def coreProcess(self, meshctxt, subp):
subp.solver_mode = solvermode.AdvancedSolverMode(
time_stepper=staticstep.StaticDriver(),
nonlinear_solver=nonlinearsolver.NoNonlinearSolver(),
symmetric_solver=matrixmethod.ConjugateGradient(
preconditioner.ILUPreconditioner(),
1.e-5, # tolerance
1000 # max_iterations
),
asymmetric_solver=matrixmethod.BiConjugateGradient(
preconditioner.ILUPreconditioner(),
1.e-5, # tolerance
1000 # max_iterations
))
meshctxt.begin_writing()
try:
try:
evolve.evolve(meshctxt, 0.0)
except:
# TODO: Be more explicit about what exceptions
# should be handled here, to distinguish actual
# convergence failures from programming errors.
self.solver_converged = False
else:
self.solver_converged = True
finally:
meshctxt.end_writing()
def LU(self):
# Test the ILU code on dense matrices.
# A full identity matrix
matrix = loadMatrix('identity-full')
pcwrap = preconditioner.ILUPreconditioner()
pc = pcwrap.create_preconditioner(matrix)
unfact = pc.unfactored()
saveMatrix(unfact, 'matrix.out')
self.assert_(fp_file_compare('matrix.out',
os.path.join(datadir, 'identity-full'),
1.e-8))
# Non-trivial test: a dense asymmetric matrix.
matrix = loadMatrix('dense-asym-mat')
pcwrap = preconditioner.ILUPreconditioner()
pc = pcwrap.create_preconditioner(matrix)
unfact = pc.unfactored()
saveMatrix(unfact, 'matrix.out')
self.assert_(fp_file_compare('matrix.out',
os.path.join(datadir, 'ilu_unfactored'),
1.e-8))
# Check the preconditioner solutions...
for vecname in ('rhs2.1', 'rhs2.2', 'rhs2.3'):
vec = loadVector(vecname)
result = pc.solve(vec)
result.save('vector.out')
self.assert_(fp_file_compare('vector.out',
os.path.join(datadir,
vecname+'_ilu.out'),
1.e-8))
# A big(ger) dense matrix
matrix = loadMatrix('big.mat')
pcwrap = preconditioner.ILUPreconditioner()
pc = pcwrap.create_preconditioner(matrix)
unfact = pc.unfactored()
saveMatrix(unfact, 'matrix.out')
self.assert_(fp_file_compare('matrix.out',
os.path.join(datadir, 'big.mat'),
1.e-8))
saveMatrix(pc.lower(), 'matrix.out')
l_ok = fp_file_compare('matrix.out',
os.path.join(datadir, 'big_lower.out'),
1.e-8)
saveMatrix(pc.upper(), 'matrix.out')
u_ok = fp_file_compare('matrix.out',
os.path.join(datadir, 'big_upper.out'),
1.e-8)
self.assert_(l_ok and u_ok)
file_utils.remove('matrix.out')
file_utils.remove('vector.out')
def resolve_symmetric(self, existingSolver):
if (isinstance(existingSolver, ConjugateGradient)
and existingSolver.tolerance == self.tolerance
and existingSolver.max_iterations == self.max_iterations
and isinstance(existingSolver.preconditioner,
preconditioner.ILUPreconditioner)):
return existingSolver
return ConjugateGradient(
preconditioner=preconditioner.ILUPreconditioner(),
tolerance=self.tolerance,
max_iterations=self.max_iterations)
def resolve_asymmetric(self, subproblemcontext, existingSolver):
krylov_guess = 30; # Guess the krylov dimension for GMRES.
if (isinstance(existingSolver, GeneralizedMinResidual) and
existingSolver.tolerance == self.tolerance and
existingSolver.max_iterations == self.max_iterations and
existingSolver.krylov_dimension == krylov_guess and
isinstance(existingSolver.preconditioner,
preconditioner.ILUPreconditioner)):
return existingSolver
return GeneralizedMinResidual(
preconditioner=preconditioner.ILUPreconditioner(),
tolerance=self.tolerance,
max_iterations=self.max_iterations,
krylov_dimension=krylov_guess)
def coreProcess(self, meshctxt, subp):
subp.solver = staticstep.StaticDriver(
matrixmethod.ConjugateGradient(
preconditioner.ILUPreconditioner(),
1.e-5, # tolerance
1000 # max_iterations
))
subp.solver_linearity = linearity.Linear()
meshctxt.begin_writing()
try:
meshctxt.solver_precompute()
try:
evolve.evolve(meshctxt, 0.0, 0.0, False)
except:
# TODO 3.1: Be more explicit about what exceptions should
# be handled here, to distinguish actual convergence
# failures from programming errors.
self.solver_converged = False
else:
self.solver_converged = True
finally:
meshctxt.end_writing()
def ILU(self):
