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# scipy.sparse.linalg

## Sparse Linear Algebra

The submodules of sparse.linalg:
  1. eigen: sparse eigenvalue problem solvers
  2. isolve: iterative methods for solving linear systems
  3. dsolve: direct factorization methods for solving linear systems

## Examples

Functions

`aslinearoperator`(A) | Return A as a LinearOperator.
`bicg`(A, b[, x0, tol, maxiter, xtype, M, ...]) | Use BIConjugate Gradient iteration to solve A x = b
`bicgstab`(A, b[, x0, tol, maxiter, xtype, M, ...]) | Use BIConjugate Gradient STABilized iteration to solve A x = b
`cg`(A, b[, x0, tol, maxiter, xtype, M, callback]) | Use Conjugate Gradient iteration to solve A x = b
`cgs`(A, b[, x0, tol, maxiter, xtype, M, callback]) | Use Conjugate Gradient Squared iteration to solve A x = b
`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 A.
`factorized`(A) | Return a fuction for solving a sparse linear system, with A pre-factorized.
`gmres`(A, b[, x0, tol, restart, maxiter, ...]) | Use Generalized Minimal RESidual iteration to solve A x = b.
`isspmatrix`(x) |
`lgmres`(A, b[, x0, tol, maxiter, M, ...]) | Solve a matrix equation using the LGMRES algorithm.
`lobpcg`(A, X[, B, M, Y, tol, maxiter, ...]) | Solve symmetric partial eigenproblems with optional preconditioning
`lsqr`(A, b[, damp, atol, btol, conlim, ...]) | Find the least-squares solution to a large, sparse, linear system of equations.
`minres`(A, b[, x0, shift, tol, maxiter, ...]) | Use MINimum RESidual iteration to solve Ax=b
`qmr`(A, b[, x0, tol, maxiter, xtype, M1, M2, ...]) | Use Quasi-Minimal Residual iteration to solve A x = b
`spilu`(A[, drop_tol, fill_factor, drop_rule, ...]) | Compute an incomplete LU decomposition for a sparse, square matrix A.
`splu`(A[, permc_spec, diag_pivot_thresh, ...]) | Compute the LU decomposition of a sparse, square matrix.
`spsolve`(A, b[, permc_spec, use_umfpack]) | Solve the sparse linear system Ax=b
`svds`(A[, k, ncv, tol]) | Compute k singular values/vectors for a sparse matrix using ARPACK.
`use_solver`(**kwargs) | Valid keyword arguments with defaults (other ignored):

Classes

`LinearOperator`(shape, matvec[, rmatvec, ...]) | Common interface for performing matrix vector products
`Tester` | Nose test runner.
`csc_matrix`(arg1[, shape, dtype, copy]) | Compressed Sparse Column matrix
`csr_matrix`(arg1[, shape, dtype, copy]) | Compressed Sparse Row matrix

Exceptions

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

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