Linear algebra (sisl.linalg)

Although numpy and scipy provides a large set of linear algebra routines, sisl re-implements many of them with a reduced memory and/or computational effort. This is because numpy.linalg and scipy.linalg routines are defaulting to a large variety of checks to assert the input matrices.

sisl implements its own variants which has interfaces much like numpy and scipy.

inv(a[, overwrite_a])	Inverts a matrix
solve(a, b[, overwrite_a, overwrite_b])	Solve a linear system a x = b
eig	partial(func, *args, **keywords) - new function with partial application
eigh	partial(func, *args, **keywords) - new function with partial application
svd	partial(func, *args, **keywords) - new function with partial application
eigs(A[, k, M, sigma, which, v0, ncv, ...])	Find k eigenvalues and eigenvectors of the square matrix A.
eigsh(A[, k, M, sigma, which, v0, ncv, ...])	Find k eigenvalues and eigenvectors of the real symmetric square matrix or complex hermitian matrix A.

eigs(A[, k, M, sigma, which, v0, ncv, ...])	Find k eigenvalues and eigenvectors of the square matrix A.
eigsh(A[, k, M, sigma, which, v0, ncv, ...])	Find k eigenvalues and eigenvectors of the real symmetric square matrix or complex hermitian matrix A.
inv(a[, overwrite_a])	Inverts a matrix
solve(a, b[, overwrite_a, overwrite_b])	Solve a linear system a x = b