def solve(self, x, b):
if not isinstance(x, function.Function):
raise TypeError("Provided solution is a '%s', not a Function" % type(x).__name__)
if not isinstance(b, function.Function):
raise TypeError("Provided RHS is a '%s', not a Function" % type(b).__name__)
if len(self._W) > 1 and self.nullspace is not None:
self.nullspace._apply(self._W.dof_dset.field_ises)
# User may have updated parameters
solving_utils.update_parameters(self, self.ksp)
if self.A.has_bcs:
b_bc = self._b
# rhs = b - action(A, zero_function_with_bcs_applied)
b_bc.assign(b - self._Abcs)
# Now we need to apply the boundary conditions to the "RHS"
for bc in self.A.bcs:
bc.apply(b_bc)
# don't want to write into b itself, because that would confuse user
b = b_bc
with b.dat.vec_ro as rhs:
with x.dat.vec as solution:
self.ksp.solve(rhs, solution)
r = self.ksp.getConvergedReason()
if r < 0:
raise RuntimeError("LinearSolver failed to converge after %d iterations with reason: %s", self.ksp.getIterationNumber(), solving_utils.KSPReasons[r])
def solve(self, x, b):
if not isinstance(x, function.Function):
raise TypeError("Provided solution is a '%s', not a Function" %
type(x).__name__)
if not isinstance(b, function.Function):
raise TypeError("Provided RHS is a '%s', not a Function" %
type(b).__name__)
if len(self._W) > 1 and self.nullspace is not None:
self.nullspace._apply(self._W.dof_dset.field_ises)
# User may have updated parameters
solving_utils.update_parameters(self, self.ksp)
if self.A.has_bcs:
b_bc = self._b
# rhs = b - action(A, zero_function_with_bcs_applied)
b_bc.assign(b - self._Abcs)
# Now we need to apply the boundary conditions to the "RHS"
for bc in self.A.bcs:
bc.apply(b_bc)
# don't want to write into b itself, because that would confuse user
b = b_bc
with b.dat.vec_ro as rhs:
with x.dat.vec as solution:
self.ksp.solve(rhs, solution)
r = self.ksp.getConvergedReason()
if r < 0:
raise RuntimeError(
"LinearSolver failed to converge after %d iterations with reason: %s",
self.ksp.getIterationNumber(), solving_utils.KSPReasons[r])
def solve(self):
dm = self.snes.getDM()
dm.setAppCtx(weakref.proxy(self._ctx))
dm.setCreateMatrix(self._ctx.create_matrix)
# Apply the boundary conditions to the initial guess.
for bc in self._problem.bcs:
bc.apply(self._problem.u)
# User might have updated parameters dict before calling
# solve, ensure these are passed through to the snes.
solving_utils.update_parameters(self, self.snes)
with self._problem.u.dat.vec as v:
self.snes.solve(None, v)
solving_utils.check_snes_convergence(self.snes)
def solve(self):
dm = self.snes.getDM()
dm.setAppCtx(weakref.proxy(self._ctx))
dm.setCreateMatrix(self._ctx.create_matrix)
# Apply the boundary conditions to the initial guess.
for bc in self._problem.bcs:
bc.apply(self._problem.u)
# User might have updated parameters dict before calling
# solve, ensure these are passed through to the snes.
solving_utils.update_parameters(self, self.snes)
with self._problem.u.dat.vec as v:
self.snes.solve(None, v)
solving_utils.check_snes_convergence(self.snes)
def __init__(self, *args, **kwargs):
"""
:arg problem: A :class:`LinearVariationalProblem` to solve.
:kwarg solver_parameters: Solver parameters to pass to PETSc.
This should be a dict mapping PETSc options to values.
:kwarg nullspace: an optional :class:`.VectorSpaceBasis` (or
:class:`.MixedVectorSpaceBasis`) spanning the null
space of the operator.
: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.
"""
super(LinearVariationalSolver, self).__init__(*args, **kwargs)
self.parameters.setdefault('snes_type', 'ksponly')
self.parameters.setdefault('ksp_rtol', 1.0e-7)
solving_utils.update_parameters(self, self.snes)
def __init__(self, *args, **kwargs):
"""
:arg problem: A :class:`LinearVariationalProblem` to solve.
:kwarg solver_parameters: Solver parameters to pass to PETSc.
This should be a dict mapping PETSc options to values.
:kwarg nullspace: an optional :class:`.VectorSpaceBasis` (or
:class:`.MixedVectorSpaceBasis`) spanning the null
space of the operator.
: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.
"""
super(LinearVariationalSolver, self).__init__(*args, **kwargs)
self.parameters.setdefault('snes_type', 'ksponly')
self.parameters.setdefault('ksp_rtol', 1.0e-7)
solving_utils.update_parameters(self, self.snes)
def parameters(self, val):
assert isinstance(val, dict), 'Must pass a dict to set parameters'
self._parameters = val
solving_utils.update_parameters(self, self.snes)
def parameters(self, val):
assert isinstance(val, dict), 'Must pass a dict to set parameters'
self._parameters = val
solving_utils.update_parameters(self, self.snes)