def run_3d_apriori():
'''Automatically choose deflation vectors with a priori bound.'''
numpy.random.seed(0)
def vector_factory_generator(_):
return krypy.recycling.factories.RitzFactory(
subsets_generator=krypy.recycling.generators.RitzSmall(
max_vectors=20,
),
subset_evaluator=krypy.recycling.evaluators.RitzApriori(
Bound=krypy.utils.BoundMinres,
deflweight=2.
),
#print_results='values'
)
recycling_solver = pynosh_helper.run(
'exp_pynosh_3d.vtu',
RecyclingSolver=DiagRecMinres,
recycling_solver_kwargs={'maxiter': 280},
vector_factory_generator=vector_factory_generator
)
plot_resnorms(recycling_solver, 'quad bound + a priori strategy (2d)',
xlim=(0, 280), ylabel=None)
return recycling_solver
def run_3d_approx():
'''Automatically choose deflation vectors with approximate Krylov.'''
numpy.random.seed(0)
def vector_factory_generator(_):
return krypy.recycling.factories.RitzFactory(
subsets_generator=krypy.recycling.generators.RitzSmall(
max_vectors=20,
),
subset_evaluator=krypy.recycling.evaluators.RitzApproxKrylov(
mode='extrapolate',
pseudospectra=False,
deflweight=2.
),
#print_results='values'
)
recycling_solver = pynosh_helper.run(
'exp_pynosh_3d.vtu',
RecyclingSolver=DiagRecMinres,
recycling_solver_kwargs={'maxiter': 280},
vector_factory_generator=vector_factory_generator
)
plot_resnorms(recycling_solver, 'approximate Krylov strategy (2d)',
xlim=(0, 280), ylabel=None)
return recycling_solver
def run_2d_vanilla():
'''No deflation.'''
numpy.random.seed(0)
recycling_solver = pynosh_helper.run(
'exp_pynosh_2d.vtu',
RecyclingSolver=DiagRecMinres,
recycling_solver_kwargs={'explicit_residual': True})
plot_resnorms(recycling_solver, 'no recycling (2d)')
plot_ritz_values(recycling_solver, 'Ritz values (2d)')
# rerun without explicit residual for timings
numpy.random.seed(0)
recycling_solver = pynosh_helper.run(
'exp_pynosh_2d.vtu',
RecyclingSolver=DiagRecMinres,
)
return recycling_solver
def run_2d_vanilla():
'''No deflation.'''
numpy.random.seed(0)
recycling_solver = pynosh_helper.run(
'exp_pynosh_2d.vtu',
RecyclingSolver=DiagRecMinres,
recycling_solver_kwargs={'explicit_residual': True}
)
plot_resnorms(recycling_solver, 'no recycling (2d)')
plot_ritz_values(recycling_solver, 'Ritz values (2d)')
# rerun without explicit residual for timings
numpy.random.seed(0)
recycling_solver = pynosh_helper.run(
'exp_pynosh_2d.vtu',
RecyclingSolver=DiagRecMinres,
)
return recycling_solver
def run_3d_vanilla():
'''No deflation.'''
numpy.random.seed(0)
recycling_solver = pynosh_helper.run(
'exp_pynosh_3d.vtu',
RecyclingSolver=DiagRecMinres,
recycling_solver_kwargs={'maxiter': 280}
)
plot_resnorms(recycling_solver, 'no recycling (3d)',
xlim=(0, 280), ylabel=None)
plot_ritz_values(recycling_solver, 'Ritz values (3d)')
return recycling_solver
def run_3d_vanilla():
'''No deflation.'''
numpy.random.seed(0)
recycling_solver = pynosh_helper.run(
'exp_pynosh_3d.vtu',
RecyclingSolver=DiagRecMinres,
recycling_solver_kwargs={'maxiter': 280})
plot_resnorms(recycling_solver,
'no recycling (3d)',
xlim=(0, 280),
ylabel=None)
plot_ritz_values(recycling_solver, 'Ritz values (3d)')
return recycling_solver
def run_2d_fixed():
'''Always choose 12 Ritz vectors with Ritz values of smallest magnitude.'''
