golden#
- scipy.optimize.golden(func, args=(), brack=None, tol=1.4901161193847656e-08, full_output=0, maxiter=5000)[source]#
- Return the minimizer of a function of one variable using the golden section method. - Given a function of one variable and a possible bracketing interval, return a minimizer of the function isolated to a fractional precision of tol. - Parameters:
- funccallable func(x,*args)
- Objective function to minimize. 
- argstuple, optional
- Additional arguments (if present), passed to func. 
- bracktuple, optional
- Either a triple - (xa, xb, xc)where- xa < xb < xcand- func(xb) < func(xa) and func(xb) < func(xc), or a pair (xa, xb) to be used as initial points for a downhill bracket search (see- scipy.optimize.bracket). The minimizer- xwill not necessarily satisfy- xa <= x <= xb.
- tolfloat, optional
- x tolerance stop criterion 
- full_outputbool, optional
- If True, return optional outputs. 
- maxiterint
- Maximum number of iterations to perform. 
 
- Returns:
- xminndarray
- Optimum point. 
- fvalfloat
- (Optional output) Optimum function value. 
- funcallsint
- (Optional output) Number of objective function evaluations made. 
 
 - 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. - Examples - We illustrate the behaviour of the function when brack is of size 2 and 3, respectively. In the case where brack is of the form (xa,xb), we can see for the given values, the output need not necessarily lie in the range - (xa, xb).- >>> def f(x): ... return (x-1)**2 - >>> from scipy import optimize - >>> minimizer = optimize.golden(f, brack=(1, 2)) >>> minimizer 1 >>> res = optimize.golden(f, brack=(-1, 0.5, 2), full_output=True) >>> xmin, fval, funcalls = res >>> f(xmin), fval (9.925165290385052e-18, 9.925165290385052e-18)