scipy.sparse.linalg.gmres

scipy.sparse.linalg.gmres(A, b, x0=None, tol=1e-05, restart=None, maxiter=None, xtype=None, M=None, callback=None, restrt=None)

Use Generalized Minimal RESidual iteration to solve A x = b.

Parameters :

A : {sparse matrix, dense matrix, LinearOperator}

The real or complex N-by-N matrix of the linear system.

b : {array, matrix}

Right hand side of the linear system. Has shape (N,) or (N,1).

Returns :

x : {array, matrix}

The converged solution.

info : int

Provides convergence information:

• 0 : successful exit
• >0 : convergence to tolerance not achieved, number of iterations
• <0 : illegal input or breakdown

Other Parameters:  

x0 : {array, matrix}

Starting guess for the solution (a vector of zeros by default).

tol : float

Tolerance to achieve. The algorithm terminates when either the relative or the absolute residual is below tol.

restart : int, optional

Number of iterations between restarts. Larger values increase iteration cost, but may be necessary for convergence. Default is 20.

maxiter : int, optional

Maximum number of iterations. Iteration will stop after maxiter steps even if the specified tolerance has not been achieved.

M : {sparse matrix, dense matrix, LinearOperator}

Inverse of the preconditioner of A. M should approximate the inverse of A and be easy to solve for (see Notes). Effective preconditioning dramatically improves the rate of convergence, which implies that fewer iterations are needed to reach a given error tolerance. By default, no preconditioner is used.

callback : function

User-supplied function to call after each iteration. It is called as callback(rk), where rk is the current residual vector.

See also

LinearOperator

Notes

A preconditioner, P, is chosen such that P is close to A but easy to solve for. The preconditioner parameter required by this routine is M = P^-1. The inverse should preferably not be calculated explicitly. Rather, use the following template to produce M:

# Construct a linear operator that computes P^-1 * x.
import scipy.sparse.linalg as spla
M_x = lambda x: spla.spsolve(P, x)
M = spla.LinearOperator((n, n), M_x)