# scipy.optimize.excitingmixing

scipy.optimize.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)

  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.

  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.

  See also

  `root`

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

scipy.optimize.anderson scipy.optimize.linearmixing