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scipy.sparse.linalg.lgmres

# 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 `Ax = 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 (restart cycles). Iteration will stop after maxiter steps even if the specified tolerance has not been achieved.

  xtype : {‘f’,’d’,’F’,’D’}

  > This parameter is DEPRECATED — avoid using it.

  > The type of the result. If None, then it will be determined from A.dtype.char and b. If A does not have a typecode method then it will compute A.matvec(x0) to get a typecode. To save the extra computation when A does not have a typecode attribute use xtype=0 for the same type as b or use xtype=’f’,’d’,’F’,or ‘D’. This parameter has been superseded by LinearOperator.

  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.

  restrt : int, optional

  > DEPRECATED - use restart instead.

  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)