r"""

Nonlinear solvers
-----------------

.. currentmodule:: scipy.optimize

This is a collection of general-purpose nonlinear multidimensional
solvers.  These solvers find *x* for which *F(x) = 0*. Both *x*
and *F* can be multidimensional.

Routines
~~~~~~~~

Large-scale nonlinear solvers:

.. autosummary::

   newton_krylov
   anderson

General nonlinear solvers:

.. autosummary::

   broyden1
   broyden2

Simple iterations:

.. autosummary::

   excitingmixing
   linearmixing
   diagbroyden


Examples
~~~~~~~~

**Small problem**

>>> def F(x):
...    return np.cos(x) + x[::-1] - [1, 2, 3, 4]
>>> import scipy.optimize
>>> x = scipy.optimize.broyden1(F, [1,1,1,1], f_tol=1e-14)
>>> x
array([ 4.04674914,  3.91158389,  2.71791677,  1.61756251])
>>> np.cos(x) + x[::-1]
array([ 1.,  2.,  3.,  4.])


**Large problem**

Suppose that we needed to solve the following integrodifferential
equation on the square :math:`[0,1]\times[0,1]`:

.. math::

   \nabla^2 P = 10 \left(\int_0^1\int_0^1\cosh(P)\,dx\,dy\right)^2

with :math:`P(x,1) = 1` and :math:`P=0` elsewhere on the boundary of
the square.

The solution can be found using the `newton_krylov` solver:

.. plot::

   import numpy as np
   from scipy.optimize import newton_krylov
   from numpy import cosh, zeros_like, mgrid, zeros

   # parameters
   nx, ny = 75, 75
   hx, hy = 1./(nx-1), 1./(ny-1)

   P_left, P_right = 0, 0
   P_top, P_bottom = 1, 0

   def residual(P):
       d2x = zeros_like(P)
       d2y = zeros_like(P)

       d2x[1:-1] = (P[2:]   - 2*P[1:-1] + P[:-2]) / hx/hx
       d2x[0]    = (P[1]    - 2*P[0]    + P_left)/hx/hx
       d2x[-1]   = (P_right - 2*P[-1]   + P[-2])/hx/hx

       d2y[:,1:-1] = (P[:,2:] - 2*P[:,1:-1] + P[:,:-2])/hy/hy
       d2y[:,0]    = (P[:,1]  - 2*P[:,0]    + P_bottom)/hy/hy
       d2y[:,-1]   = (P_top   - 2*P[:,-1]   + P[:,-2])/hy/hy

       return d2x + d2y - 10*cosh(P).mean()**2

   # solve
   guess = zeros((nx, ny), float)
   sol = newton_krylov(residual, guess, method='lgmres', verbose=1)
   print('Residual: %g' % abs(residual(sol)).max())

   # visualize
   import matplotlib.pyplot as plt
   x, y = mgrid[0:1:(nx*1j), 0:1:(ny*1j)]
   plt.pcolor(x, y, sol)
   plt.colorbar()
   plt.show()

"""
# Copyright (C) 2009, Pauli Virtanen <pav@iki.fi>
# Distributed under the same license as SciPy.

from __future__ import division, print_function, absolute_import

import sys
import numpy as np
from scipy._lib.six import callable, exec_, xrange
from scipy.linalg import norm, solve, inv, qr, svd, LinAlgError
from numpy import asarray, dot, vdot
import scipy.sparse.linalg
import scipy.sparse
from scipy.linalg import get_blas_funcs
import inspect
from scipy._lib._util import getargspec_no_self as _getargspec
from .linesearch import scalar_search_wolfe1, scalar_search_armijo


__all__ = [
    'broyden1', 'broyden2', 'anderson', 'linearmixing',
    'diagbroyden', 'excitingmixing', 'newton_krylov']

#------------------------------------------------------------------------------
# Utility functions
#------------------------------------------------------------------------------


class NoConvergence(Exception):
    pass


def maxnorm(x):
    return np.absolute(x).max()


def _as_inexact(x):
    """Return `x` as an array, of either floats or complex floats"""
    x = asarray(x)
    if not np.issubdtype(x.dtype, np.inexact):
        return asarray(x, dtype=np.float_)
    return x


def _array_like(x, x0):
    """Return ndarray `x` as same array subclass and shape as `x0`"""
    x = np.reshape(x, np.shape(x0))
    wrap = getattr(x0, '__array_wrap__', x.__array_wrap__)
    return wrap(x)


def _safe_norm(v):
    if not np.isfinite(v).all():
        return np.array(np.inf)
    return norm(v)

#------------------------------------------------------------------------------
# Generic nonlinear solver machinery
#------------------------------------------------------------------------------


_doc_parts = dict(
    params_basic="""
    F : function(x) -> f
        Function whose root to find; should take and return an array-like
        object.
    xin : array_like
        Initial guess for the solution
    """.strip(),
    params_extra="""
    iter : int, optional
        Number of iterations to make. If omitted (default), make as many
        as required to meet tolerances.
    verbose : bool, optional
        Print status to stdout on every iteration.
    maxiter : int, optional
        Maximum number of iterations to make. If more are needed to
        meet convergence, `NoConvergence` is raised.
    f_tol : float, optional
        Absolute tolerance (in max-norm) for the residual.
        If omitted, default is 6e-6.
    f_rtol : float, optional
        Relative tolerance for the residual. If omitted, not used.
    x_tol : float, optional
        Absolute minimum step size, as determined from the Jacobian
        approximation. If the step size is smaller than this, optimization
        is terminated as successful. If omitted, not used.
    x_rtol : float, optional
        Relative minimum step size. If omitted, not used.
    tol_norm : function(vector) -> scalar, optional
        Norm to use in convergence check. Default is the maximum norm.
    line_search : {None, 'armijo' (default), 'wolfe'}, optional
        Which type of a line search to use to determine the step size in the
        direction given by the Jacobian approximation. Defaults to 'armijo'.
    callback : function, optional
        Optional callback function. It is called on every iteration as
        ``callback(x, f)`` where `x` is the current solution and `f`
        the corresponding residual.

