scipy.optimize.brent

scipy.optimize.brent(func, args=(), brack=None, tol=1.48e-08, full_output=0, maxiter=500)

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 f(x,*args)

Objective function.

args : tuple, optional

Additional arguments (if present).

brack : tuple, optional

Either a triple (xa,xb,xc) where xa<xb<xc and func(xb) < func(xa), func(xc) or a pair (xa,xb) which are used as a starting interval for a downhill bracket search (see bracket). Providing the pair (xa,xb) does not always mean the obtained solution will satisfy xa<=x<=xb.

tol : float, optional

Stop if between iteration change is less than tol.

full_output : bool, optional

If True, return all output args (xmin, fval, iter, funcalls).

maxiter : int, optional

Maximum number of iterations in solution.

Returns:

xmin : ndarray

Optimum point.

fval : float

Optimum value.

iter : int

Number of iterations.

funcalls : int

Number of objective function evaluations made.

See also

minimize_scalar

Interface to minimization algorithms for scalar univariate functions. See the ‘Brent’ method in particular.

Notes

Uses inverse parabolic interpolation when possible to speed up convergence of golden section method.