-rs',
'strength': pyamg_rs_strength,
'CF': pyamg_rs_CF,
'presmoother': pyamg_rs_presmoother,
'postsmoother': pyamg_rs_postsmoother,
'max_levels': pyamg_rs_max_levels,
'max_coarse': pyamg_rs_max_coarse,
'coarse_solver': pyamg_rs_coarse_solver,
'cycle': pyamg_rs_cycle,
'accel': pyamg_rs_accel,
'tol': pyamg_rs_tol,
'maxiter': pyamg_rs_maxiter}),
('pyamg-sa', {'type': 'pyamg-sa',
'symmetry': pyamg_sa_symmetry,
'strength': pyamg_sa_strength,
'aggregate': pyamg_sa_aggregate,
'smooth': pyamg_sa_smooth,
'presmoother': pyamg_sa_presmoother,
'postsmoother': pyamg_sa_postsmoother,
'improve_candidates': pyamg_sa_improve_candidates,
'max_levels': pyamg_sa_max_levels,
'max_coarse': pyamg_sa_max_coarse,
'diagonal_dominance': pyamg_sa_diagonal_dominance,
'coarse_solver': pyamg_sa_coarse_solver,
'cycle': pyamg_sa_cycle,
'accel': pyamg_sa_accel,
'tol': pyamg_sa_tol,
'maxiter': pyamg_sa_maxiter}))
opts = OrderedDict(opts)
opts.update(genericsolvers.options())
def_opt = opts.pop(default_solver)
if default_least_squares_solver != default_solver:
def_ls_opt = opts.pop(default_least_squares_solver)
ordered_opts = OrderedDict(((default_solver, def_opt),
(default_least_squares_solver, def_ls_opt)))
else:
ordered_opts = OrderedDict(((default_solver, def_opt),))
ordered_opts.update(opts)
return ordered_opts
def sparse_options(
default_solver="spsolve",
default_least_squares_solver="least_squares_lsmr",
bicgstab_tol=1e-15,
bicgstab_maxiter=None,
spilu_drop_tol=1e-4,
spilu_fill_factor=10,
spilu_drop_rule="basic,area",
spilu_permc_spec="COLAMD",
spsolve_permc_spec="COLAMD",
spsolve_keep_factorization=True,
lgmres_tol=1e-5,
lgmres_maxiter=1000,
lgmres_inner_m=39,
lgmres_outer_k=3,
least_squares_lsmr_damp=0.0,
least_squares_lsmr_atol=1e-6,
least_squares_lsmr_btol=1e-6,
least_squares_lsmr_conlim=1e8,
least_squares_lsmr_maxiter=None,
least_squares_lsmr_show=False,
least_squares_lsqr_damp=0.0,
least_squares_lsqr_atol=1e-6,
least_squares_lsqr_btol=1e-6,
least_squares_lsqr_conlim=1e8,
least_squares_lsqr_iter_lim=None,
least_squares_lsqr_show=False,
pyamg_tol=1e-5,
pyamg_maxiter=400,
pyamg_verb=False,
pyamg_rs_strength=("classical", {"theta": 0.25}),
pyamg_rs_CF="RS",
pyamg_rs_presmoother=("gauss_seidel", {"sweep": "symmetric"}),
pyamg_rs_postsmoother=("gauss_seidel", {"sweep": "symmetric"}),
pyamg_rs_max_levels=10,
pyamg_rs_max_coarse=500,
pyamg_rs_coarse_solver="pinv2",
pyamg_rs_cycle="V",
pyamg_rs_accel=None,
pyamg_rs_tol=1e-5,
pyamg_rs_maxiter=100,
pyamg_sa_symmetry="hermitian",
pyamg_sa_strength="symmetric",
pyamg_sa_aggregate="standard",
pyamg_sa_smooth=("jacobi", {"omega": 4.0 / 3.0}),
pyamg_sa_presmoother=("block_gauss_seidel", {"sweep": "symmetric"}),
pyamg_sa_postsmoother=("block_gauss_seidel", {"sweep": "symmetric"}),
pyamg_sa_improve_candidates=[("block_gauss_seidel", {"sweep": "symmetric", "iterations": 4}), None],
pyamg_sa_max_levels=10,
pyamg_sa_max_coarse=500,
pyamg_sa_diagonal_dominance=False,
pyamg_sa_coarse_solver="pinv2",
pyamg_sa_cycle="V",
pyamg_sa_accel=None,
pyamg_sa_tol=1e-5,
pyamg_sa_maxiter=100,
):
"""Returns |solver_options| (with default values) for sparse |NumPy| matricies.