numpy.random.seed(0)
def vector_factory_generator(_):
return krypy.recycling.factories.RitzFactorySimple(n_vectors=12,
which='sm')
recycling_solver = pynosh_helper.run(
'exp_pynosh_2d.vtu',
RecyclingSolver=DiagRecMinres,
vector_factory_generator=vector_factory_generator)
plot_resnorms(recycling_solver, '12 deflation vectors (2d)')
return recycling_solver
def run_2d_fixed():
'''Always choose 12 Ritz vectors with Ritz values of smallest magnitude.'''
numpy.random.seed(0)
def vector_factory_generator(_):
return krypy.recycling.factories.RitzFactorySimple(
n_vectors=12,
which='sm'
)
recycling_solver = pynosh_helper.run(
'exp_pynosh_2d.vtu',
RecyclingSolver=DiagRecMinres,
vector_factory_generator=vector_factory_generator
)
plot_resnorms(recycling_solver, '12 deflation vectors (2d)')
return recycling_solver
def run_3d_fixed():
'''Always choose 12 Ritz vectors with Ritz values of smallest magnitude.'''
numpy.random.seed(0)
def vector_factory_generator(_):
return krypy.recycling.factories.RitzFactorySimple(n_vectors=12,
which='sm')
recycling_solver = pynosh_helper.run(
'exp_pynosh_3d.vtu',
RecyclingSolver=DiagRecMinres,
recycling_solver_kwargs={'maxiter': 280},
vector_factory_generator=vector_factory_generator)
plot_resnorms(recycling_solver,
'12 deflation vectors (3d)',
xlim=(0, 280),
ylabel=None)
return recycling_solver
def run_3d_fixed():
'''Always choose 12 Ritz vectors with Ritz values of smallest magnitude.'''
numpy.random.seed(0)
def vector_factory_generator(_):
return krypy.recycling.factories.RitzFactorySimple(
n_vectors=12,
which='sm'
)
recycling_solver = pynosh_helper.run(
'exp_pynosh_3d.vtu',
RecyclingSolver=DiagRecMinres,
recycling_solver_kwargs={'maxiter': 280},
vector_factory_generator=vector_factory_generator
)
plot_resnorms(recycling_solver, '12 deflation vectors (3d)',
xlim=(0, 280), ylabel=None)
return recycling_solver
def run_2d_apriori():
'''Automatically choose deflation vectors with a priori bound.'''
numpy.random.seed(0)
def vector_factory_generator(_):
return krypy.recycling.factories.RitzFactory(
subsets_generator=krypy.recycling.generators.RitzSmall(
max_vectors=15, ),
subset_evaluator=krypy.recycling.evaluators.RitzApriori(
Bound=krypy.utils.BoundMinres, deflweight=2.),
#print_results='values'
)
recycling_solver = pynosh_helper.run(
'exp_pynosh_2d.vtu',
RecyclingSolver=DiagRecMinres,
vector_factory_generator=vector_factory_generator)
plot_resnorms(recycling_solver, 'quad bound + a priori strategy (2d)')
return recycling_solver
def run_2d_approx():
'''Automatically choose deflation vectors with approximate Krylov.'''
numpy.random.seed(0)
def vector_factory_generator(_):
return krypy.recycling.factories.RitzFactory(
subsets_generator=krypy.recycling.generators.RitzSmall(
max_vectors=15, ),
subset_evaluator=krypy.recycling.evaluators.RitzApproxKrylov(
mode='extrapolate', pseudospectra=False, deflweight=2.),
#print_results='values'
)
recycling_solver = pynosh_helper.run(
'exp_pynosh_2d.vtu',
RecyclingSolver=DiagRecMinres,
vector_factory_generator=vector_factory_generator)
plot_resnorms(recycling_solver, 'approximate Krylov strategy (2d)')
return recycling_solver