def configure_pmg(self, snes, pdm):
odm = snes.getDM()
psnes = PETSc.SNES().create(comm=snes.comm)
psnes.setOptionsPrefix(snes.getOptionsPrefix() + "pfas_")
psnes.setType("fas")
psnes.setDM(pdm)
psnes.incrementTabLevel(1, parent=snes)
(f, residual) = snes.getFunction()
assert residual is not None
(fun, args, kargs) = residual
psnes.setFunction(fun, f.duplicate(), args=args, kargs=kargs)
pdm.setGlobalVector(f.duplicate())
self.dummy = f.duplicate()
psnes.setSolution(f.duplicate())
# PETSc unfortunately requires us to make an ugly hack.
# We would like to use GMG for the coarse solve, at least
# sometimes. But PETSc will use this p-DM's getRefineLevels()
# instead of the getRefineLevels() of the MeshHierarchy to
# decide how many levels it should use for PCMG applied to
# the p-MG's coarse problem. So we need to set an option
# for the user, if they haven't already; I don't know any
# other way to get PETSc to know this at the right time.
opts = PETSc.Options(snes.getOptionsPrefix() + "pfas_")
if "fas_coarse_pc_mg_levels" not in opts:
opts["fas_coarse_pc_mg_levels"] = odm.getRefineLevel() + 1
if "fas_coarse_snes_fas_levels" not in opts:
opts["fas_coarse_snes_fas_levels"] = odm.getRefineLevel() + 1
return psnes
def __init__(self, problem, **kwargs):
r"""
:arg problem: A :class:`NonlinearVariationalProblem` to solve.
:kwarg nullspace: an optional :class:`.VectorSpaceBasis` (or
:class:`.MixedVectorSpaceBasis`) spanning the null
space of the operator.
:kwarg transpose_nullspace: as for the nullspace, but used to
make the right hand side consistent.
:kwarg near_nullspace: as for the nullspace, but used to
specify the near nullspace (for multigrid solvers).
:kwarg solver_parameters: Solver parameters to pass to PETSc.
This should be a dict mapping PETSc options to values.
:kwarg appctx: A dictionary containing application context that
is passed to the preconditioner if matrix-free.
:kwarg options_prefix: an optional prefix used to distinguish
PETSc options. If not provided a unique prefix will be
created. Use this option if you want to pass options
to the solver from the command line in addition to
through the ``solver_parameters`` dict.
:kwarg pre_jacobian_callback: A user-defined function that will
be called immediately before Jacobian assembly. This can
be used, for example, to update a coefficient function
that has a complicated dependence on the unknown solution.
:kwarg pre_function_callback: As above, but called immediately
before residual assembly
Example usage of the ``solver_parameters`` option: to set the
nonlinear solver type to just use a linear solver, use
.. code-block:: python
{'snes_type': 'ksponly'}
PETSc flag options (where the presence of the option means something) should
be specified with ``None``.
For example:
.. code-block:: python
{'snes_monitor': None}
To use the ``pre_jacobian_callback`` or ``pre_function_callback``
functionality, the user-defined function must accept the current
solution as a petsc4py Vec. Example usage is given below:
.. code-block:: python
def update_diffusivity(current_solution):
with cursol.dat.vec_wo as v:
current_solution.copy(v)
solve(trial*test*dx == dot(grad(cursol), grad(test))*dx, diffusivity)
solver = NonlinearVariationalSolver(problem,
pre_jacobian_callback=update_diffusivity)
"""
assert isinstance(problem, NonlinearVariationalProblem)
parameters = kwargs.get("solver_parameters")
if "parameters" in kwargs:
raise TypeError("Use solver_parameters, not parameters")
nullspace = kwargs.get("nullspace")
nullspace_T = kwargs.get("transpose_nullspace")
near_nullspace = kwargs.get("near_nullspace")
options_prefix = kwargs.get("options_prefix")
pre_j_callback = kwargs.get("pre_jacobian_callback")
pre_f_callback = kwargs.get("pre_function_callback")
super(NonlinearVariationalSolver,
self).__init__(parameters, options_prefix)
# Allow anything, interpret "matfree" as matrix_free.
mat_type = self.parameters.get("mat_type")
pmat_type = self.parameters.get("pmat_type")
matfree = mat_type == "matfree"
pmatfree = pmat_type == "matfree"
appctx = kwargs.get("appctx")
ctx = solving_utils._SNESContext(problem,
mat_type=mat_type,
pmat_type=pmat_type,
appctx=appctx,
pre_jacobian_callback=pre_j_callback,
pre_function_callback=pre_f_callback,
options_prefix=self.options_prefix)
# No preconditioner by default for matrix-free
if (problem.Jp is not None and pmatfree) or matfree:
self.set_default_parameter("pc_type", "none")
elif ctx.is_mixed:
# Mixed problem, use jacobi pc if user has not supplied
# one.
self.set_default_parameter("pc_type", "jacobi")
self.snes = PETSc.SNES().create(comm=problem.dm.comm)
self._problem = problem
self._ctx = ctx
self._work = problem.u.dof_dset.layout_vec.duplicate()
self.snes.setDM(problem.dm)
ctx.set_function(self.snes)
ctx.set_jacobian(self.snes)
ctx.set_nullspace(nullspace,
problem.J.arguments()[0].function_space()._ises,
transpose=False,
near=False)
ctx.set_nullspace(nullspace_T,
problem.J.arguments()[1].function_space()._ises,
transpose=True,
near=False)
ctx.set_nullspace(near_nullspace,
problem.J.arguments()[0].function_space()._ises,
transpose=False,
near=True)
ctx._nullspace = nullspace
ctx._nullspace_T = nullspace_T
ctx._near_nullspace = near_nullspace
# Set from options now, so that people who want to noodle with
# the snes object directly (mostly Patrick), can. We need the
# DM with an app context in place so that if the DM is active
# on a subKSP the context is available.
dm = self.snes.getDM()
with dmhooks.appctx(dm, self._ctx):
self.set_from_options(self.snes)
# Used for custom grid transfer.
self._transfer_operators = ()
self._setup = False
def __init__(self, problem, **kwargs):
"""
:arg problem: A :class:`NonlinearVariationalProblem` to solve.
:kwarg nullspace: an optional :class:`.VectorSpaceBasis` (or
:class:`.MixedVectorSpaceBasis`) spanning the null
space of the operator.
:kwarg solver_parameters: Solver parameters to pass to PETSc.
This should be a dict mapping PETSc options to values. For
example, to set the nonlinear solver type to just use a linear
solver:
:kwarg options_prefix: an optional prefix used to distinguish
PETSc options. If not provided a unique prefix will be
created. Use this option if you want to pass options
to the solver from the command line in addition to
through the ``solver_parameters`` dict.
.. code-block:: python
{'snes_type': 'ksponly'}
PETSc flag options should be specified with `bool` values. For example:
.. code-block:: python
{'snes_monitor': True}
"""
parameters, nullspace, options_prefix = solving_utils._extract_kwargs(
**kwargs)
# Do this first so __del__ doesn't barf horribly if we get an
# error in __init__
if options_prefix is not None:
self._opt_prefix = options_prefix
self._auto_prefix = False
else:
self._opt_prefix = 'firedrake_snes_%d_' % NonlinearVariationalSolver._id
self._auto_prefix = True
NonlinearVariationalSolver._id += 1
assert isinstance(problem, NonlinearVariationalProblem)
ctx = solving_utils._SNESContext(problem)
self.snes = PETSc.SNES().create()
self.snes.setOptionsPrefix(self._opt_prefix)
# Mixed problem, use jacobi pc if user has not supplied one.
if ctx.is_mixed:
parameters.setdefault('pc_type', 'jacobi')
# Allow command-line arguments to override dict parameters
opts = PETSc.Options()
for k, v in opts.getAll().iteritems():
if k.startswith(self._opt_prefix):
parameters[k[len(self._opt_prefix):]] = v
self._problem = problem
self._ctx = ctx
self.snes.setDM(problem.dm)
ctx.set_function(self.snes)
ctx.set_jacobian(self.snes)
ctx.set_nullspace(nullspace,
problem.J.arguments()[0].function_space()._ises)
self.parameters = parameters
def __init__(self, problem, **kwargs):
"""
Solve a :class:`NonlinearVariationalProblem` on a hierarchy of meshes.
:arg problem: A :class:`NonlinearVariationalProblem` or
iterable thereof (if specifying the problem on each level
by hand).
:kwarg nullspace: an optional :class:`.VectorSpaceBasis` (or
:class:`MixedVectorSpaceBasis`) spanning the null space of the
operator.
:kwarg solver_parameters: Solver parameters to pass to PETSc.
This should be a dict mapping PETSc options to values.
PETSc flag options should be specified with `bool`
values (:data:`True` for on, :data:`False` for off).
:kwarg options_prefix: an optional prefix used to distinguish
PETSc options. If not provided a unique prefix will be
created. Use this option if you want to pass options
to the solver from the command line in addition to
through the :data:`solver_parameters` dict.
.. note::
This solver is set up for use with geometric multigrid,
that is you can use :data:`"snes_type": "fas"` or
:data:`"pc_type": "mg"` transparently.
"""
# Do this first so __del__ doesn't barf horribly if we get an
# error in __init__
parameters, nullspace, options_prefix \
= firedrake.solving_utils._extract_kwargs(**kwargs)
if options_prefix is not None:
self._opt_prefix = options_prefix
self._auto_prefix = False
else:
self._opt_prefix = "firedrake_nlvsh_%d_" % NLVSHierarchy._id
self._auto_prefix = True
NLVSHierarchy._id += 1
if isinstance(problem, firedrake.NonlinearVariationalProblem):
# We just got a single problem so coarsen up the hierarchy
problems = []
while True:
if problem:
problems.append(problem)
else:
break
problem = coarsen_problem(problem)
problems.reverse()
else:
# User has provided list of problems
problems = problem
ctx = firedrake.solving_utils._SNESContext(problems)
if nullspace is not None:
raise NotImplementedError(
"Coarsening nullspaces no yet implemented")
snes = PETSc.SNES().create()
snes.setDM(problems[-1].dm)
self.problems = problems
self.snes = snes
self.ctx = ctx
self.ctx.set_function(self.snes)
self.ctx.set_jacobian(self.snes)
self.snes.setOptionsPrefix(self._opt_prefix)
# Allow command-line arguments to override dict parameters
opts = PETSc.Options()
for k, v in opts.getAll().iteritems():
if k.startswith(self._opt_prefix):
parameters[k[len(self._opt_prefix):]] = v
self.parameters = parameters
