Convergence rates of adaptive methods, Besov spaces, and multilevel approximation

TL;DR

This paper explores the relationships between adaptive finite element approximation classes, multilevel approximation spaces, and Besov spaces, providing new embeddings and a theoretical framework for modified error measures in elliptic problems.

Contribution

It introduces multilevel approximation spaces as an intermediary between adaptive classes and Besov spaces, and develops a theory for approximation classes based on total error in elliptic problems.

Findings

Multilevel approximation spaces embed into adaptive approximation classes.

A theoretical framework for modified error-based approximation classes is established.

Characterizations of these classes relate to Besov space memberships.

Abstract

This paper concerns characterizations of approximation classes associated to adaptive finite element methods with isotropic h-refinements. It is known from the seminal work of Binev, Dahmen, DeVore and Petrushev that such classes are related to Besov spaces. The range of parameters for which the inverse embedding results hold is rather limited, and recently, Gaspoz and Morin have shown, among other things, that this limitation disappears if we replace Besov spaces by suitable approximation spaces associated to finite element approximation from uniformly refined triangulations. We call the latter spaces *multievel approximation spaces*, and argue that these spaces are placed naturally halfway between adaptive approximation classes and Besov spaces, in the sense that it is more natural to relate multilevel approximation spaces with either Besov spaces or adaptive approximation classes, than to go directly from adaptive approximation classes to Besov spaces. In particular, we prove embeddings of multilevel approximation spaces into adaptive approximation classes, complementing the inverse embedding theorems of Gaspoz and Morin. Furthermore, in the present paper, we initiate a theoretical study of adaptive approximation classes that are defined using a modified notion of error, the so-called *total error*, which is the energy error plus an oscillation term. Such approximation classes have recently been shown to arise naturally in the analysis of adaptive algorithms. We first develop a sufficiently general approximation theory framework to handle such modifications, and then apply the abstract theory to second order elliptic problems discretized by Lagrange finite elements, resulting in characterizations of modified approximation classes in terms of memberships of the problem solution and data into certain approximation spaces, which are in turn related to Besov spaces.