Parameters
----------
default_solver
Default sparse solver to use (spsolve, bicgstab, bicgstab_spilu, pyamg,
pyamg_rs, pyamg_sa, generic_lgmres, least_squares_lsmr, least_squares_lsqr).
default_least_squares_solver
Default solver to use for least squares problems (least_squares_lsmr,
least_squares_lsqr).
bicgstab_tol
See :func:`scipy.sparse.linalg.bicgstab`.
bicgstab_maxiter
See :func:`scipy.sparse.linalg.bicgstab`.
spilu_drop_tol
See :func:`scipy.sparse.linalg.spilu`.
spilu_fill_factor
See :func:`scipy.sparse.linalg.spilu`.
spilu_drop_rule
See :func:`scipy.sparse.linalg.spilu`.
spilu_permc_spec
See :func:`scipy.sparse.linalg.spilu`.
spsolve_permc_spec
See :func:`scipy.sparse.linalg.spsolve`.
lgmres_tol
See :func:`scipy.sparse.linalg.lgmres`.
lgmres_maxiter
See :func:`scipy.sparse.linalg.lgmres`.
lgmres_inner_m
See :func:`scipy.sparse.linalg.lgmres`.
lgmres_outer_k
See :func:`scipy.sparse.linalg.lgmres`.
least_squares_lsmr_damp
See :func:`scipy.sparse.linalg.lsmr`.
least_squares_lsmr_atol
See :func:`scipy.sparse.linalg.lsmr`.
least_squares_lsmr_btol
See :func:`scipy.sparse.linalg.lsmr`.
least_squares_lsmr_conlim
See :func:`scipy.sparse.linalg.lsmr`.
least_squares_lsmr_maxiter
See :func:`scipy.sparse.linalg.lsmr`.
least_squares_lsmr_show
See :func:`scipy.sparse.linalg.lsmr`.
least_squares_lsqr_damp
See :func:`scipy.sparse.linalg.lsqr`.
least_squares_lsqr_atol
See :func:`scipy.sparse.linalg.lsqr`.
least_squares_lsqr_btol
See :func:`scipy.sparse.linalg.lsqr`.
least_squares_lsqr_conlim
See :func:`scipy.sparse.linalg.lsqr`.
least_squares_lsqr_iter_lim
See :func:`scipy.sparse.linalg.lsqr`.
least_squares_lsqr_show
See :func:`scipy.sparse.linalg.lsqr`.
pyamg_tol
Tolerance for `PyAMG <http://pyamg.github.io/>`_ blackbox solver.
pyamg_maxiter
Maximum iterations for `PyAMG <http://pyamg.github.io/>`_ blackbox solver.
pyamg_verb
Verbosity flag for `PyAMG <http://pyamg.github.io/>`_ blackbox solver.
pyamg_rs_strength
Parameter for `PyAMG <http://pyamg.github.io/>`_ Ruge-Stuben solver.
pyamg_rs_CF
Parameter for `PyAMG <http://pyamg.github.io/>`_ Ruge-Stuben solver.
pyamg_rs_presmoother
Parameter for `PyAMG <http://pyamg.github.io/>`_ Ruge-Stuben solver.
pyamg_rs_postsmoother
Parameter for `PyAMG <http://pyamg.github.io/>`_ Ruge-Stuben solver.
pyamg_rs_max_levels
Parameter for `PyAMG <http://pyamg.github.io/>`_ Ruge-Stuben solver.
