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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:
    Ffunction(x) -> f

      Function whose root to find; should take and return an array-like object.

    xinarray_like

      Initial guess for the solution

    alphafloat, optional

      Initial Jacobian approximation is (-1/alpha).

    alphamaxfloat, optional

      The entries of the diagonal Jacobian are kept in the range `[alpha, alphamax]`.

    iterint, optional

      Number of iterations to make. If omitted (default), make as many as required to meet tolerances.

    verbosebool, optional

      Print status to stdout on every iteration.

    maxiterint, optional

      Maximum number of iterations to make. If more are needed to meet convergence, `NoConvergence` is raised.

    f_tolfloat, optional

      Absolute tolerance (in max-norm) for the residual. If omitted, default is 6e-6.

    f_rtolfloat, optional

      Relative tolerance for the residual. If omitted, not used.

    x_tolfloat, 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_rtolfloat, optional

      Relative minimum step size. If omitted, not used.

    tol_normfunction(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’.

    callbackfunction, 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:
    solndarray

      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.