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

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


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

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