scipy.sparse.linalg.SuperLU¶

class scipy.sparse.linalg.SuperLU

LU factorization of a sparse matrix.

Factorization is represented as:

Pr * A * Pc = L * U


To construct these SuperLU objects, call the splu and spilu functions.

Notes

New in version 0.14.0.

Examples

The LU decomposition can be used to solve matrix equations. Consider:

>>> import numpy as np
>>> from scipy.sparse import csc_matrix, linalg as sla
>>> A = csc_matrix([[1,2,0,4],[1,0,0,1],[1,0,2,1],[2,2,1,0.]])


This can be solved for a given right-hand side:

>>> lu = sla.splu(A)
>>> b = np.array([1, 2, 3, 4])
>>> x = lu.solve(b)
>>> A.dot(x)
array([ 1.,  2.,  3.,  4.])


The lu object also contains an explicit representation of the decomposition. The permutations are represented as mappings of indices:

>>> lu.perm_r
array([0, 2, 1, 3], dtype=int32)
>>> lu.perm_c
array([2, 0, 1, 3], dtype=int32)


The L and U factors are sparse matrices in CSC format:

>>> lu.L.A
array([[ 1. ,  0. ,  0. ,  0. ],
[ 0. ,  1. ,  0. ,  0. ],
[ 0. ,  0. ,  1. ,  0. ],
[ 1. ,  0.5,  0.5,  1. ]])
>>> lu.U.A
array([[ 2.,  0.,  1.,  4.],
[ 0.,  2.,  1.,  1.],
[ 0.,  0.,  1.,  1.],
[ 0.,  0.,  0., -5.]])


The permutation matrices can be constructed:

>>> Pr = csc_matrix((4, 4))
>>> Pr[lu.perm_r, np.arange(4)] = 1
>>> Pc = csc_matrix((4, 4))
>>> Pc[np.arange(4), lu.perm_c] = 1


We can reassemble the original matrix:

>>> (Pr.T * (lu.L * lu.U) * Pc.T).A
array([[ 1.,  2.,  0.,  4.],
[ 1.,  0.,  0.,  1.],
[ 1.,  0.,  2.,  1.],
[ 2.,  2.,  1.,  0.]])

Attributes: shape Shape of the original matrix as a tuple of ints. nnz Number of nonzero elements in the matrix. perm_c Permutation Pc represented as an array of indices. perm_r Permutation Pr represented as an array of indices. L Lower triangular factor with unit diagonal as a scipy.sparse.csc_matrix. U Upper triangular factor as a scipy.sparse.csc_matrix.

Methods

 solve(rhs[, trans]) Solves linear system of equations with one or several right-hand sides.

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