    Returns
    -------
    sol : ndarray
        An array (of similar array type as `x0`) containing the final solution.

    Raises
    ------
    NoConvergence
        When a solution was not found.

    """.strip()
)


def _set_doc(obj):
    if obj.__doc__:
        obj.__doc__ = obj.__doc__ % _doc_parts


def nonlin_solve(F, x0, jacobian='krylov', iter=None, verbose=False,
                 maxiter=None, f_tol=None, f_rtol=None, x_tol=None, x_rtol=None,
                 tol_norm=None, line_search='armijo', callback=None,
                 full_output=False, raise_exception=True):
    """
    Find a root of a function, in a way suitable for large-scale problems.

    Parameters
    ----------
    %(params_basic)s
    jacobian : Jacobian
        A Jacobian approximation: `Jacobian` object or something that
        `asjacobian` can transform to one. Alternatively, a string specifying
        which of the builtin Jacobian approximations to use:

            krylov, broyden1, broyden2, anderson
            diagbroyden, linearmixing, excitingmixing

    %(params_extra)s
    full_output : bool
        If true, returns a dictionary `info` containing convergence
        information.
    raise_exception : bool
        If True, a `NoConvergence` exception is raise if no solution is found.

    See Also
    --------
    asjacobian, Jacobian

    Notes
    -----
    This algorithm implements the inexact Newton method, with
    backtracking or full line searches. Several Jacobian
    approximations are available, including Krylov and Quasi-Newton
    methods.

    References
    ----------
    .. [KIM] C. T. Kelley, \"Iterative Methods for Linear and Nonlinear
       Equations\". Society for Industrial and Applied Mathematics. (1995)
       https://archive.siam.org/books/kelley/fr16/

    """
    # Can't use default parameters because it's being explicitly passed as None
    # from the calling function, so we need to set it here.
    tol_norm = maxnorm if tol_norm is None else tol_norm
    condition = TerminationCondition(f_tol=f_tol, f_rtol=f_rtol,
                                     x_tol=x_tol, x_rtol=x_rtol,
                                     iter=iter, norm=tol_norm)

    x0 = _as_inexact(x0)
    func = lambda z: _as_inexact(F(_array_like(z, x0))).flatten()
    x = x0.flatten()

    dx = np.inf
    Fx = func(x)
    Fx_norm = norm(Fx)

    jacobian = asjacobian(jacobian)
    jacobian.setup(x.copy(), Fx, func)

    if maxiter is None:
        if iter is not None:
            maxiter = iter + 1
        else:
            maxiter = 100*(x.size+1)

    if line_search is True:
        line_search = 'armijo'
    elif line_search is False:
        line_search = None

    if line_search not in (None, 'armijo', 'wolfe'):
        raise ValueError("Invalid line search")

    # Solver tolerance selection
    gamma = 0.9
    eta_max = 0.9999
    eta_treshold = 0.1
    eta = 1e-3

    for n in xrange(maxiter):
        status = condition.check(Fx, x, dx)
        if status:
            break

        # The tolerance, as computed for scipy.sparse.linalg.* routines
        tol = min(eta, eta*Fx_norm)
        dx = -jacobian.solve(Fx, tol=tol)

        if norm(dx) == 0:
            raise ValueError("Jacobian inversion yielded zero vector. "
                             "This indicates a bug in the Jacobian "
                             "approximation.")

        # Line search, or Newton step
        if line_search:
            s, x, Fx, Fx_norm_new = _nonlin_line_search(func, x, Fx, dx,
                                                        line_search)
        else:
            s = 1.0
            x = x + dx
            Fx = func(x)
            Fx_norm_new = norm(Fx)

        jacobian.update(x.copy(), Fx)

        if callback:
            callback(x, Fx)

        # Adjust forcing parameters for inexact methods
        eta_A = gamma * Fx_norm_new**2 / Fx_norm**2
        if gamma * eta**2 < eta_treshold:
            eta = min(eta_max, eta_A)
        else:
            eta = min(eta_max, max(eta_A, gamma*eta**2))

        Fx_norm = Fx_norm_new

        # Print status
        if verbose:
            sys.stdout.write("%d:  |F(x)| = %g; step %g\n" % (
                n, tol_norm(Fx), s))
            sys.stdout.flush()
    else:
        if raise_exception:
            raise NoConvergence(_array_like(x, x0))
        else:
            status = 2

    if full_output:
        info = {'nit': condition.iteration,
                'fun': Fx,
                'status': status,
                'success': status == 1,
                'message': {1: 'A solution was found at the specified '
                               'tolerance.',
                            2: 'The maximum number of iterations allowed '
                               'has been reached.'
                            }[status]
                }
        return _array_like(x, x0), info
    else:
        return _array_like(x, x0)


_set_doc(nonlin_solve)


def _nonlin_line_search(func, x, Fx, dx, search_type='armijo', rdiff=1e-8,
                        smin=1e-2):
    tmp_s = [0]
    tmp_Fx = [Fx]
    tmp_phi = [norm(Fx)**2]
    s_norm = norm(x) / norm(dx)

    def phi(s, store=True):
        if s == tmp_s[0]:
            return tmp_phi[0]
        xt = x + s*dx
        v = func(xt)
        p = _safe_norm(v)**2
        if store:
            tmp_s[0] = s
            tmp_phi[0] = p
            tmp_Fx[0] = v
        return p

    def derphi(s):
        ds = (abs(s) + s_norm + 1) * rdiff
        return (phi(s+ds, store=False) - phi(s)) / ds

    if search_type == 'wolfe':
        s, phi1, phi0 = scalar_search_wolfe1(phi, derphi, tmp_phi[0],
                                             xtol=1e-2, amin=smin)
    elif search_type == 'armijo':
        s, phi1 = scalar_search_armijo(phi, tmp_phi[0], -tmp_phi[0],
                                       amin=smin)

    if s is None:
        # XXX: No suitable step length found. Take the full Newton step,
        #      and hope for the best.
        s = 1.0

    x = x + s*dx
    if s == tmp_s[0]:
        Fx = tmp_Fx[0]
    else:
        Fx = func(x)
    Fx_norm = norm(Fx)

    return s, x, Fx, Fx_norm


class TerminationCondition(object):
    """
    Termination condition for an iteration. It is terminated if