pyamg_rs_max_coarse
Parameter for `PyAMG <http://pyamg.github.io/>`_ Ruge-Stuben solver.
pyamg_rs_coarse_solver
Parameter for `PyAMG <http://pyamg.github.io/>`_ Ruge-Stuben solver.
pyamg_rs_cycle
Parameter for `PyAMG <http://pyamg.github.io/>`_ Ruge-Stuben solver.
pyamg_rs_accel
Parameter for `PyAMG <http://pyamg.github.io/>`_ Ruge-Stuben solver.
pyamg_rs_tol
Parameter for `PyAMG <http://pyamg.github.io/>`_ Ruge-Stuben solver.
pyamg_rs_maxiter
Parameter for `PyAMG <http://pyamg.github.io/>`_ Ruge-Stuben solver.
pyamg_sa_symmetry
Parameter for `PyAMG <http://pyamg.github.io/>`_ Smoothed-Aggregation solver.
pyamg_sa_strength
Parameter for `PyAMG <http://pyamg.github.io/>`_ Smoothed-Aggregation solver.
pyamg_sa_aggregate
Parameter for `PyAMG <http://pyamg.github.io/>`_ Smoothed-Aggregation solver.
pyamg_sa_smooth
Parameter for `PyAMG <http://pyamg.github.io/>`_ Smoothed-Aggregation solver.
pyamg_sa_presmoother
Parameter for `PyAMG <http://pyamg.github.io/>`_ Smoothed-Aggregation solver.
pyamg_sa_postsmoother
Parameter for `PyAMG <http://pyamg.github.io/>`_ Smoothed-Aggregation solver.
pyamg_sa_improve_candidates
Parameter for `PyAMG <http://pyamg.github.io/>`_ Smoothed-Aggregation solver.
pyamg_sa_max_levels
Parameter for `PyAMG <http://pyamg.github.io/>`_ Smoothed-Aggregation solver.
pyamg_sa_max_coarse
Parameter for `PyAMG <http://pyamg.github.io/>`_ Smoothed-Aggregation solver.
pyamg_sa_diagonal_dominance
Parameter for `PyAMG <http://pyamg.github.io/>`_ Smoothed-Aggregation solver.
pyamg_sa_coarse_solver
Parameter for `PyAMG <http://pyamg.github.io/>`_ Smoothed-Aggregation solver.
pyamg_sa_cycle
Parameter for `PyAMG <http://pyamg.github.io/>`_ Smoothed-Aggregation solver.
pyamg_sa_accel
Parameter for `PyAMG <http://pyamg.github.io/>`_ Smoothed-Aggregation solver.
pyamg_sa_tol
Parameter for `PyAMG <http://pyamg.github.io/>`_ Smoothed-Aggregation solver.
pyamg_sa_maxiter
Parameter for `PyAMG <http://pyamg.github.io/>`_ Smoothed-Aggregation solver.
Returns
-------
A tuple of all possible |solver_options|.
