Prescription for choosing an interpolating function

TL;DR

This paper introduces a criterion for selecting the best interpolating function among various methods, validated through correlation studies with known true functions.

Contribution

It proposes reference quantities to effectively choose appropriate interpolating functions from multiple options.

Findings

Reference quantities correlate well with the true deviation.

The method helps identify optimal interpolating functions.

Validation through examples confirms the effectiveness of the criterion.

Abstract

Interpolating functional method is a powerful tool for studying the behavior of a quantity in the intermediate region of the parameter space of interest by using its perturbative expansions at both ends. Recently several interpolating functional methods have been proposed, in addition to the well-known Pade approximant, namely the "Fractional Power of Polynomial" (FPP) and the "Fractional Power of Rational functions" (FPR) methods. Since combinations of these methods also give interpolating functions, we may end up with multitudes of the possible approaches. So a criterion for choosing an appropriate interpolating function is very much needed. In this paper, we propose reference quantities which can be used for choosing a good interpolating function. In order to validate the prescription based on these quantities, we study the degree of correlation between "the reference quantities" and the "actual degree of deviation between the interpolating function and the true function" in examples where the true functions are known.