    - |F| < f_rtol*|F_0|, AND
    - |F| < f_tol

    AND

    - |dx| < x_rtol*|x|, AND
    - |dx| < x_tol

    """
    def __init__(self, f_tol=None, f_rtol=None, x_tol=None, x_rtol=None,
                 iter=None, norm=maxnorm):

        if f_tol is None:
            f_tol = np.finfo(np.float_).eps ** (1./3)
        if f_rtol is None:
            f_rtol = np.inf
        if x_tol is None:
            x_tol = np.inf
        if x_rtol is None:
            x_rtol = np.inf

        self.x_tol = x_tol
        self.x_rtol = x_rtol
        self.f_tol = f_tol
        self.f_rtol = f_rtol

        self.norm = norm

        self.iter = iter

        self.f0_norm = None
        self.iteration = 0

    def check(self, f, x, dx):
        self.iteration += 1
        f_norm = self.norm(f)
        x_norm = self.norm(x)
        dx_norm = self.norm(dx)

        if self.f0_norm is None:
            self.f0_norm = f_norm

        if f_norm == 0:
            return 1

        if self.iter is not None:
            # backwards compatibility with SciPy 0.6.0
            return 2 * (self.iteration > self.iter)

        # NB: condition must succeed for rtol=inf even if norm == 0
        return int((f_norm <= self.f_tol
                    and f_norm/self.f_rtol <= self.f0_norm)
                   and (dx_norm <= self.x_tol
                        and dx_norm/self.x_rtol <= x_norm))


#------------------------------------------------------------------------------
# Generic Jacobian approximation
#------------------------------------------------------------------------------

class Jacobian(object):
    """
    Common interface for Jacobians or Jacobian approximations.

    The optional methods come useful when implementing trust region
    etc.  algorithms that often require evaluating transposes of the
    Jacobian.

    Methods
    -------
    solve
        Returns J^-1 * v
    update
        Updates Jacobian to point `x` (where the function has residual `Fx`)

    matvec : optional
        Returns J * v
    rmatvec : optional
        Returns A^H * v
    rsolve : optional
        Returns A^-H * v
    matmat : optional
        Returns A * V, where V is a dense matrix with dimensions (N,K).
    todense : optional
        Form the dense Jacobian matrix. Necessary for dense trust region
        algorithms, and useful for testing.

    Attributes
    ----------
    shape
        Matrix dimensions (M, N)
    dtype
        Data type of the matrix.
    func : callable, optional
        Function the Jacobian corresponds to

    """

    def __init__(self, **kw):
        names = ["solve", "update", "matvec", "rmatvec", "rsolve",
                 "matmat", "todense", "shape", "dtype"]
        for name, value in kw.items():
            if name not in names:
                raise ValueError("Unknown keyword argument %s" % name)
            if value is not None:
                setattr(self, name, kw[name])

        if hasattr(self, 'todense'):
            self.__array__ = lambda: self.todense()

    def aspreconditioner(self):
        return InverseJacobian(self)

    def solve(self, v, tol=0):
        raise NotImplementedError

    def update(self, x, F):
        pass

    def setup(self, x, F, func):
        self.func = func
        self.shape = (F.size, x.size)
        self.dtype = F.dtype
        if self.__class__.setup is Jacobian.setup:
            # Call on the first point unless overridden
            self.update(x, F)


class InverseJacobian(object):
    def __init__(self, jacobian):
        self.jacobian = jacobian
        self.matvec = jacobian.solve
        self.update = jacobian.update
        if hasattr(jacobian, 'setup'):
            self.setup = jacobian.setup
        if hasattr(jacobian, 'rsolve'):
            self.rmatvec = jacobian.rsolve

    @property
    def shape(self):
        return self.jacobian.shape

    @property
    def dtype(self):
        return self.jacobian.dtype


def asjacobian(J):
    """
    Convert given object to one suitable for use as a Jacobian.
    """
    spsolve = scipy.sparse.linalg.spsolve
    if isinstance(J, Jacobian):
        return J
    elif inspect.isclass(J) and issubclass(J, Jacobian):
        return J()
    elif isinstance(J, np.ndarray):
        if J.ndim > 2:
            raise ValueError('array must have rank <= 2')
        J = np.atleast_2d(np.asarray(J))
        if J.shape[0] != J.shape[1]:
            raise ValueError('array must be square')

        return Jacobian(matvec=lambda v: dot(J, v),
                        rmatvec=lambda v: dot(J.conj().T, v),
                        solve=lambda v: solve(J, v),
                        rsolve=lambda v: solve(J.conj().T, v),
                        dtype=J.dtype, shape=J.shape)
    elif scipy.sparse.isspmatrix(J):
        if J.shape[0] != J.shape[1]:
            raise ValueError('matrix must be square')
        return Jacobian(matvec=lambda v: J*v,
                        rmatvec=lambda v: J.conj().T * v,
                        solve=lambda v: spsolve(J, v),
                        rsolve=lambda v: spsolve(J.conj().T, v),
                        dtype=J.dtype, shape=J.shape)
    elif hasattr(J, 'shape') and hasattr(J, 'dtype') and hasattr(J, 'solve'):
        return Jacobian(matvec=getattr(J, 'matvec'),
                        rmatvec=getattr(J, 'rmatvec'),
                        solve=J.solve,
                        rsolve=getattr(J, 'rsolve'),
                        update=getattr(J, 'update'),
                        setup=getattr(J, 'setup'),
                        dtype=J.dtype,
                        shape=J.shape)
    elif callable(J):
        # Assume it's a function J(x) that returns the Jacobian
        class Jac(Jacobian):
            def update(self, x, F):
                self.x = x

            def solve(self, v, tol=0):
                m = J(self.x)
                if isinstance(m, np.ndarray):
                    return solve(m, v)
                elif scipy.sparse.isspmatrix(m):
                    return spsolve(m, v)
                else:
                    raise ValueError("Unknown matrix type")

