Contraction and optimality properties of an adaptive Legendre-Galerkin method: the multi-dimensional case

TL;DR

This paper provides a rigorous analysis of an adaptive multidimensional Legendre-Galerkin method, establishing its convergence and optimality properties for elliptic boundary-value problems using a specially constructed Riesz basis.

Contribution

It introduces a multidimensional Riesz basis for $H^1$ and develops an adaptive algorithm with proven convergence and optimality in Gevrey-type sparsity classes.

Findings

Proves convergence of the adaptive algorithm.

Establishes optimality in certain sparsity classes.

Constructs a new multidimensional Riesz basis.

Abstract

We analyze the theoretical properties of an adaptive Legendre-Galerkin method in the multidimensional case. After the recent investigations for Fourier-Galerkin methods in a periodic box and for Legendre-Galerkin methods in the one dimensional setting, the present study represents a further step towards a mathematically rigorous understanding of adaptive spectral/$hp$ discretizations of elliptic boundary-value problems. The main contribution of the paper is a careful construction of a multidimensional Riesz basis in $H^1$, based on a quasi-orthonormalization procedure. This allows us to design an adaptive algorithm, to prove its convergence by a contraction argument, and to discuss its optimality properties (in the sense of non-linear approximation theory) in certain sparsity classes of Gevrey type.