"""
assert default_least_squares_solver.startswith("least_squares")
opts = (
(
"bicgstab_spilu",
{
"type": "bicgstab_spilu",
"tol": bicgstab_tol,
"maxiter": bicgstab_maxiter,
"spilu_drop_tol": spilu_drop_tol,
"spilu_fill_factor": spilu_fill_factor,
"spilu_drop_rule": spilu_drop_rule,
"spilu_permc_spec": spilu_permc_spec,
},
),
("bicgstab", {"type": "bicgstab", "tol": bicgstab_tol, "maxiter": bicgstab_maxiter}),
(
"spsolve",
{"type": "spsolve", "permc_spec": spsolve_permc_spec, "keep_factorization": spsolve_keep_factorization},
),
(
"lgmres",
{
"type": "lgmres",
"tol": lgmres_tol,
"maxiter": lgmres_maxiter,
"inner_m": lgmres_inner_m,
"outer_k": lgmres_outer_k,
},
),
(
"least_squares_lsqr",
{
"type": "least_squares_lsqr",
"damp": least_squares_lsqr_damp,
"atol": least_squares_lsqr_atol,
"btol": least_squares_lsqr_btol,
"conlim": least_squares_lsqr_conlim,
"iter_lim": least_squares_lsqr_iter_lim,
"show": least_squares_lsqr_show,
},
),
)
if HAVE_SCIPY_LSMR:
opts += (
(
"least_squares_lsmr",
{
"type": "least_squares_lsmr",
"damp": least_squares_lsmr_damp,
"atol": least_squares_lsmr_atol,
"btol": least_squares_lsmr_btol,
"conlim": least_squares_lsmr_conlim,
"maxiter": least_squares_lsmr_maxiter,
"show": least_squares_lsmr_show,
},
),
)
if HAVE_PYAMG:
opts += (
("pyamg", {"type": "pyamg", "tol": pyamg_tol, "maxiter": pyamg_maxiter}),
(
"pyamg-rs",
{
"type": "pyamg-rs",
"strength": pyamg_rs_strength,
"CF": pyamg_rs_CF,
"presmoother": pyamg_rs_presmoother,
"postsmoother": pyamg_rs_postsmoother,
"max_levels": pyamg_rs_max_levels,
"max_coarse": pyamg_rs_max_coarse,
"coarse_solver": pyamg_rs_coarse_solver,
"cycle": pyamg_rs_cycle,
"accel": pyamg_rs_accel,
"tol": pyamg_rs_tol,
"maxiter": pyamg_rs_maxiter,
},
),
(
"pyamg-sa",
{
"type": "pyamg-sa",
"symmetry": pyamg_sa_symmetry,
"strength": pyamg_sa_strength,
"aggregate": pyamg_sa_aggregate,
"smooth": pyamg_sa_smooth,
"presmoother": pyamg_sa_presmoother,
"postsmoother": pyamg_sa_postsmoother,
"improve_candidates": pyamg_sa_improve_candidates,
"max_levels": pyamg_sa_max_levels,
"max_coarse": pyamg_sa_max_coarse,
"diagonal_dominance": pyamg_sa_diagonal_dominance,
"coarse_solver": pyamg_sa_coarse_solver,
"cycle": pyamg_sa_cycle,
"accel": pyamg_sa_accel,
"tol": pyamg_sa_tol,
"maxiter": pyamg_sa_maxiter,
},
),
)
opts = OrderedDict(opts)
opts.update(genericsolvers.options())
def_opt = opts.pop(default_solver)
if default_least_squares_solver != default_solver:
def_ls_opt = opts.pop(default_least_squares_solver)
ordered_opts = OrderedDict(((default_solver, def_opt), (default_least_squares_solver, def_ls_opt)))
else:
ordered_opts = OrderedDict(((default_solver, def_opt),))
ordered_opts.update(opts)
return ordered_opts
def sparse_options(default_solver='spsolve',
default_least_squares_solver='least_squares_lsmr'
if HAVE_SCIPY_LSMR else 'least_squares_generic_lsmr',
bicgstab_tol=1e-15,
bicgstab_maxiter=None,
spilu_drop_tol=1e-4,
spilu_fill_factor=10,
spilu_drop_rule='basic,area',
spilu_permc_spec='COLAMD',
spsolve_permc_spec='COLAMD',
spsolve_keep_factorization=True,
lgmres_tol=1e-5,