            def matvec(self, v):
                m = J(self.x)
                if isinstance(m, np.ndarray):
                    return dot(m, v)
                elif scipy.sparse.isspmatrix(m):
                    return m*v
                else:
                    raise ValueError("Unknown matrix type")

            def rsolve(self, v, tol=0):
                m = J(self.x)
                if isinstance(m, np.ndarray):
                    return solve(m.conj().T, v)
                elif scipy.sparse.isspmatrix(m):
                    return spsolve(m.conj().T, v)
                else:
                    raise ValueError("Unknown matrix type")

            def rmatvec(self, v):
                m = J(self.x)
                if isinstance(m, np.ndarray):
                    return dot(m.conj().T, v)
                elif scipy.sparse.isspmatrix(m):
                    return m.conj().T * v
                else:
                    raise ValueError("Unknown matrix type")
        return Jac()
    elif isinstance(J, str):
        return dict(broyden1=BroydenFirst,
                    broyden2=BroydenSecond,
                    anderson=Anderson,
                    diagbroyden=DiagBroyden,
                    linearmixing=LinearMixing,
                    excitingmixing=ExcitingMixing,
                    krylov=KrylovJacobian)[J]()
    else:
        raise TypeError('Cannot convert object to a Jacobian')


#------------------------------------------------------------------------------
# Broyden
#------------------------------------------------------------------------------

class GenericBroyden(Jacobian):
    def setup(self, x0, f0, func):
        Jacobian.setup(self, x0, f0, func)
        self.last_f = f0
        self.last_x = x0

        if hasattr(self, 'alpha') and self.alpha is None:
            # Autoscale the initial Jacobian parameter
            # unless we have already guessed the solution.
            normf0 = norm(f0)
            if normf0:
                self.alpha = 0.5*max(norm(x0), 1) / normf0
            else:
                self.alpha = 1.0

    def _update(self, x, f, dx, df, dx_norm, df_norm):
        raise NotImplementedError

    def update(self, x, f):
        df = f - self.last_f
        dx = x - self.last_x
        self._update(x, f, dx, df, norm(dx), norm(df))
        self.last_f = f
        self.last_x = x


class LowRankMatrix(object):
    r"""
    A matrix represented as

    .. math:: \alpha I + \sum_{n=0}^{n=M} c_n d_n^\dagger

    However, if the rank of the matrix reaches the dimension of the vectors,
    full matrix representation will be used thereon.

    """

    def __init__(self, alpha, n, dtype):
        self.alpha = alpha
        self.cs = []
        self.ds = []
        self.n = n
        self.dtype = dtype
        self.collapsed = None

    @staticmethod
    def _matvec(v, alpha, cs, ds):
        axpy, scal, dotc = get_blas_funcs(['axpy', 'scal', 'dotc'],
                                          cs[:1] + [v])
        w = alpha * v
        for c, d in zip(cs, ds):
            a = dotc(d, v)
            w = axpy(c, w, w.size, a)
        return w

    @staticmethod
    def _solve(v, alpha, cs, ds):
        """Evaluate w = M^-1 v"""
        if len(cs) == 0:
            return v/alpha

        # (B + C D^H)^-1 = B^-1 - B^-1 C (I + D^H B^-1 C)^-1 D^H B^-1

        axpy, dotc = get_blas_funcs(['axpy', 'dotc'], cs[:1] + [v])

        c0 = cs[0]
        A = alpha * np.identity(len(cs), dtype=c0.dtype)
        for i, d in enumerate(ds):
            for j, c in enumerate(cs):
                A[i,j] += dotc(d, c)

        q = np.zeros(len(cs), dtype=c0.dtype)
        for j, d in enumerate(ds):
            q[j] = dotc(d, v)
        q /= alpha
        q = solve(A, q)

        w = v/alpha
        for c, qc in zip(cs, q):
            w = axpy(c, w, w.size, -qc)

        return w

    def matvec(self, v):
        """Evaluate w = M v"""
        if self.collapsed is not None:
            return np.dot(self.collapsed, v)
        return LowRankMatrix._matvec(v, self.alpha, self.cs, self.ds)

    def rmatvec(self, v):
        """Evaluate w = M^H v"""
        if self.collapsed is not None:
            return np.dot(self.collapsed.T.conj(), v)
        return LowRankMatrix._matvec(v, np.conj(self.alpha), self.ds, self.cs)

    def solve(self, v, tol=0):
        """Evaluate w = M^-1 v"""
        if self.collapsed is not None:
            return solve(self.collapsed, v)
        return LowRankMatrix._solve(v, self.alpha, self.cs, self.ds)

    def rsolve(self, v, tol=0):
        """Evaluate w = M^-H v"""
        if self.collapsed is not None:
            return solve(self.collapsed.T.conj(), v)
        return LowRankMatrix._solve(v, np.conj(self.alpha), self.ds, self.cs)

    def append(self, c, d):
        if self.collapsed is not None:
            self.collapsed += c[:,None] * d[None,:].conj()
            return

        self.cs.append(c)
        self.ds.append(d)

        if len(self.cs) > c.size:
            self.collapse()

    def __array__(self):
        if self.collapsed is not None:
            return self.collapsed

        Gm = self.alpha*np.identity(self.n, dtype=self.dtype)
        for c, d in zip(self.cs, self.ds):
            Gm += c[:,None]*d[None,:].conj()
        return Gm

    def collapse(self):
        """Collapse the low-rank matrix to a full-rank one."""
        self.collapsed = np.array(self)
        self.cs = None
        self.ds = None
        self.alpha = None

    def restart_reduce(self, rank):
        """
        Reduce the rank of the matrix by dropping all vectors.
        """
        if self.collapsed is not None:
            return
        assert rank > 0
        if len(self.cs) > rank:
            del self.cs[:]
            del self.ds[:]

    def simple_reduce(self, rank):
        """
        Reduce the rank of the matrix by dropping oldest vectors.
        """
        if self.collapsed is not None:
            return
        assert rank > 0
        while len(self.cs) > rank:
            del self.cs[0]
            del self.ds[0]

    def svd_reduce(self, max_rank, to_retain=None):
        """
        Reduce the rank of the matrix by retaining some SVD components.

