LinearOperator(*args, **kwargs)

Common interface for performing matrix vector products

aslinearoperator(A)

Return A as a LinearOperator.

inv(A)

Compute the inverse of a sparse arrays

expm(A)

Compute the matrix exponential using Pade approximation.

expm_multiply(A, B[, start, stop, num, ...])

Compute the action of the matrix exponential of A on B.

funm_multiply_krylov(f, A, b, *[, assume_a, ...])

A restarted Krylov method for evaluatingy=f(tA)b from[R4392c003a72c-1][R4392c003a72c-2].

matrix_power(A, power)

Raise a square matrix to the integer power,power.

norm(x[, ord, axis])

Norm of a sparse matrix

onenormest(A[, t, itmax, compute_v, compute_w])

Compute a lower bound of the 1-norm of a sparse array.

spsolve(A, b[, permc_spec, use_umfpack])

Solve the sparse linear system Ax=b, where b may be a vector or a matrix.

spsolve_triangular(A, b[, lower, ...])

Solve the equationAx=b forx, assuming A is a triangular matrix.

is_sptriangular(A)

Returns 2-tuple indicating lower/upper triangular structure for sparseA

spbandwidth(A)

Return the lower and upper bandwidth of a 2D numeric array.

factorized(A)

Return a function for solving a sparse linear system, with A pre-factorized.

MatrixRankWarning

Warning for exactly singular matrices.

use_solver(**kwargs)

Select default sparse direct solver to be used.

bicg(A, b[, x0, rtol, atol, maxiter, M, ...])

SolveAx=b with the BIConjugate Gradient method.

bicgstab(A, b[, x0, rtol, atol, maxiter, M, ...])

SolveAx=b with the BIConjugate Gradient STABilized method.

cg(A, b[, x0, rtol, atol, maxiter, M, callback])

SolveAx=b with the Conjugate Gradient method, for a symmetric, positive-definiteA.

cgs(A, b[, x0, rtol, atol, maxiter, M, callback])

SolveAx=b with the Conjugate Gradient Squared method.

gmres(A, b[, x0, rtol, atol, restart, ...])

SolveAx=b with the Generalized Minimal RESidual method.

lgmres(A, b[, x0, rtol, atol, maxiter, M, ...])

SolveAx=b with the LGMRES algorithm.

minres(A, b[, x0, rtol, shift, maxiter, M, ...])

SolveAx=b with the MINimum RESidual method, for a symmetricA.

qmr(A, b[, x0, rtol, atol, maxiter, M1, M2, ...])

SolveAx=b with the Quasi-Minimal Residual method.

gcrotmk(A, b[, x0, rtol, atol, maxiter, M, ...])

SolveAx=b with the flexible GCROT(m,k) algorithm.

tfqmr(A, b[, x0, rtol, atol, maxiter, M, ...])

SolveAx=b with the Transpose-Free Quasi-Minimal Residual method.

lsqr(A, b[, damp, atol, btol, conlim, ...])

Find the least-squares solution to a large, sparse, linear system of equations.

lsmr(A, b[, damp, atol, btol, conlim, ...])

Iterative solver for least-squares problems.

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.

lobpcg(A, X[, B, M, Y, tol, maxiter, ...])

Locally Optimal Block Preconditioned Conjugate Gradient Method (LOBPCG).

svds(A[, k, ncv, tol, which, v0, maxiter, ...])

Partial singular value decomposition of a sparse matrix.

splu(A[, permc_spec, diag_pivot_thresh, ...])

Compute the LU decomposition of a sparse, square matrix.

spilu(A[, drop_tol, fill_factor, drop_rule, ...])

Compute an incomplete LU decomposition for a sparse, square matrix.

SuperLU()

LU factorization of a sparse matrix.

LaplacianNd(*args, **kwargs)

The grid Laplacian inN dimensions and its eigenvalues/eigenvectors.

ArpackNoConvergence(msg, eigenvalues, ...)

ARPACK iteration did not converge

ArpackError(info[, infodict])

ARPACK error