scipy.optimize.golden

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

Return the minimum of a function of one variable.

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.