        This corresponds to the \"Broyden Rank Reduction Inverse\"
        algorithm described in [1]_.

        Note that the SVD decomposition can be done by solving only a
        problem whose size is the effective rank of this matrix, which
        is viable even for large problems.

        Parameters
        ----------
        max_rank : int
            Maximum rank of this matrix after reduction.
        to_retain : int, optional
            Number of SVD components to retain when reduction is done
            (ie. rank > max_rank). Default is ``max_rank - 2``.

        References
        ----------
        .. [1] B.A. van der Rotten, PhD thesis,
           \"A limited memory Broyden method to solve high-dimensional
           systems of nonlinear equations\". Mathematisch Instituut,
           Universiteit Leiden, The Netherlands (2003).

           https://web.archive.org/web/20161022015821/http://www.math.leidenuniv.nl/scripties/Rotten.pdf

        """
        if self.collapsed is not None:
            return

        p = max_rank
        if to_retain is not None:
            q = to_retain
        else:
            q = p - 2

        if self.cs:
            p = min(p, len(self.cs[0]))
        q = max(0, min(q, p-1))

        m = len(self.cs)
        if m < p:
            # nothing to do
            return

        C = np.array(self.cs).T
        D = np.array(self.ds).T

        D, R = qr(D, mode='economic')
        C = dot(C, R.T.conj())

        U, S, WH = svd(C, full_matrices=False, compute_uv=True)

        C = dot(C, inv(WH))
        D = dot(D, WH.T.conj())

        for k in xrange(q):
            self.cs[k] = C[:,k].copy()
            self.ds[k] = D[:,k].copy()

        del self.cs[q:]
        del self.ds[q:]


_doc_parts['broyden_params'] = """
    alpha : float, optional
        Initial guess for the Jacobian is ``(-1/alpha)``.
    reduction_method : str or tuple, optional
        Method used in ensuring that the rank of the Broyden matrix
        stays low. Can either be a string giving the name of the method,
        or a tuple of the form ``(method, param1, param2, ...)``
        that gives the name of the method and values for additional parameters.

        Methods available:

            - ``restart``: drop all matrix columns. Has no extra parameters.
            - ``simple``: drop oldest matrix column. Has no extra parameters.
            - ``svd``: keep only the most significant SVD components.
              Takes an extra parameter, ``to_retain``, which determines the
              number of SVD components to retain when rank reduction is done.
              Default is ``max_rank - 2``.

    max_rank : int, optional
        Maximum rank for the Broyden matrix.
        Default is infinity (ie., no rank reduction).
    """.strip()


class BroydenFirst(GenericBroyden):
    r"""
    Find a root of a function, using Broyden's first Jacobian approximation.

    This method is also known as \"Broyden's good method\".

    Parameters
    ----------
    %(params_basic)s
    %(broyden_params)s
    %(params_extra)s

    See Also
    --------
    root : Interface to root finding algorithms for multivariate
           functions. See ``method=='broyden1'`` in particular.

    Notes
    -----
    This algorithm implements the inverse Jacobian Quasi-Newton update

    .. math:: H_+ = H + (dx - H df) dx^\dagger H / ( dx^\dagger H df)

    which corresponds to Broyden's first Jacobian update

    .. math:: J_+ = J + (df - J dx) dx^\dagger / dx^\dagger dx


    References
    ----------
    .. [1] B.A. van der Rotten, PhD thesis,
       \"A limited memory Broyden method to solve high-dimensional
       systems of nonlinear equations\". Mathematisch Instituut,
       Universiteit Leiden, The Netherlands (2003).

       https://web.archive.org/web/20161022015821/http://www.math.leidenuniv.nl/scripties/Rotten.pdf

    """

    def __init__(self, alpha=None, reduction_method='restart', max_rank=None):
        GenericBroyden.__init__(self)
        self.alpha = alpha
        self.Gm = None

        if max_rank is None:
            max_rank = np.inf
        self.max_rank = max_rank

        if isinstance(reduction_method, str):
            reduce_params = ()
        else:
            reduce_params = reduction_method[1:]
            reduction_method = reduction_method[0]
        reduce_params = (max_rank - 1,) + reduce_params

        if reduction_method == 'svd':
            self._reduce = lambda: self.Gm.svd_reduce(*reduce_params)
        elif reduction_method == 'simple':
            self._reduce = lambda: self.Gm.simple_reduce(*reduce_params)
        elif reduction_method == 'restart':
            self._reduce = lambda: self.Gm.restart_reduce(*reduce_params)
        else:
            raise ValueError("Unknown rank reduction method '%s'" %
                             reduction_method)

    def setup(self, x, F, func):
        GenericBroyden.setup(self, x, F, func)
        self.Gm = LowRankMatrix(-self.alpha, self.shape[0], self.dtype)

    def todense(self):
        return inv(self.Gm)

    def solve(self, f, tol=0):
        r = self.Gm.matvec(f)
        if not np.isfinite(r).all():
            # singular; reset the Jacobian approximation
            self.setup(self.last_x, self.last_f, self.func)
        return self.Gm.matvec(f)

    def matvec(self, f):
        return self.Gm.solve(f)

    def rsolve(self, f, tol=0):
        return self.Gm.rmatvec(f)

    def rmatvec(self, f):
        return self.Gm.rsolve(f)

    def _update(self, x, f, dx, df, dx_norm, df_norm):
        self._reduce()  # reduce first to preserve secant condition

        v = self.Gm.rmatvec(dx)
        c = dx - self.Gm.matvec(df)
        d = v / vdot(df, v)

        self.Gm.append(c, d)


class BroydenSecond(BroydenFirst):
    """
    Find a root of a function, using Broyden\'s second Jacobian approximation.

    This method is also known as \"Broyden's bad method\".

    Parameters
    ----------
    %(params_basic)s
    %(broyden_params)s
    %(params_extra)s

    See Also
    --------
    root : Interface to root finding algorithms for multivariate
           functions. See ``method=='broyden2'`` in particular.

