Generalized Convolution Quadrature for non smooth sectorial problems

TL;DR

This paper extends the generalized Convolution Quadrature method to handle non-smooth data in sectorial problems, providing stability, convergence analysis, and efficient implementation strategies, especially for graded meshes.

Contribution

It offers a new analysis of gCQ under realistic regularity assumptions, including stability, convergence, and optimal graded mesh selection, with practical implementation insights.

Findings

gCQ achieves stability and convergence with less restrictive data regularity.

Optimal graded meshes improve accuracy for non-smooth data.

Fast, memory-efficient gCQ implementation is feasible and effective.

Abstract

We consider the application of the generalized Convolution Quadrature (gCQ) to approximate the solution of an important class of sectorial problems. The gCQ is a generalization of Lubich's Convolution Quadrature (CQ) that allows for variable steps. The available stability and convergence theory for the gCQ requires non realistic regularity assumptions on the data, which do not hold in many applications of interest, such as the approximation of subdiffusion equations. It is well known that for non smooth enough data the original CQ, with uniform steps, presents an order reduction close to the singularity. We generalize the analysis of the gCQ to data satisfying realistic regularity assumptions and provide sufficient conditions for stability and convergence on arbitrary sequences of time points. We consider the particular case of graded meshes and show how to choose them optimally, according to the behaviour of the data. An important advantage of the gCQ method is that it allows for a fast and memory reduced implementation. We describe how the fast and oblivious gCQ can be implemented and illustrate our theoretical results with several numerical experiments.