# Sparse linear algebra (scipy.sparse.linalg)#

## Abstract linear operators#

 LinearOperator(*args, **kwargs) Common interface for performing matrix vector products Return A as a LinearOperator.

## Matrix Operations#

 Compute the inverse of a sparse matrix 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. matrix_power(A, power) Raise a square matrix to the integer power, power.

## Matrix norms#

 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 matrix.

## Solving linear problems#

Direct methods for linear equation systems:

 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 equation A x = b for x, assuming A is a triangular matrix. Return a function for solving a sparse linear system, with A pre-factorized. MatrixRankWarning use_solver(**kwargs) Select default sparse direct solver to be used.

Iterative methods for linear equation systems:

 bicg(A, b[, x0, rtol, atol, maxiter, M, ...]) Use BIConjugate Gradient iteration to solve Ax = b. bicgstab(A, b[, x0, rtol, atol, maxiter, M, ...]) Use BIConjugate Gradient STABilized iteration to solve Ax = b. cg(A, b[, x0, rtol, atol, maxiter, M, callback]) Use Conjugate Gradient iteration to solve Ax = b. cgs(A, b[, x0, rtol, atol, maxiter, M, callback]) Use Conjugate Gradient Squared iteration to solve Ax = b. gmres(A, b[, x0, rtol, atol, restart, ...]) Use Generalized Minimal RESidual iteration to solve Ax = b. lgmres(A, b[, x0, rtol, atol, maxiter, M, ...]) Solve a matrix equation using the LGMRES algorithm. minres(A, b[, x0, rtol, shift, maxiter, M, ...]) Use MINimum RESidual iteration to solve Ax=b qmr(A, b[, x0, rtol, atol, maxiter, M1, M2, ...]) Use Quasi-Minimal Residual iteration to solve Ax = b. gcrotmk(A, b[, x0, rtol, atol, maxiter, M, ...]) Solve a matrix equation using flexible GCROT(m,k) algorithm. tfqmr(A, b[, x0, rtol, atol, maxiter, M, ...]) Use Transpose-Free Quasi-Minimal Residual iteration to solve Ax = b.

Iterative methods for least-squares problems:

 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.

## Matrix factorizations#

Eigenvalue 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).

Singular values problems:

 svds(A[, k, ncv, tol, which, v0, maxiter, ...]) Partial singular value decomposition of a sparse matrix.

The svds function supports the following solvers:

Complete or incomplete LU factorizations

 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. LU factorization of a sparse matrix.

## Sparse arrays with structure#

 LaplacianNd(*args, **kwargs) The grid Laplacian in N dimensions and its eigenvalues/eigenvectors.

## Exceptions#

 ArpackNoConvergence(msg, eigenvalues, ...) ARPACK iteration did not converge ArpackError(info[, infodict]) ARPACK error