def stop(iteration, x):
iterStatus = threshStatus = False
if iterations is not None:
iterStatus = (iteration >= iterations)
if threshold is not None:
norm = np.linalg.norm(getresidual(b, A, x, N))
threshStatus = (norm < threshold)
return iterStatus or threshStatus
def stop(iteration, x):
iterStatus = threshStatus = False
if iterations is not None:
iterStatus = (iteration >= iterations)
if threshold is not None:
norm = np.linalg.norm(getresidual(b, A, x, N))
threshStatus = (norm < threshold)
return iterStatus or threshStatus
def test_gs_thresh(self):
'''Test the Gauss-Siedel to-threshold solver.'''
NX = 12
NY = NX
N = NX * NY
A = operators.poisson((NX, NY,))
b = np.random.random((N,))
x_init = np.zeros((N,))
threshold = 0.0001
verbose = False
x = smoothToThreshold(A, b, x_init,
threshold,
verbose=verbose)
if self.verbose:
print 'norm is', (np.linalg.norm(tools.getresidual(b, A, x, N)))
def test_a(self):
'''
Poisson equation on a sine curve. In 3D?
It wasn't originally, but the restriciton algorithm was having
trouble with 1D. This might bear investigation.
'''
problemscale = 12
size = problemscale ** 3
gridLevels = 4
u_zeros = np.zeros((size,))
u_actual = np.sin(np.array(range(int(size))) * 3.0 / size).T
A = operators.poisson((size,))
b = tools.flexibleMmult(A, u_actual)
uSmoothed = smooth(A, b, u_zeros, iterations=1)
parameters = {'coarsestLevel': gridLevels - 1,
'problemShape': (problemscale, problemscale, problemscale),
'gridLevels': gridLevels,
'threshold': 8e-3,
}
u_mmg = mgSolve(A, b, parameters)
if self.verbose:
print 'norm is', (np.linalg.norm(tools.getresidual(b, A, uSmoothed.reshape((size, 1)), size)))
residual_norm = np.linalg.norm(tools.flexibleMmult(A, u_mmg) - b)
assert parameters['threshold'] > residual_norm
def mgCycle(A, b, level, R, parameters, initial=None):
"""
Internally used function that shows the actual multi-level solution method,
through a recursive call within the "level < coarsestLevel" block, below.
Parameters
----------
A : list of ndarrays
A list of square arrays; one for each level of resolution. As returned
by operators.coeffecientList().
b : ndarray
top-level RHS vector
level : int
The current multigrid level. This value is 0 at the entry point for standard,
recursive multigrid.
R : list of ndarrays
A list of (generally nonsquare) arrays; one for each transition between
levels of resolution. As returned by operators.restrictionList().
parameters : dict
A dictionary of parameters. See documentation for mgSolve() for details.
Optional Parameters
-------------------
initial=np.zeros((b.size, )) : ndarray
Initial iterate. Defaults to the zero vector, but the last supplied
solution should be used for chained v-cycles.
Returns
-------
uOut : ndarray
solution
infoDict
Dictionary of information about the solution process. Fields:
norm
The final norm of the residual
"""
verbose = parameters["verbose"]
if initial is None:
initial = np.zeros((b.size,))