lgmres_maxiter=1000,
lgmres_inner_m=39,
lgmres_outer_k=3,
least_squares_lsmr_damp=0.0,
least_squares_lsmr_atol=1e-6,
least_squares_lsmr_btol=1e-6,
least_squares_lsmr_conlim=1e8,
least_squares_lsmr_maxiter=None,
least_squares_lsmr_show=False,
least_squares_lsqr_damp=0.0,
least_squares_lsqr_atol=1e-6,
least_squares_lsqr_btol=1e-6,
least_squares_lsqr_conlim=1e8,
least_squares_lsqr_iter_lim=None,
least_squares_lsqr_show=False,
pyamg_tol=1e-5,
pyamg_maxiter=400,
pyamg_verb=False,
pyamg_rs_strength=('classical', {
'theta': 0.25
}),
pyamg_rs_CF='RS',
pyamg_rs_presmoother=('gauss_seidel', {
'sweep': 'symmetric'
}),
pyamg_rs_postsmoother=('gauss_seidel', {
'sweep': 'symmetric'
}),
pyamg_rs_max_levels=10,
pyamg_rs_max_coarse=500,
pyamg_rs_coarse_solver='pinv2',
pyamg_rs_cycle='V',
pyamg_rs_accel=None,
pyamg_rs_tol=1e-5,
pyamg_rs_maxiter=100,
pyamg_sa_symmetry='hermitian',
pyamg_sa_strength='symmetric',
pyamg_sa_aggregate='standard',
pyamg_sa_smooth=('jacobi', {
'omega': 4.0 / 3.0
}),
pyamg_sa_presmoother=('block_gauss_seidel', {
'sweep': 'symmetric'
}),
pyamg_sa_postsmoother=('block_gauss_seidel', {
'sweep': 'symmetric'
}),
pyamg_sa_improve_candidates=[('block_gauss_seidel', {
'sweep': 'symmetric',
'iterations': 4
}), None],
pyamg_sa_max_levels=10,
pyamg_sa_max_coarse=500,
pyamg_sa_diagonal_dominance=False,
pyamg_sa_coarse_solver='pinv2',
pyamg_sa_cycle='V',
pyamg_sa_accel=None,
pyamg_sa_tol=1e-5,
pyamg_sa_maxiter=100):
"""Returns |solver_options| (with default values) for sparse |NumPy| matricies.
Parameters
----------
default_solver
Default sparse solver to use (spsolve, bicgstab, bicgstab_spilu, pyamg,
pyamg_rs, pyamg_sa, generic_lgmres, least_squares_lsmr, least_squares_lsqr).
default_least_squares_solver
Default solver to use for least squares problems (least_squares_lsmr,
least_squares_lsqr).
bicgstab_tol
See :func:`scipy.sparse.linalg.bicgstab`.
bicgstab_maxiter
See :func:`scipy.sparse.linalg.bicgstab`.
spilu_drop_tol
See :func:`scipy.sparse.linalg.spilu`.
spilu_fill_factor
See :func:`scipy.sparse.linalg.spilu`.
spilu_drop_rule
See :func:`scipy.sparse.linalg.spilu`.
spilu_permc_spec
See :func:`scipy.sparse.linalg.spilu`.
spsolve_permc_spec
See :func:`scipy.sparse.linalg.spsolve`.
lgmres_tol
See :func:`scipy.sparse.linalg.lgmres`.
lgmres_maxiter
See :func:`scipy.sparse.linalg.lgmres`.
lgmres_inner_m
See :func:`scipy.sparse.linalg.lgmres`.
lgmres_outer_k
See :func:`scipy.sparse.linalg.lgmres`.
least_squares_lsmr_damp
See :func:`scipy.sparse.linalg.lsmr`.
least_squares_lsmr_atol
See :func:`scipy.sparse.linalg.lsmr`.
least_squares_lsmr_btol
See :func:`scipy.sparse.linalg.lsmr`.
least_squares_lsmr_conlim
See :func:`scipy.sparse.linalg.lsmr`.
least_squares_lsmr_maxiter
See :func:`scipy.sparse.linalg.lsmr`.
least_squares_lsmr_show
See :func:`scipy.sparse.linalg.lsmr`.
least_squares_lsqr_damp
See :func:`scipy.sparse.linalg.lsqr`.
least_squares_lsqr_atol
See :func:`scipy.sparse.linalg.lsqr`.
least_squares_lsqr_btol
See :func:`scipy.sparse.linalg.lsqr`.
least_squares_lsqr_conlim
See :func:`scipy.sparse.linalg.lsqr`.
