Scaling Optimized Spectral Approximations on Unbounded Domains: The Generalized Hermite and Laguerre Methods

TL;DR

This paper introduces a new error analysis framework for scaled Hermite and Laguerre spectral methods on unbounded domains, enabling better approximation accuracy and understanding of convergence behaviors.

Contribution

It develops a systematic framework analogous to Nyquist-Shannon sampling, guiding optimal scaling and revealing complex convergence patterns for these spectral methods.

Findings

The framework predicts root-exponential convergence behaviors.

Different decay and oscillation characteristics affect convergence rates.

Concatenated Laguerre functions can outperform single Hermite sets.

Abstract

We propose a novel error analysis framework for scaled generalized Laguerre and generalized Hermite approximations.This framework can be regarded as an analogue of the Nyquist-Shannon sampling theorem: It characterizes the spatial and frequency bandwidths that can be effectively captured by Laguerre or Hermite sampling points. Provided a function satisfies the corresponding bandwidth constraints, it can be accurately approximated within this framework. The proposed framework is notably more powerful than classical theory -- it not only provides systematic guidance for choosing the optimal scaling factor, but also predicts root-exponential and other intricate convergence behaviors that classical approaches fail to capture. Leveraging this framework, we conducted a detailed comparative study of Hermite and Laguerre approximations. We find that functions with similar decay and oscillation characteristics may nonetheless display markedly different convergence rates. Furthermore, approximations based on two concatenated sets of Laguerre functions may offer significant advantages over those using a single set of Hermite functions.