scipy.optimize.bracket

scipy.optimize.bracket(func, xa=0.0, xb=1.0, args=(), grow_limit=110.0, maxiter=1000)

Given a function and distinct initial points, search in the downhill direction (as defined by the initital points) and return new points xa, xb, xc that bracket the minimum of the function f(xa) > f(xb) < f(xc). It doesn’t always mean that obtained solution will satisfy xa<=x<=xb

Parameters:

func : callable f(x,*args)

Objective function to minimize.

xa, xb : float

Bracketing interval.

args : tuple

Additional arguments (if present), passed to func.

grow_limit : float

Maximum grow limit.

maxiter : int

Maximum number of iterations to perform.

Returns:

xa, xb, xc, fa, fb, fc, funcalls

xa, xb, xc : float

Bracket.

fa, fb, fc : float

Objective function values in bracket.

funcalls : int

Number of function evaluations made.