least_squares_lsqr_iter_lim
See :func:`scipy.sparse.linalg.lsqr`.
least_squares_lsqr_show
See :func:`scipy.sparse.linalg.lsqr`.
pyamg_tol
Tolerance for `PyAMG <http://pyamg.github.io/>`_ blackbox solver.
pyamg_maxiter
Maximum iterations for `PyAMG <http://pyamg.github.io/>`_ blackbox solver.
pyamg_verb
Verbosity flag for `PyAMG <http://pyamg.github.io/>`_ blackbox solver.
pyamg_rs_strength
Parameter for `PyAMG <http://pyamg.github.io/>`_ Ruge-Stuben solver.
pyamg_rs_CF
Parameter for `PyAMG <http://pyamg.github.io/>`_ Ruge-Stuben solver.
pyamg_rs_presmoother
Parameter for `PyAMG <http://pyamg.github.io/>`_ Ruge-Stuben solver.
pyamg_rs_postsmoother
Parameter for `PyAMG <http://pyamg.github.io/>`_ Ruge-Stuben solver.
pyamg_rs_max_levels
Parameter for `PyAMG <http://pyamg.github.io/>`_ Ruge-Stuben solver.
pyamg_rs_max_coarse
Parameter for `PyAMG <http://pyamg.github.io/>`_ Ruge-Stuben solver.
pyamg_rs_coarse_solver
Parameter for `PyAMG <http://pyamg.github.io/>`_ Ruge-Stuben solver.
pyamg_rs_cycle
Parameter for `PyAMG <http://pyamg.github.io/>`_ Ruge-Stuben solver.
pyamg_rs_accel
Parameter for `PyAMG <http://pyamg.github.io/>`_ Ruge-Stuben solver.
pyamg_rs_tol
Parameter for `PyAMG <http://pyamg.github.io/>`_ Ruge-Stuben solver.
pyamg_rs_maxiter
Parameter for `PyAMG <http://pyamg.github.io/>`_ Ruge-Stuben solver.
pyamg_sa_symmetry
Parameter for `PyAMG <http://pyamg.github.io/>`_ Smoothed-Aggregation solver.
pyamg_sa_strength
Parameter for `PyAMG <http://pyamg.github.io/>`_ Smoothed-Aggregation solver.
pyamg_sa_aggregate
Parameter for `PyAMG <http://pyamg.github.io/>`_ Smoothed-Aggregation solver.
pyamg_sa_smooth
Parameter for `PyAMG <http://pyamg.github.io/>`_ Smoothed-Aggregation solver.
pyamg_sa_presmoother
Parameter for `PyAMG <http://pyamg.github.io/>`_ Smoothed-Aggregation solver.
pyamg_sa_postsmoother
Parameter for `PyAMG <http://pyamg.github.io/>`_ Smoothed-Aggregation solver.
pyamg_sa_improve_candidates
Parameter for `PyAMG <http://pyamg.github.io/>`_ Smoothed-Aggregation solver.
pyamg_sa_max_levels
Parameter for `PyAMG <http://pyamg.github.io/>`_ Smoothed-Aggregation solver.
pyamg_sa_max_coarse
Parameter for `PyAMG <http://pyamg.github.io/>`_ Smoothed-Aggregation solver.
pyamg_sa_diagonal_dominance
Parameter for `PyAMG <http://pyamg.github.io/>`_ Smoothed-Aggregation solver.
pyamg_sa_coarse_solver
Parameter for `PyAMG <http://pyamg.github.io/>`_ Smoothed-Aggregation solver.
pyamg_sa_cycle
Parameter for `PyAMG <http://pyamg.github.io/>`_ Smoothed-Aggregation solver.
pyamg_sa_accel
Parameter for `PyAMG <http://pyamg.github.io/>`_ Smoothed-Aggregation solver.
pyamg_sa_tol
Parameter for `PyAMG <http://pyamg.github.io/>`_ Smoothed-Aggregation solver.
pyamg_sa_maxiter
Parameter for `PyAMG <http://pyamg.github.io/>`_ Smoothed-Aggregation solver.