    Notes
    -----
    This algorithm implements the inverse Jacobian Quasi-Newton update

    .. math:: H_+ = H + (dx - H df) df^\\dagger / ( df^\\dagger df)

    corresponding to Broyden's second method.

    References
    ----------
    .. [1] B.A. van der Rotten, PhD thesis,
       \"A limited memory Broyden method to solve high-dimensional
       systems of nonlinear equations\". Mathematisch Instituut,
       Universiteit Leiden, The Netherlands (2003).

       https://web.archive.org/web/20161022015821/http://www.math.leidenuniv.nl/scripties/Rotten.pdf

    """

    def _update(self, x, f, dx, df, dx_norm, df_norm):
        self._reduce()  # reduce first to preserve secant condition

        v = df
        c = dx - self.Gm.matvec(df)
        d = v / df_norm**2
        self.Gm.append(c, d)


#------------------------------------------------------------------------------
# Broyden-like (restricted memory)
#------------------------------------------------------------------------------

class Anderson(GenericBroyden):
    """
    Find a root of a function, using (extended) Anderson mixing.

    The Jacobian is formed by for a 'best' solution in the space
    spanned by last `M` vectors. As a result, only a MxM matrix
    inversions and MxN multiplications are required. [Ey]_

    Parameters
    ----------
    %(params_basic)s
    alpha : float, optional
        Initial guess for the Jacobian is (-1/alpha).
    M : float, optional
        Number of previous vectors to retain. Defaults to 5.
    w0 : float, optional
        Regularization parameter for numerical stability.
        Compared to unity, good values of the order of 0.01.
    %(params_extra)s

    See Also
    --------
    root : Interface to root finding algorithms for multivariate
           functions. See ``method=='anderson'`` in particular.

    References
    ----------
    .. [Ey] V. Eyert, J. Comp. Phys., 124, 271 (1996).

    """

    # Note:
    #
    # Anderson method maintains a rank M approximation of the inverse Jacobian,
    #
    #     J^-1 v ~ -v*alpha + (dX + alpha dF) A^-1 dF^H v
    #     A      = W + dF^H dF
    #     W      = w0^2 diag(dF^H dF)
    #
    # so that for w0 = 0 the secant condition applies for last M iterates, ie.,
    #
    #     J^-1 df_j = dx_j
    #
    # for all j = 0 ... M-1.
    #
    # Moreover, (from Sherman-Morrison-Woodbury formula)
    #
    #    J v ~ [ b I - b^2 C (I + b dF^H A^-1 C)^-1 dF^H ] v
    #    C   = (dX + alpha dF) A^-1
    #    b   = -1/alpha
    #
    # and after simplification
    #
    #    J v ~ -v/alpha + (dX/alpha + dF) (dF^H dX - alpha W)^-1 dF^H v
    #

    def __init__(self, alpha=None, w0=0.01, M=5):
        GenericBroyden.__init__(self)
        self.alpha = alpha
        self.M = M
        self.dx = []
        self.df = []
        self.gamma = None
        self.w0 = w0

    def solve(self, f, tol=0):
        dx = -self.alpha*f

        n = len(self.dx)
        if n == 0:
            return dx

        df_f = np.empty(n, dtype=f.dtype)
        for k in xrange(n):
            df_f[k] = vdot(self.df[k], f)

        try:
            gamma = solve(self.a, df_f)
        except LinAlgError:
            # singular; reset the Jacobian approximation
            del self.dx[:]
            del self.df[:]
            return dx

        for m in xrange(n):
            dx += gamma[m]*(self.dx[m] + self.alpha*self.df[m])
        return dx

    def matvec(self, f):
        dx = -f/self.alpha

        n = len(self.dx)
        if n == 0:
            return dx

        df_f = np.empty(n, dtype=f.dtype)
        for k in xrange(n):
            df_f[k] = vdot(self.df[k], f)

        b = np.empty((n, n), dtype=f.dtype)
        for i in xrange(n):
            for j in xrange(n):
                b[i,j] = vdot(self.df[i], self.dx[j])
                if i == j and self.w0 != 0:
                    b[i,j] -= vdot(self.df[i], self.df[i])*self.w0**2*self.alpha
        gamma = solve(b, df_f)

        for m in xrange(n):
            dx += gamma[m]*(self.df[m] + self.dx[m]/self.alpha)
        return dx

    def _update(self, x, f, dx, df, dx_norm, df_norm):
        if self.M == 0:
            return

        self.dx.append(dx)
        self.df.append(df)

        while len(self.dx) > self.M:
            self.dx.pop(0)
            self.df.pop(0)

        n = len(self.dx)
        a = np.zeros((n, n), dtype=f.dtype)

        for i in xrange(n):
            for j in xrange(i, n):
                if i == j:
                    wd = self.w0**2
                else:
                    wd = 0
                a[i,j] = (1+wd)*vdot(self.df[i], self.df[j])

        a += np.triu(a, 1).T.conj()
        self.a = a

#------------------------------------------------------------------------------
# Simple iterations
#------------------------------------------------------------------------------


class DiagBroyden(GenericBroyden):
    """
    Find a root of a function, using diagonal Broyden Jacobian approximation.

    The Jacobian approximation is derived from previous iterations, by
    retaining only the diagonal of Broyden matrices.

    .. warning::

       This algorithm may be useful for specific problems, but whether
       it will work may depend strongly on the problem.

    Parameters
    ----------
    %(params_basic)s
    alpha : float, optional
        Initial guess for the Jacobian is (-1/alpha).
    %(params_extra)s

    See Also
    --------
    root : Interface to root finding algorithms for multivariate
           functions. See ``method=='diagbroyden'`` in particular.
    """

    def __init__(self, alpha=None):
        GenericBroyden.__init__(self)
        self.alpha = alpha

    def setup(self, x, F, func):
        GenericBroyden.setup(self, x, F, func)
        self.d = np.full((self.shape[0],), 1 / self.alpha, dtype=self.dtype) 

    def solve(self, f, tol=0):
        return -f / self.d

    def matvec(self, f):
        return -f * self.d

    def rsolve(self, f, tol=0):
        return -f / self.d.conj()

    def rmatvec(self, f):
        return -f * self.d.conj()

    def todense(self):
        return np.diag(-self.d)

    def _update(self, x, f, dx, df, dx_norm, df_norm):
        self.d -= (df + self.d*dx)*dx/dx_norm**2


class LinearMixing(GenericBroyden):
    """
    Find a root of a function, using a scalar Jacobian approximation.

