def golden(func, args=(), brack=None, tol=_epsilon, full_output=<NUM_LIT:0>):
options = {'<STR_LIT>': tol}<EOL>res = _minimize_scalar_golden(func, brack, args, **options)<EOL>if full_output:<EOL><INDENT>return res['<STR_LIT:x>'], res['<STR_LIT>'], res['<STR_LIT>']<EOL><DEDENT>else:<EOL><INDENT>return res['<STR_LIT:x>']<EOL><DEDENT>
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
    Additional arguments (if present), passed to func.
brack : tuple
    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
    x tolerance stop criterion
full_output : bool
    If True, return optional outputs.

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.