GSS: Golden Section Search for Minimising Univariate Function over...

In skedastic: Handling Heteroskedasticity in the Linear Regression Model

Golden Section Search for Minimising Univariate Function over a Closed Interval

Description

Golden Section Search (GSS) is a useful algorithm for minimising a continuous univariate function f(x) over an interval \left[a,b\right] in instances where the first derivative f'(x) is not easily obtainable, such as with loss functions that need to be minimised under cross-validation to tune a hyperparameter in a machine learning model. The method is described by \insertCiteFox21;textualskedastic.

Usage

GSS(f, a, b, tol = 1e-08, maxitgss = 100L, ...)

Arguments

f	A function of one variable that returns a numeric value
a	A numeric of length 1 representing the lower endpoint of the search interval
b	A numeric of length 1 representing the upper endpoint of the search interval; must be greater than a
tol	A numeric of length 1 representing the tolerance used to determine when the search interval has been narrowed sufficiently for convergence
maxitgss	An integer of length 1 representing the maximum number of iterations to use in the search
...	Additional arguments to pass to f

Details

This function is modelled after a MATLAB function written by \insertCiteZarnowiec22;textualskedastic. The method assumes that there is one local minimum in this interval. The solution produced is also an interval, the width of which can be made arbitrarily small by setting a tolerance value. Since the desired solution is a single point, the midpoint of the final interval can be taken as the best approximation to the solution.

The premise of the method is to shorten the interval \left[a,b\right] by comparing the values of the function at two test points, x_1 < x_2, where x_1 = a + r(b-a) and x_2 = b - r(b-a), and r=(\sqrt{5}-1)/2\approx 0.618 is the reciprocal of the golden ratio. One compares f(x_1) to f(x_2) and updates the search interval \left[a,b\right] as follows:

• If f(x_1)<f(x_2), the solution cannot lie in \left[x_2,b\right]; thus, update the search interval to

  \left[a_\mathrm{new},b_\mathrm{new}\right]=\left[a,x_2\right]

• If f(x_1)>f(x_2), the solution cannot lie in \left[a,x_1\right]; thus, update the search interval to

  \left[a_\mathrm{new},b_\mathrm{new}\right]=\left[x_1,b\right]

One then chooses two new test points by replacing a and b with a_\mathrm{new} and b_\mathrm{new} and recalculating x_1 and x_2 based on these new endpoints. One continues iterating in this fashion until b-a< \tau, where \tau is the desired tolerance.

Value

A list object containing the following:

• argmin, the argument of f that minimises f

• funmin, the minimum value of f achieved at argmin

• converged, a logical indicating whether the convergence tolerance was satisfied

• iterations, an integer indicating the number of search iterations used

References

\insertAllCited

See Also

goldsect is similar to this function, but does not allow the user to pass additional arguments to f

Examples

f <- function(x) (x - 1) ^ 2
GSS(f, a = 0, b = 2)

skedastic documentation built on June 8, 2025, 11:07 a.m.