SciPy

scipy.optimize.brent

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

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.

tol : float

Stop if between iteration change is less than tol.

full_output : bool

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

maxiter : int

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.