Module « scipy.optimize »

Fonction excitingmixing - module scipy.optimize

Signature de la fonction excitingmixing

def excitingmixing(F, xin, iter=None, alpha=None, alphamax=1.0, 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) 

Description

excitingmixing.__doc__

    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
    ----------
    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
    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]``.
    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.