# scipy.sparse.linalg.gmres

scipy.sparse.linalg.gmres(A, b, x0=None, tol=1e-05, restart=None, maxiter=None, M=None, callback=None, restrt=None, atol=None, callback_type=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. Alternatively, `A` can be a linear operator which can produce `Ax` using, e.g., `scipy.sparse.linalg.LinearOperator`.

    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, atol float, optional

      Tolerances for convergence, `norm(residual) <= max(tol*norm(b), atol)`. The default for `atol` is `'legacy'`, which emulates a different legacy behavior.

      Warning

      The default value for atol will be changed in a future release. For future compatibility, specify atol explicitly.

    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.

    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(args), where args are selected by callback_type.

    callback_type{‘x’, ‘pr_norm’, ‘legacy’}, optional
      Callback function argument requested:

        - `x`: current iterate (ndarray), called on every restart

        - `pr_norm`: relative (preconditioned) residual norm (float), called on every inner iteration

        - `legacy` (default): same as `pr_norm`, but also changes the meaning of ‘maxiter’ to count inner iterations instead of restart cycles.

    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)

  Examples

      >>> from scipy.sparse import csc_matrix
      >>> from scipy.sparse.linalg import gmres
      >>> A = csc_matrix([[3, 2, 0], [1, -1, 0], [0, 5, 1]], dtype=float)
      >>> b = np.array([2, 4, -1], dtype=float)
      >>> x, exitCode = gmres(A, b)
      >>> print(exitCode)            # 0 indicates successful convergence
      0
      >>> np.allclose(A.dot(x), b)
      True

scipy.sparse.linalg.cgs scipy.sparse.linalg.lgmres