# Actual incomplete factorizations of sparse matrices.
# A sparse identity matrix
print >> sys.stderr, "identity"
matrix = loadMatrix('identity')
pcwrap = preconditioner.ILUPreconditioner()
pc = pcwrap.create_preconditioner(matrix)
unfact = pc.unfactored()
saveMatrix(unfact, 'matrix.out')
self.assert_(fp_file_compare('matrix.out',
os.path.join(datadir, 'identity'),
1.e-8))
# A sparse diagonal non-identity matrix.
print >> sys.stderr, "diag"
matrix = loadMatrix('diag')
pc = pcwrap.create_preconditioner(matrix)
unfact = pc.unfactored()
saveMatrix(unfact, 'matrix.out')
self.assert_(fp_file_compare('matrix.out',
os.path.join(datadir, 'diag'),
1.e-8))
# Non-trivial tests. The reference files were checked against
# SparseLib++'s ILU routine.
# A 4x4 sparse matrix.
print >> sys.stderr, "matrix1"
matrix = loadMatrix('matrix1')
pc = pcwrap.create_preconditioner(matrix)
saveMatrix(pc.lower(), 'matrix.out')
l_ok = fp_file_compare('matrix.out',
os.path.join(datadir, 'matrix1_lower.out'),
1.e-8)
saveMatrix(pc.upper(), 'matrix.out')
u_ok = fp_file_compare('matrix.out',
os.path.join(datadir, 'matrix1_upper.out'),
1.e-8)
self.assert_(l_ok and u_ok)
# A 10x10 sparse matrix with 62 entries.
print >> sys.stderr, "sparse10x10.mat"
matrix = loadMatrix('sparse10x10.mat')
pc = pcwrap.create_preconditioner(matrix)
saveMatrix(pc.lower(), 'matrix.out')
l_ok = fp_file_compare('matrix.out',
os.path.join(datadir, 'sparse10_lower.out'),
1.e-8)
saveMatrix(pc.upper(), 'matrix.out')
u_ok = fp_file_compare('matrix.out',
os.path.join(datadir, 'sparse10_upper.out'),
1.e-8)
self.assert_(l_ok and u_ok)
unfact = pc.unfactored()
saveMatrix(unfact, 'matrix.out')
# Check that the factoring *isn't* exact.
self.assert_(
not fp_file_compare('matrix.out',
os.path.join(datadir, 'sparse10x10.mat'),
1.e-8, quiet=True))
# A 50x50 sparse matrix with 157 entries.
print >> sys.stderr, "bigsparse2.mat"
matrix = loadMatrix('bigsparse2.mat')
pc = pcwrap.create_preconditioner(matrix)
saveMatrix(pc.lower(), 'matrix.out')
l_ok = fp_file_compare('matrix.out',
os.path.join(datadir, 'bigsparse2_lower.out'),
1.e-8)
saveMatrix(pc.upper(), 'matrix.out')
u_ok = fp_file_compare('matrix.out',
os.path.join(datadir, 'bigsparse2_upper.out'),
1.e-8)
self.assert_(l_ok and u_ok)
# A 50x50 sparse matrix with 297 entries.
print >> sys.stderr, "bigsparse.mat"
matrix = loadMatrix('bigsparse.mat')
pc = pcwrap.create_preconditioner(matrix)
saveMatrix(pc.lower(), 'matrix.out')
l_ok = fp_file_compare('matrix.out',
os.path.join(datadir, 'bigsparse_lower.out'),
1.e-8)
saveMatrix(pc.upper(), 'matrix.out')
u_ok = fp_file_compare('matrix.out',
os.path.join(datadir, 'bigsparse_upper.out'),
1.e-8)
self.assert_(l_ok and u_ok)
file_utils.remove('matrix.out')