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    FGMRES Arnoldi process, with optional projection or augmentation

    Parameters
    ----------
    matvec : callable
        Operation A*x
    v0 : ndarray
        Initial vector, normalized to nrm2(v0) == 1
    m : int
        Number of GMRES rounds
    atol : float
        Absolute tolerance for early exit
    lpsolve : callable
        Left preconditioner L
    rpsolve : callable
        Right preconditioner R
    cs : list of (ndarray, ndarray)
        Columns of matrices C and U in GCROT
    outer_v : list of ndarrays
        Augmentation vectors in LGMRES
    prepend_outer_v : bool, optional
        Whether augmentation vectors come before or after
        Krylov iterates

    Raises
    ------
    LinAlgError
        If nans encountered

    Returns
    -------
    Q, R : ndarray
        QR decomposition of the upper Hessenberg H=QR
    B : ndarray
        Projections corresponding to matrix C
    vs : list of ndarray
        Columns of matrix V
    zs : list of ndarray
        Columns of matrix Z
    y : ndarray
        Solution to ||H y - e_1||_2 = min!
    res : float
        The final (preconditioned) residual norm

    Nc                 S   s   | S Nr   xr   r   X/var/www/sunrise/env/lib/python3.9/site-packages/scipy/sparse/linalg/_isolve/_gcrotmk.pylpsolve@   s    z_fgmres.<locals>.lpsolvec                 S   s   | S r   r   r   r   r   r   rpsolveC   s    z_fgmres.<locals>.rpsolveaxpydotscalnrm2)dtype)   r   )r   r   Fr      r   ignore)ZoverdivideTFr   ordercol)whichZoverwrite_qruZcheck_finite)r   r   )r   npnanlenzerosr   ZonesZfinfoepsrangecopy	enumerateshapeZerrstateisfiniteappendr   absr   r   Zconj)"matvecZv0matolr   r   csZouter_vZprepend_outer_vr   r   r   r   vszsyresBQRr&   Z	breakdownjzwZw_normicalphaZhcurvZQ2ZR2_r   r   r   _fgmres   s~    1
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    Solve a matrix equation using flexible GCROT(m,k) algorithm.

    Parameters
    ----------
    A : {sparse matrix, ndarray, 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 : ndarray
        Right hand side of the linear system. Has shape (N,) or (N,1).
    x0 : ndarray
        Starting guess for the solution.
    tol, atol : float, optional
        Tolerances for convergence, ``norm(residual) <= max(tol*norm(b), atol)``.
        The default for ``atol`` is `tol`.

        .. warning::

           The default value for `atol` will be changed in a future release.
           For future compatibility, specify `atol` explicitly.
    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, ndarray, LinearOperator}, optional
        Preconditioner for A.  The preconditioner should approximate the
        inverse of A. gcrotmk is a 'flexible' algorithm and the preconditioner
        can vary from iteration to iteration. Effective preconditioning
        dramatically improves the rate of convergence, which implies that
        fewer iterations are needed to reach a given error tolerance.
    callback : function, optional
        User-supplied function to call after each iteration.  It is called
        as callback(xk), where xk is the current solution vector.
    m : int, optional
        Number of inner FGMRES iterations per each outer iteration.
        Default: 20
    k : int, optional
        Number of vectors to carry between inner FGMRES iterations.
        According to [2]_, good values are around m.
        Default: m
    CU : list of tuples, optional
        List of tuples ``(c, u)`` which contain the columns of the matrices
        C and U in the GCROT(m,k) algorithm. For details, see [2]_.
        The list given and vectors contained in it are modified in-place.
        If not given, start from empty matrices. The ``c`` elements in the
        tuples can be ``None``, in which case the vectors are recomputed
        via ``c = A u`` on start and orthogonalized as described in [3]_.
    discard_C : bool, optional
        Discard the C-vectors at the end. Useful if recycling Krylov subspaces
        for different linear systems.
    truncate : {'oldest', 'smallest'}, optional
        Truncation scheme to use. Drop: oldest vectors, or vectors with
        smallest singular values using the scheme discussed in [1,2].
        See [2]_ for detailed comparison.
        Default: 'oldest'

    Returns
    -------
    x : ndarray
        The solution found.
    info : int
        Provides convergence information:

        * 0  : successful exit
        * >0 : convergence to tolerance not achieved, number of iterations

    Examples
    --------
    >>> import numpy as np
    >>> from scipy.sparse import csc_matrix
    >>> from scipy.sparse.linalg import gcrotmk
    >>> R = np.random.randn(5, 5)
    >>> A = csc_matrix(R)
    >>> b = np.random.randn(5)
    >>> x, exit_code = gcrotmk(A, b)
    >>> print(exit_code)
    0
    >>> np.allclose(A.dot(x), b)
    True

    References
    ----------
    .. [1] E. de Sturler, ''Truncation strategies for optimal Krylov subspace
           methods'', SIAM J. Numer. Anal. 36, 864 (1999).
    .. [2] J.E. Hicken and D.W. Zingg, ''A simplified and flexible variant
           of GCROT for solving nonsymmetric linear systems'',
           SIAM J. Sci. Comput. 32, 172 (2010).
    .. [3] M.L. Parks, E. de Sturler, G. Mackey, D.D. Johnson, S. Maiti,
           ''Recycling Krylov subspaces for sequences of linear systems'',
           SIAM J. Sci. Comput. 28, 1651 (2006).

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<listcomp>?      zgcrotmk.<locals>.<listcomp>c                 S   s   | d d uS )Nr   r   )cur   r   r   <lambda>D  rM   zgcrotmk.<locals>.<lambda>)keyr   r   r   TZeconomic)Zoverwrite_amodeZpivotingg-q=)r   r   g      ?r   r   r   c                 S   s   g | ]\}}|qS r   r   rI   r   r   r   rL     rM   )r   r0   r1   rE   rF   c                 S   s   g | ]\}}d |fqS r   r   )rJ   czuzr   r   r   rL     rM   )!r	   r"   r+   all
ValueErrorwarningswarnDeprecationWarningr.   r(   r   sortemptyr*   r$   r   popr,   r   listTr'   r-   zipmaxrA   r   r   FloatingPointErrorZeroDivisionErrorr   r   r)   )AAbZx0ZtolmaxiterMcallbackr/   kZCUZ	discard_Ctruncater0   r   postprocessr.   Zpsolver   r   r   rr   Zb_normCusr9   r=   rK   r7   r8   Pr1   Znew_usr<   ZycZj_outerbetaZbeta_tolmlr6   r2   r3   r4   ZpresZuxr:   ZbyrN   ZbycZhyZcxr?   Zhycr>   gammaDWsigmaVZnew_CUr;   cupZwpcpZupr   r   r   r
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