Symbol based Convergence Analysis in Block Multigrid Methods with Applications for Stokes Problems

TL;DR

This paper introduces a symbol-based convergence analysis for block multigrid methods applied to saddle-point problems like the Stokes equations, demonstrating efficiency and size-independent convergence through numerical experiments.

Contribution

It develops a novel symbol-based approach for analyzing convergence in block multigrid methods with Toeplitz structures, specifically applied to Stokes problems.

Findings

Convergence rate is independent of matrix size.

Method effectively exploits block Toeplitz structure.

Numerical results outperform existing strategies.

Abstract

The main focus of this paper is the study of efficient multigrid methods for large linear systems with a particular saddle-point structure. Indeed, when the system matrix is symmetric, but indefinite, the variational convergence theory that is usually used to prove multigrid convergence cannot be directly applied. However, different algebraic approaches analyze properly preconditioned saddle-point problems, proving convergence of the Two-Grid method. In particular, this is efficient when the blocks of the coefficient matrix possess a Toeplitz or circulant structure. Indeed, it is possible to derive sufficient conditions for convergence and provide optimal parameters for the preconditioning of the saddle-point problem in terms of the associated generating symbols. In this paper, we propose a symbol-based convergence analysis for problems that have a hidden block Toeplitz structure. Then, they can be investigated focusing on the properties of the associated generating function f, which consequently is a matrix-valued function with dimension depending on the block size of the problem. As numerical tests we focus on the matrix sequence stemming from the finite element approximation of the Stokes problem. We show the efficiency of the methods studying the hidden 9-by-9 block multilevel structure of the obtained matrix sequence. Moreover, we propose an efficient algebraic multigrid method with convergence rate independent of the matrix size. Finally, we present several numerical tests comparing the results with state-of-the-art strategies.