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.