(self, cycle='V')
return LinearOperator(shape, matvec, dtype=dtype)
Create a preconditioner using this multigrid cycle.

        Parameters
        ----------
        cycle : {'V','W','F','AMLI'}
            Type of multigrid cycle to perform in each iteration.

        Returns
        -------
        precond : LinearOperator
            Preconditioner suitable for the iterative solvers in defined in
            the scipy.sparse.linalg module (e.g. cg, gmres) and any other
            solver that uses the LinearOperator interface.  Refer to the
            LinearOperator documentation in scipy.sparse.linalg

        See Also
        --------
        MultilevelSolver.solve, scipy.sparse.linalg.LinearOperator

        Examples
        --------
        >>> from pyamg.aggregation import smoothed_aggregation_solver
        >>> from pyamg.gallery import poisson
        >>> from scipy.sparse.linalg import cg
        >>> import scipy as sp
        >>> A = poisson((100, 100), format='csr')          # matrix
        >>> b = np.random.rand(A.shape[0])                 # random RHS
        >>> ml = smoothed_aggregation_solver(A)            # AMG solver
        >>> M = ml.aspreconditioner(cycle='V')             # preconditioner
        >>> x, info = cg(A, b, tol=1e-8, maxiter=30, M=M)  # solve with CG
Create a preconditioner using this multigrid cycle.
Create a preconditioner using this multigrid cycle .
def aspreconditioner(self, cycle='V'):
        """Create a preconditioner using this multigrid cycle.

        Parameters
        ----------
        cycle : {'V','W','F','AMLI'}
            Type of multigrid cycle to perform in each iteration.

        Returns
        -------
        precond : LinearOperator
            Preconditioner suitable for the iterative solvers in defined in
            the scipy.sparse.linalg module (e.g. cg, gmres) and any other
            solver that uses the LinearOperator interface.  Refer to the
            LinearOperator documentation in scipy.sparse.linalg

        See Also
        --------
        MultilevelSolver.solve, scipy.sparse.linalg.LinearOperator

        Examples
        --------
        >>> from pyamg.aggregation import smoothed_aggregation_solver
        >>> from pyamg.gallery import poisson
        >>> from scipy.sparse.linalg import cg
        >>> import scipy as sp
        >>> A = poisson((100, 100), format='csr')          # matrix
        >>> b = np.random.rand(A.shape[0])                 # random RHS
        >>> ml = smoothed_aggregation_solver(A)            # AMG solver
        >>> M = ml.aspreconditioner(cycle='V')             # preconditioner
        >>> x, info = cg(A, b, tol=1e-8, maxiter=30, M=M)  # solve with CG

        """
        shape = self.levels[0].A.shape
        dtype = self.levels[0].A.dtype

        def matvec(b):
            return self.solve(b, maxiter=1, cycle=cycle, tol=1e-12)

        return LinearOperator(shape, matvec, dtype=dtype)
def aspreconditioner ( self , cycle = 'V' ) : shape = self . levels [ 0 ] . A . shape dtype = self . levels [ 0 ] . A . dtype def matvec ( b ) : return self . solve ( b , maxiter = 1 , cycle = cycle , tol = 1e-12 ) return LinearOperator ( shape , matvec , dtype = dtype )