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 : 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.