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scipy.optimize.golden¶
-
scipy.optimize.
golden
(func, args=(), brack=None, tol=1.4901161193847656e-08, full_output=0, maxiter=5000)[source]¶ Return the minimum of a function of one variable.
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 func(x,*args)
Objective function to minimize.
args : tuple, optional
Additional arguments (if present), passed to func.
brack : tuple, optional
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 obtained solution will satisfy a<=x<=c.tol : float, optional
x tolerance stop criterion
full_output : bool, optional
If True, return optional outputs.
maxiter : int
Maximum number of iterations to perform.
See also
minimize_scalar
- Interface to minimization algorithms for scalar univariate functions. See the ‘Golden’ method in particular.
Notes
Uses analog of bisection method to decrease the bracketed interval.