Returns
-------
A tuple of all possible |solver_options|.
"""
assert default_least_squares_solver.startswith('least_squares')
opts = (('bicgstab_spilu', {
'type': 'bicgstab_spilu',
'tol': bicgstab_tol,
'maxiter': bicgstab_maxiter,
'spilu_drop_tol': spilu_drop_tol,
'spilu_fill_factor': spilu_fill_factor,
'spilu_drop_rule': spilu_drop_rule,
'spilu_permc_spec': spilu_permc_spec
}), ('bicgstab', {
'type': 'bicgstab',
'tol': bicgstab_tol,
'maxiter': bicgstab_maxiter
}), ('spsolve', {
'type': 'spsolve',
'permc_spec': spsolve_permc_spec,
'keep_factorization': spsolve_keep_factorization
}), ('lgmres', {
'type': 'lgmres',
'tol': lgmres_tol,
'maxiter': lgmres_maxiter,
'inner_m': lgmres_inner_m,
'outer_k': lgmres_outer_k
}), ('least_squares_lsqr', {
'type': 'least_squares_lsqr',
'damp': least_squares_lsqr_damp,
'atol': least_squares_lsqr_atol,
'btol': least_squares_lsqr_btol,
'conlim': least_squares_lsqr_conlim,
'iter_lim': least_squares_lsqr_iter_lim,
'show': least_squares_lsqr_show
}))
if HAVE_SCIPY_LSMR:
opts += (('least_squares_lsmr', {
'type': 'least_squares_lsmr',
'damp': least_squares_lsmr_damp,
'atol': least_squares_lsmr_atol,
'btol': least_squares_lsmr_btol,
'conlim': least_squares_lsmr_conlim,
'maxiter': least_squares_lsmr_maxiter,
'show': least_squares_lsmr_show
}), )
if HAVE_PYAMG:
opts += (('pyamg', {
'type': 'pyamg',
'tol': pyamg_tol,
'maxiter': pyamg_maxiter
}), ('pyamg-rs', {
'type': 'pyamg-rs',
'strength': pyamg_rs_strength,
'CF': pyamg_rs_CF,
'presmoother': pyamg_rs_presmoother,
'postsmoother': pyamg_rs_postsmoother,
'max_levels': pyamg_rs_max_levels,
'max_coarse': pyamg_rs_max_coarse,
'coarse_solver': pyamg_rs_coarse_solver,
'cycle': pyamg_rs_cycle,
'accel': pyamg_rs_accel,
'tol': pyamg_rs_tol,
'maxiter': pyamg_rs_maxiter
}), ('pyamg-sa', {
'type': 'pyamg-sa',
'symmetry': pyamg_sa_symmetry,
'strength': pyamg_sa_strength,
'aggregate': pyamg_sa_aggregate,
'smooth': pyamg_sa_smooth,
'presmoother': pyamg_sa_presmoother,
'postsmoother': pyamg_sa_postsmoother,
'improve_candidates': pyamg_sa_improve_candidates,
'max_levels': pyamg_sa_max_levels,
'max_coarse': pyamg_sa_max_coarse,
'diagonal_dominance': pyamg_sa_diagonal_dominance,
'coarse_solver': pyamg_sa_coarse_solver,
'cycle': pyamg_sa_cycle,
'accel': pyamg_sa_accel,
'tol': pyamg_sa_tol,
'maxiter': pyamg_sa_maxiter
}))
opts = OrderedDict(opts)
opts.update(genericsolvers.options())
def_opt = opts.pop(default_solver)
if default_least_squares_solver != default_solver:
def_ls_opt = opts.pop(default_least_squares_solver)
ordered_opts = OrderedDict(
((default_solver, def_opt), (default_least_squares_solver,
def_ls_opt)))
else:
ordered_opts = OrderedDict(((default_solver, def_opt), ))
ordered_opts.update(opts)
return ordered_opts