Higher order stroboscopic averaged functions: a general relationship with Melnikov functions

TL;DR

This paper establishes a general relationship between higher order stroboscopic averaged functions and Melnikov functions for perturbed periodic differential equations, simplifying their computation and providing Mathematica algorithms.

Contribution

It introduces a novel general relationship between two types of averaged functions, enabling easier computation without near-identity transformations.

Findings

Derived a general formula linking $ extbf{g}_i$ and $ extbf{f}_i$ for all orders

Provided Mathematica algorithms for computing higher order averaging functions

Simplified the process of analyzing periodic solutions in perturbed systems

Abstract

In the research literature, one can find distinct notions for higher order averaged functions of regularly perturbed non-autonomous $T$- periodic differential equations of the kind $x'=\varepsilon F(t,x,\varepsilon)$. By one hand, the classical (stroboscopic) averaging method provides asymptotic estimates for its solutions in terms of some uniquely defined functions $\mathbf{g}_i$'s, called averaged functions, which are obtained through near-identity stroboscopic transformations and by solving homological equations. On the other hand, a Melnikov procedure is employed to obtain bifurcation functions $\mathbf{f}_i$'s which controls in some sense the existence of isolated $T$-periodic solutions of the differential equation above. In the research literature, the bifurcation functions $\mathbf{f}_i$'s are sometimes likewise called averaged functions, nevertheless, they also receive the name of Poincar\'{e}-Pontryagin-Melnikov functions or just Melnikov functions. While it is known that $\mathbf{f}_1=T \mathbf{g}_1,$ a general relationship between $\mathbf{g}_i$ and $\mathbf{f}_i$ is not known so far for $i\geq 2.$ Here, such a general relationship between these two distinct notions of averaged functions is provided, which allows the computation of the stroboscopic averaged functions of any order avoiding the necessity of dealing with near-identity transformations and homological equations. In addition, an Appendix is provided with implemented Mathematica algorithms for computing both higher order averaging functions.