A Bootstrap Multigrid Eigensolver

TL;DR

This paper develops a bootstrap geometric multigrid method for efficiently solving eigenvalue problems from PDE discretizations, demonstrating its effectiveness in handling multiple, interior, and shifted indefinite eigenvalues through numerical experiments.

Contribution

It introduces a simplified bootstrap geometric multigrid approach derived from BAMG, tailored for PDE eigenproblems, with demonstrated numerical success.

Findings

Effective recovery of large multiplicity eigenvalues

Accurate computation of interior eigenvalues

Successful approximation of shifted indefinite problems

Abstract

This paper introduces bootstrap multigrid methods for solving eigenvalue problems arising from the discretization of partial differential equations. Inspired by the full bootstrap algebraic multigrid (BAMG) setup algorithm that includes an AMG eigensolver, it is illustrated how the algorithm can be simplified for the case of a discretized partial differential equation (PDE), thereby developing a bootstrap geometric multigrid (BMG) approach. We illustrate numerically the efficacy of the BMG method for: (1) recovering eigenvalues having large multiplicity, (2) computing interior eigenvalues, and (3) approximating shifted indefinite eigenvalue problems. Numerical experiments are presented to illustrate the basic components and ideas behind the success of the overall bootstrap multigrid approach. For completeness, we present a simplified error analysis of a two-grid bootstrap algorithm for the Laplace-Beltrami eigenvalue problem.