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

Additional arguments (if present).

brack : tuple

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 the obtained solution will satisfy a<=x<=c.

full_output : bool

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

Returns:
xmin : ndarray

Optimum point.

fval : float

Optimum value.

iter : int

Number of iterations.

funcalls : int

Number of objective function evaluations made.

Notes

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

Previous topic

scipy.optimize.bracket

Next topic

scipy.optimize.fsolve

This Page

Quick search