N = b.size
# The general case is a recursive call. It comprises (1) pre-smoothing,
# (2) finding the coarse residual, (3) solving for the coarse correction
# to account for that residual via recursive call, (4) adding that
# correction to the pre-smoothed solution, (5) then possibly post-smoothing.
if level < parameters["coarsestLevel"]:
# (1) pre-smoothing
uApx = smooth(A[level], b, initial, parameters["preIterations"], verbose=verbose)
# (2) coarse residual
bCoarse = tools.flexibleMmult(R[level], b.reshape((N, 1)))
NH = len(bCoarse)
if verbose:
print level * " " + "calling mgCycle at level %i" % level
residual = tools.getresidual(b, A[level], uApx, N)
coarseResidual = tools.flexibleMmult(R[level], residual.reshape((N, 1))).reshape((NH,))
# (3) correction for residual via recursive call
coarseCorrection = mgCycle(A, coarseResidual, level + 1, R, parameters)[0]
correction = (tools.flexibleMmult(R[level].transpose(), coarseCorrection.reshape((NH, 1)))).reshape((N,))
if parameters["postIterations"] > 0:
# (5) post-smoothing
uOut = smooth(A[level], b, uApx + correction, parameters["postIterations"], verbose=verbose) # (4)
else:
uOut = uApx + correction # (4)
# Save norm, to monitor for convergence at topmost level.
norm = scipy.sparse.base.np.linalg.norm(tools.getresidual(b, A[level], uOut, N))
# The recursive calls only end when we're at the coarsest level, where
# we simply apply the chosen coarse solver.
else:
norm = 0
if verbose:
print level * " " + "direct solving at level %i" % level
uOut = coarseSolve(A[level], b.reshape((N, 1)))
return uOut, {"norm": norm}
def mgCycle(A, b, level, R, parameters, initial=None):
"""
Internally used function that shows the actual multi-level solution method,
through a recursive call within the "level < coarsestLevel" block, below.
Parameters
----------
A : list of ndarrays
A list of square arrays; one for each level of resolution. As returned
by operators.coeffecientList().
b : ndarray
top-level RHS vector
level : int
The current multigrid level. This value is 0 at the entry point for standard,
recursive multigrid.
R : list of ndarrays
A list of (generally nonsquare) arrays; one for each transition between
levels of resolution. As returned by operators.restrictionList().
parameters : dict
A dictionary of parameters. See documentation for mgSolve() for details.
Optional Parameters
-------------------
initial=np.zeros((b.size, )) : ndarray
Initial iterate. Defaults to the zero vector, but the last supplied
solution should be used for chained v-cycles.
Returns
-------
uOut : ndarray
solution
infoDict
Dictionary of information about the solution process. Fields:
norm
The final norm of the residual
"""
verbose = parameters['verbose']
if initial is None:
initial = np.zeros((b.size, ))
N = b.size
# The general case is a recursive call. It comprises (1) pre-smoothing,
# (2) finding the coarse residual, (3) solving for the coarse correction
# to account for that residual via recursive call, (4) adding that
# correction to the pre-smoothed solution, (5) then possibly post-smoothing.
if level < parameters['coarsestLevel']:
# (1) pre-smoothing
uApx = smooth(A[level], b, initial,
parameters['preIterations'], verbose=verbose)
# (2) coarse residual
bCoarse = tools.flexibleMmult(R[level], b.reshape((N, 1)))
NH = len(bCoarse)
if verbose: print level * " " + "calling mgCycle at level %i" % level
residual = tools.getresidual(b, A[level], uApx, N)
coarseResidual = tools.flexibleMmult(R[level], residual.reshape((N, 1))).reshape((NH,))
# (3) correction for residual via recursive call
coarseCorrection = mgCycle(A, coarseResidual, level+1, R, parameters)[0]
correction = (tools.flexibleMmult(R[level].transpose(), coarseCorrection.reshape((NH, 1)))).reshape((N, ))
if parameters['postIterations'] > 0:
# (5) post-smoothing
uOut = smooth(A[level],
b,
uApx + correction, # (4)
parameters['postIterations'],
verbose=verbose)
else:
uOut = uApx + correction # (4)
# Save norm, to monitor for convergence at topmost level.
norm = scipy.sparse.base.np.linalg.norm(tools.getresidual(b, A[level], uOut, N))
# The recursive calls only end when we're at the coarsest level, where
# we simply apply the chosen coarse solver.
else:
norm = 0
if verbose: print level * " " + "direct solving at level %i" % level
uOut = coarseSolve(A[level], b.reshape((N, 1)))
return uOut, {'norm': norm}
