def matvec(self, b):
'''Reduce'''
reshaped = []
for indices in self.mapping:
if len(indices) == 1:
reshaped.append(b.blocks[indices[0]])
else:
reshaped.append(PETScVector(as_petsc_nest(block_vec([b.blocks[idx] for idx in indices]))))
return block_vec(reshaped) if len(reshaped) > 1 else reshaped[0]
def mult(self, mat, x, y):
'''y = A*x'''
y *= 0
# Now x shall be comming as a nested vector
# Convert
x_bvec = block_vec(map(PETScVector, x.getNestSubVecs()))
# Apply
y_bvec = self.A * x_bvec
# Convert back
y.axpy(1., as_petsc_nest(y_bvec))
def matvec(self, b):
'''Reduce'''
# print type(b), b.size(), self.offsets
# assert b.size() == self.offsets[-1]
reduced = []
for f, l in zip(self.offsets[:-1], self.offsets[1:]):
if (l - f) == 1:
reduced.append(b[f])
else:
reduced.append(PETScVector(as_petsc_nest(block_vec(b.blocks[f:l]))))
return block_vec(reduced) if len(reduced) > 1 else reduced[0]
def multTranspose(self, mat, x, y):
'''y = A.T*x'''
AT = block_transpose(self.A)
y *= 0
# Now x shall be comming as a nested vector
# Convert
x_bvec = block_vec(list(map(PETScVector, x.getNestSubVecs())))
# Apply
y_bvec = AT*x_bvec
# Convert back
y.axpy(1., as_petsc_nest(y_bvec))
