pyamg.util#

Utility functions. linalg.py provides some linear algebra functionality not yet found in scipy.

utils.py provides some utility functions for use with pyamg

bsr_utils.py provides utility functions for accessing and writing individual rows of BSR matrices

pyamg.util.make_system(A, M, x0, b)[source]#

Make a linear system Ax=b

Parameters:

ALinearOperator: sparse or dense matrix (or any valid input to aslinearoperator)
M{LinearOperator, Nones}: preconditioner sparse or dense matrix (or any valid input to aslinearoperator)
x0{array_like, str, None}: initial guess to iterative method. x0 = 'Mb' means using the nonzero initial guess M @ b. Default is None, which means using the zero initial guess.
barray_like: right hand side

Returns:

(A, M, x, b, postprocess)

ALinearOperator: matrix of the linear system
MLinearOperator: preconditioner
xrank 1 ndarray: initial guess
brank 1 ndarray: right hand side
postprocessfunction: converts the solution vector to the appropriate type and dimensions (e.g. (N,1) matrix)

pyamg.util.upcast(*args)[source]#

Returns the nearest supported sparse dtype for the combination of one or more types.

upcast(t0, t1, …, tn) -> T where T is a supported dtype

Examples

>>> from scipy.sparse._sputils import upcast
>>> upcast('int32')
<type 'numpy.int32'>
>>> upcast('bool')
<type 'numpy.bool_'>
>>> upcast('int32','float32')
<type 'numpy.float64'>
>>> upcast('bool',complex,float)
<type 'numpy.complex128'>