def solve(self, var, b):
timer = backend.Timer("Matrix-free solver")
solver = backend.PETScKrylovSolver(*self.solver_parameters)
solver.parameters.update(self.parameters)
x = backend.Function(self.fn_space)
if b.data is None:
backend.info_red("Warning: got zero RHS for the solve associated with variable %s" % var)
return adjlinalg.Vector(x)
if isinstance(b.data, backend.Function):
rhs = b.data.vector().copy()
else:
rhs = backend.assemble(b.data)
if var.type in ['ADJ_TLM', 'ADJ_ADJOINT']:
self.bcs = [backend.homogenize(bc) for bc in self.bcs if isinstance(bc, backend.cpp.DirichletBC)] + [bc for bc in self.bcs if not isinstance(bc, backend.DirichletBC)]
for bc in self.bcs:
bc.apply(rhs)
if self.operators[1] is not None: # we have a user-supplied preconditioner
solver.set_operators(self.data, self.operators[1])
solver.solve(backend.down_cast(x.vector()), backend.down_cast(rhs))
else:
solver.solve(self.data, backend.down_cast(x.vector()), backend.down_cast(rhs))
timer.stop()
return adjlinalg.Vector(x)
def solve(self, var, b):
timer = backend.Timer("Matrix-free solver")
solver = backend.PETScKrylovSolver(*self.solver_parameters)
solver.parameters.update(self.parameters)
x = backend.Function(self.fn_space)
if b.data is None:
backend.info_red(
"Warning: got zero RHS for the solve associated with variable %s"
% var)
return adjlinalg.Vector(x)
if isinstance(b.data, backend.Function):
rhs = b.data.vector().copy()
else:
rhs = backend.assemble(b.data)
if var.type in ['ADJ_TLM', 'ADJ_ADJOINT']:
self.bcs = [
utils.homogenize(bc)
for bc in self.bcs if isinstance(bc, backend.cpp.DirichletBC)
] + [
bc
for bc in self.bcs if not isinstance(bc, backend.DirichletBC)
]
for bc in self.bcs:
bc.apply(rhs)
if self.operators[
1] is not None: # we have a user-supplied preconditioner
solver.set_operators(self.data, self.operators[1])
solver.solve(backend.down_cast(x.vector()), backend.down_cast(rhs))
else:
solver.solve(self.data, backend.down_cast(x.vector()),
backend.down_cast(rhs))
timer.stop()
return adjlinalg.Vector(x)
def down_cast(*args, **kwargs):
"""When a form is assembled, the information about its nonlinear dependencies is lost,
and it is no longer easy to manipulate. Therefore, dolfin_adjoint overloads the :py:func:`dolfin.down_cast`
function to *attach the form to the returned object*. This lets the automatic annotation work,
even when the user calls the lower-level :py:data:`solve(A, x, b)`.
"""
dc = backend.down_cast(*args, **kwargs)
if hasattr(args[0], 'form'):
dc.form = args[0].form
if hasattr(args[0], 'function'):
dc.function = args[0].function
if hasattr(args[0], 'bcs'):
dc.bcs = args[0].bcs
return dc
def down_cast(*args, **kwargs):
"""When a form is assembled, the information about its nonlinear dependencies is lost,
and it is no longer easy to manipulate. Therefore, dolfin_adjoint overloads the :py:func:`dolfin.down_cast`
function to *attach the form to the returned object*. This lets the automatic annotation work,
even when the user calls the lower-level :py:data:`solve(A, x, b)`.
"""
dc = backend.down_cast(*args, **kwargs)
if hasattr(args[0], 'form'):
dc.form = args[0].form
if hasattr(args[0], 'function'):
dc.function = args[0].function
if hasattr(args[0], 'bcs'):
dc.bcs = args[0].bcs
return dc