    .. warning::

       This algorithm may be useful for specific problems, but whether
       it will work may depend strongly on the problem.

    Parameters
    ----------
    %(params_basic)s
    alpha : float, optional
        The Jacobian approximation is (-1/alpha).
    %(params_extra)s

    See Also
    --------
    root : Interface to root finding algorithms for multivariate
           functions. See ``method=='linearmixing'`` in particular.

    """

    def __init__(self, alpha=None):
        GenericBroyden.__init__(self)
        self.alpha = alpha

    def solve(self, f, tol=0):
        return -f*self.alpha

    def matvec(self, f):
        return -f/self.alpha

    def rsolve(self, f, tol=0):
        return -f*np.conj(self.alpha)

    def rmatvec(self, f):
        return -f/np.conj(self.alpha)

    def todense(self):
        return np.diag(np.full(self.shape[0], -1/self.alpha))

    def _update(self, x, f, dx, df, dx_norm, df_norm):
        pass


class ExcitingMixing(GenericBroyden):
    """
    Find a root of a function, using a tuned diagonal Jacobian approximation.

    The Jacobian matrix is diagonal and is tuned on each iteration.

    .. warning::

       This algorithm may be useful for specific problems, but whether
       it will work may depend strongly on the problem.

    See Also
    --------
    root : Interface to root finding algorithms for multivariate
           functions. See ``method=='excitingmixing'`` in particular.

    Parameters
    ----------
    %(params_basic)s
    alpha : float, optional
        Initial Jacobian approximation is (-1/alpha).
    alphamax : float, optional
        The entries of the diagonal Jacobian are kept in the range
        ``[alpha, alphamax]``.
    %(params_extra)s
    """

    def __init__(self, alpha=None, alphamax=1.0):
        GenericBroyden.__init__(self)
        self.alpha = alpha
        self.alphamax = alphamax
        self.beta = None

    def setup(self, x, F, func):
        GenericBroyden.setup(self, x, F, func)
        self.beta = np.full((self.shape[0],), self.alpha, dtype=self.dtype)

    def solve(self, f, tol=0):
        return -f*self.beta

    def matvec(self, f):
        return -f/self.beta

    def rsolve(self, f, tol=0):
        return -f*self.beta.conj()

    def rmatvec(self, f):
        return -f/self.beta.conj()

    def todense(self):
        return np.diag(-1/self.beta)

    def _update(self, x, f, dx, df, dx_norm, df_norm):
        incr = f*self.last_f > 0
        self.beta[incr] += self.alpha
        self.beta[~incr] = self.alpha
        np.clip(self.beta, 0, self.alphamax, out=self.beta)


#------------------------------------------------------------------------------
# Iterative/Krylov approximated Jacobians
#------------------------------------------------------------------------------

class KrylovJacobian(Jacobian):
    r"""
    Find a root of a function, using Krylov approximation for inverse Jacobian.

    This method is suitable for solving large-scale problems.

    Parameters
    ----------
    %(params_basic)s
    rdiff : float, optional
        Relative step size to use in numerical differentiation.
    method : {'lgmres', 'gmres', 'bicgstab', 'cgs', 'minres'} or function
        Krylov method to use to approximate the Jacobian.
        Can be a string, or a function implementing the same interface as
        the iterative solvers in `scipy.sparse.linalg`.

        The default is `scipy.sparse.linalg.lgmres`.
    inner_M : LinearOperator or InverseJacobian
        Preconditioner for the inner Krylov iteration.
        Note that you can use also inverse Jacobians as (adaptive)
        preconditioners. For example,

        >>> from scipy.optimize.nonlin import BroydenFirst, KrylovJacobian
        >>> from scipy.optimize.nonlin import InverseJacobian
        >>> jac = BroydenFirst()
        >>> kjac = KrylovJacobian(inner_M=InverseJacobian(jac))

        If the preconditioner has a method named 'update', it will be called
        as ``update(x, f)`` after each nonlinear step, with ``x`` giving
        the current point, and ``f`` the current function value.
    inner_tol, inner_maxiter, ...
        Parameters to pass on to the \"inner\" Krylov solver.
        See `scipy.sparse.linalg.gmres` for details.
    outer_k : int, optional
        Size of the subspace kept across LGMRES nonlinear iterations.
        See `scipy.sparse.linalg.lgmres` for details.
    %(params_extra)s

    See Also
    --------
    root : Interface to root finding algorithms for multivariate
           functions. See ``method=='krylov'`` in particular.
    scipy.sparse.linalg.gmres
    scipy.sparse.linalg.lgmres

    Notes
    -----
    This function implements a Newton-Krylov solver. The basic idea is
    to compute the inverse of the Jacobian with an iterative Krylov
    method. These methods require only evaluating the Jacobian-vector
    products, which are conveniently approximated by a finite difference:

    .. math:: J v \approx (f(x + \omega*v/|v|) - f(x)) / \omega

    Due to the use of iterative matrix inverses, these methods can
    deal with large nonlinear problems.

    SciPy's `scipy.sparse.linalg` module offers a selection of Krylov
    solvers to choose from. The default here is `lgmres`, which is a
    variant of restarted GMRES iteration that reuses some of the
    information obtained in the previous Newton steps to invert
    Jacobians in subsequent steps.

    For a review on Newton-Krylov methods, see for example [1]_,
    and for the LGMRES sparse inverse method, see [2]_.

