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# scipy.optimize.golden

`scipy.optimize.``golden`(func, args=(), brack=None, tol=1.4901161193847656e-08, full_output=0, maxiter=5000)

  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.