# 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 using golden section method.

  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.

  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**2

      >>> from scipy import optimize

      >>> minimum = optimize.golden(f, brack=(1, 2))
      >>> minimum
      1.5717277788484873e-162
      >>> minimum = optimize.golden(f, brack=(-1, 0.5, 2))
      >>> minimum
      -1.5717277788484873e-162

scipy.optimize.brent scipy.optimize.leastsq