    References
    ----------
    .. [1] D.A. Knoll and D.E. Keyes, J. Comp. Phys. 193, 357 (2004).
           :doi:`10.1016/j.jcp.2003.08.010`
    .. [2] A.H. Baker and E.R. Jessup and T. Manteuffel,
           SIAM J. Matrix Anal. Appl. 26, 962 (2005).
           :doi:`10.1137/S0895479803422014`

    """

    def __init__(self, rdiff=None, method='lgmres', inner_maxiter=20,
                 inner_M=None, outer_k=10, **kw):
        self.preconditioner = inner_M
        self.rdiff = rdiff
        self.method = dict(
            bicgstab=scipy.sparse.linalg.bicgstab,
            gmres=scipy.sparse.linalg.gmres,
            lgmres=scipy.sparse.linalg.lgmres,
            cgs=scipy.sparse.linalg.cgs,
            minres=scipy.sparse.linalg.minres,
            ).get(method, method)

        self.method_kw = dict(maxiter=inner_maxiter, M=self.preconditioner)

        if self.method is scipy.sparse.linalg.gmres:
            # Replace GMRES's outer iteration with Newton steps
            self.method_kw['restrt'] = inner_maxiter
            self.method_kw['maxiter'] = 1
            self.method_kw.setdefault('atol', 0)
        elif self.method is scipy.sparse.linalg.gcrotmk:
            self.method_kw.setdefault('atol', 0)
        elif self.method is scipy.sparse.linalg.lgmres:
            self.method_kw['outer_k'] = outer_k
            # Replace LGMRES's outer iteration with Newton steps
            self.method_kw['maxiter'] = 1
            # Carry LGMRES's `outer_v` vectors across nonlinear iterations
            self.method_kw.setdefault('outer_v', [])
            self.method_kw.setdefault('prepend_outer_v', True)
            # But don't carry the corresponding Jacobian*v products, in case
            # the Jacobian changes a lot in the nonlinear step
            #
            # XXX: some trust-region inspired ideas might be more efficient...
            #      See eg. Brown & Saad. But needs to be implemented separately
            #      since it's not an inexact Newton method.
            self.method_kw.setdefault('store_outer_Av', False)
            self.method_kw.setdefault('atol', 0)

        for key, value in kw.items():
            if not key.startswith('inner_'):
                raise ValueError("Unknown parameter %s" % key)
            self.method_kw[key[6:]] = value

    def _update_diff_step(self):
        mx = abs(self.x0).max()
        mf = abs(self.f0).max()
        self.omega = self.rdiff * max(1, mx) / max(1, mf)

    def matvec(self, v):
        nv = norm(v)
        if nv == 0:
            return 0*v
        sc = self.omega / nv
        r = (self.func(self.x0 + sc*v) - self.f0) / sc
        if not np.all(np.isfinite(r)) and np.all(np.isfinite(v)):
            raise ValueError('Function returned non-finite results')
        return r

    def solve(self, rhs, tol=0):
        if 'tol' in self.method_kw:
            sol, info = self.method(self.op, rhs, **self.method_kw)
        else:
            sol, info = self.method(self.op, rhs, tol=tol, **self.method_kw)
        return sol

    def update(self, x, f):
        self.x0 = x
        self.f0 = f
        self._update_diff_step()

        # Update also the preconditioner, if possible
        if self.preconditioner is not None:
            if hasattr(self.preconditioner, 'update'):
                self.preconditioner.update(x, f)

    def setup(self, x, f, func):
        Jacobian.setup(self, x, f, func)
        self.x0 = x
        self.f0 = f
        self.op = scipy.sparse.linalg.aslinearoperator(self)

        if self.rdiff is None:
            self.rdiff = np.finfo(x.dtype).eps ** (1./2)

        self._update_diff_step()

        # Setup also the preconditioner, if possible
        if self.preconditioner is not None:
            if hasattr(self.preconditioner, 'setup'):
                self.preconditioner.setup(x, f, func)


#------------------------------------------------------------------------------
# Wrapper functions
#------------------------------------------------------------------------------

def _nonlin_wrapper(name, jac):
    """
    Construct a solver wrapper with given name and jacobian approx.

    It inspects the keyword arguments of ``jac.__init__``, and allows to
    use the same arguments in the wrapper function, in addition to the
    keyword arguments of `nonlin_solve`

    """
    args, varargs, varkw, defaults = _getargspec(jac.__init__)
    kwargs = list(zip(args[-len(defaults):], defaults))
    kw_str = ", ".join(["%s=%r" % (k, v) for k, v in kwargs])
    if kw_str:
        kw_str = ", " + kw_str
    kwkw_str = ", ".join(["%s=%s" % (k, k) for k, v in kwargs])
    if kwkw_str:
        kwkw_str = kwkw_str + ", "

    # Construct the wrapper function so that its keyword arguments
    # are visible in pydoc.help etc.
    wrapper = """
def %(name)s(F, xin, iter=None %(kw)s, verbose=False, maxiter=None,
             f_tol=None, f_rtol=None, x_tol=None, x_rtol=None,
             tol_norm=None, line_search='armijo', callback=None, **kw):
    jac = %(jac)s(%(kwkw)s **kw)
    return nonlin_solve(F, xin, jac, iter, verbose, maxiter,
                        f_tol, f_rtol, x_tol, x_rtol, tol_norm, line_search,
                        callback)
"""

    wrapper = wrapper % dict(name=name, kw=kw_str, jac=jac.__name__,
                             kwkw=kwkw_str)
    ns = {}
    ns.update(globals())
    exec_(wrapper, ns)
    func = ns[name]
    func.__doc__ = jac.__doc__
    _set_doc(func)
    return func


broyden1 = _nonlin_wrapper('broyden1', BroydenFirst)
broyden2 = _nonlin_wrapper('broyden2', BroydenSecond)
anderson = _nonlin_wrapper('anderson', Anderson)
linearmixing = _nonlin_wrapper('linearmixing', LinearMixing)
diagbroyden = _nonlin_wrapper('diagbroyden', DiagBroyden)
excitingmixing = _nonlin_wrapper('excitingmixing', ExcitingMixing)
newton_krylov = _nonlin_wrapper('newton_krylov', KrylovJacobian)