Skip to main content
`Ctrl`+`K`
scipy.optimize.

# golden

scipy.optimize.golden(func, args=(), brack=None, tol=np.float64(1.4901161193847656e-08), full_output=0, maxiter=5000)

  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:
    func callable func(x,*args)

      Objective function to minimize.

    args tuple, optional

      Additional arguments (if present), passed to func.

    brack tuple, optional

      Either a triple `(xa, xb, xc)` where `xa < xb < xc` and `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 `x` will not necessarily satisfy `xa <= x <= xb`.

    tol float, optional

      x tolerance stop criterion

    full_output bool, optional

      If True, return optional outputs.

    maxiter int

      Maximum number of iterations to perform.

  Returns:
    xmin ndarray

      Optimum point.

    fval float

      (Optional output) Optimum function value.

    funcalls int

      (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)