Analysis of the Self Projected Matching Pursuit Algorithm

Laura Rebollo-Neira*, Miroslav Rozlovznik, Pradip Sasmal

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

The convergence and numerical analysis of a low memory implementation of the Orthogonal Matching Pursuit greedy strategy, which is termed Self Projected Matching Pursuit, is presented. This approach renders an iterative way of solving the least squares problem with much less storage requirement than direct linear algebra techniques. Hence, it is appropriate for solving large linear systems. The analysis highlights its suitability within the class of well posed problems.
Original languageEnglish
Pages (from-to)8980-8994
Number of pages15
Journal Journal of The Franklin Institute
Volume357
Issue number13
Early online date13 Jun 2020
DOIs
Publication statusPublished - Sep 2020

Bibliographical note

© 2020, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

Fingerprint Dive into the research topics of 'Analysis of the Self Projected Matching Pursuit Algorithm'. Together they form a unique fingerprint.

Cite this