Applying acceleration to Krylov subspace eigenvalue solvers

Fuente: arXiv
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Main Authors: Baker, Michelle, Pollock, Sara
Format: Preprint
Published: 2026
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author Baker, Michelle
Pollock, Sara
author_facet Baker, Michelle
Pollock, Sara
contents In this paper, we apply acceleration to the inverse-free preconditioned Krylov subspace method introduced by Golub and Ye, which solves the symmetric generalized eigenvalue problem for the algebraically smallest eigenvalue. As the method is an improvement on steepest descent, we consider acceleration based on Nesterov accelerated steepest descent and Polyak's heavy-ball method. We extend acceleration to the block version of the Krylov subspace method and prove convergence for a more generalized choice of subspace. We present numerical results demonstrating the effect of fixed and safeguarded-adaptive choice of the momentum parameter, which show convergence in fewer outer iterations compared with LOBPCG with the same subspace size and generally fewer iterations than the base method when solving for multiple clustered eigenvalues with small dimension size. We also provide an explanation for the acceleration seen from implementing Polyak's heavy-ball method, including justifying the given parameter range.
format Preprint
id arxiv_https___arxiv_org_abs_2603_20590
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Applying acceleration to Krylov subspace eigenvalue solvers
Baker, Michelle
Pollock, Sara
Numerical Analysis
In this paper, we apply acceleration to the inverse-free preconditioned Krylov subspace method introduced by Golub and Ye, which solves the symmetric generalized eigenvalue problem for the algebraically smallest eigenvalue. As the method is an improvement on steepest descent, we consider acceleration based on Nesterov accelerated steepest descent and Polyak's heavy-ball method. We extend acceleration to the block version of the Krylov subspace method and prove convergence for a more generalized choice of subspace. We present numerical results demonstrating the effect of fixed and safeguarded-adaptive choice of the momentum parameter, which show convergence in fewer outer iterations compared with LOBPCG with the same subspace size and generally fewer iterations than the base method when solving for multiple clustered eigenvalues with small dimension size. We also provide an explanation for the acceleration seen from implementing Polyak's heavy-ball method, including justifying the given parameter range.
title Applying acceleration to Krylov subspace eigenvalue solvers
topic Numerical Analysis
url https://arxiv.org/abs/2603.20590