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Title: Convergence analysis of a block preconditioned steepest descent eigensolver with implicit deflation
Abstract

Gradient‐type iterative methods for solving Hermitian eigenvalue problems can be accelerated by using preconditioning and deflation techniques. A preconditioned steepest descent iteration with implicit deflation (PSD‐id) is one of such methods. The convergence behavior of the PSD‐id is recently investigated based on the pioneering work of Samokish on the preconditioned steepest descent method (PSD). The resulting non‐asymptotic estimates indicate a superlinear convergence of the PSD‐id under strong assumptions on the initial guess. The present paper utilizes an alternative convergence analysis of the PSD by Neymeyr under much weaker assumptions. We embed Neymeyr's approach into the analysis of the PSD‐id using a restricted formulation of the PSD‐id. More importantly, we extend the new convergence analysis of the PSD‐id to a practically preferred block version of the PSD‐id, or BPSD‐id, and show the cluster robustness of the BPSD‐id. Numerical examples are provided to validate the theoretical estimates.

 
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Award ID(s):
1913364
NSF-PAR ID:
10482962
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
Wiley
Date Published:
Journal Name:
Numerical Linear Algebra with Applications
Volume:
30
Issue:
5
ISSN:
1070-5325
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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