Module « scipy.optimize »

Fonction golden - module scipy.optimize

Signature de la fonction golden

def golden(func, args=(), brack=None, tol=1.4901161193847656e-08, full_output=0, maxiter=5000) 

Description

golden.__doc__

    Return the minimum of a function of one variable using golden section
    method.

    Given a function of one variable and a possible bracketing interval,
    return the minimum of the function isolated to a fractional precision of
    tol.

    Parameters
    ----------
    func : callable func(x,*args)
        Objective function to minimize.
    args : tuple, optional
        Additional arguments (if present), passed to func.
    brack : tuple, optional
        Triple (a,b,c), where (a<b<c) and func(b) <
        func(a),func(c). If bracket consists of two numbers (a,
        c), then they are assumed to be a starting interval for a
        downhill bracket search (see `bracket`); it doesn't always
        mean that obtained solution will satisfy a<=x<=c.
    tol : float, optional
        x tolerance stop criterion
    full_output : bool, optional
        If True, return optional outputs.
    maxiter : int
        Maximum number of iterations to perform.

    See also
    --------
    minimize_scalar: Interface to minimization algorithms for scalar
        univariate functions. See the 'Golden' `method` in particular.

    Notes
    -----
    Uses analog of bisection method to decrease the bracketed
    interval.

    Examples
    --------
    We illustrate the behaviour of the function when `brack` is of
    size 2 and 3, respectively. In the case where `brack` is of the
    form (xa,xb), we can see for the given values, the output need
    not necessarily lie in the range ``(xa, xb)``.

    >>> def f(x):
    ...     return x**2

    >>> from scipy import optimize

    >>> minimum = optimize.golden(f, brack=(1, 2))
    >>> minimum
    1.5717277788484873e-162
    >>> minimum = optimize.golden(f, brack=(-1, 0.5, 2))
    >>> minimum
    -1.5717277788484873e-162