golden(func, args=(), brack=None, tol=1.4901161193847656e-08, full_output=0, maxiter=5000)
Given a function of one variable and a possible bracketing interval, return the minimum of the function isolated to a fractional precision of tol.
Uses analog of bisection method to decrease the bracketed interval.
Objective function to minimize.
Additional arguments (if present), passed to func.
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.
x tolerance stop criterion
If True, return optional outputs.
Maximum number of iterations to perform.
Return the minimum of a function of one variable using golden section method.
minimize_scalar
Interface to minimization algorithms for scalar univariate functions. See the 'Golden' :None:None:`method` in particular.
We illustrate the behaviour of the function when :None:None:`brack` is of size 2 and 3, respectively. In the case where :None:None:`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))See :
... minimum -1.5717277788484873e-162
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.optimize._optimize.golden
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