Analysis of the Self Projected Matching Pursuit Algorithm

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.

Publication DOI: https://doi.org/10.1016/j.jfranklin.2020.06.006
Divisions: College of Engineering & Physical Sciences > Mathematics
Additional Information: © 2020, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
Uncontrolled Keywords: Control and Systems Engineering,Signal Processing,Computer Networks and Communications,Applied Mathematics
Full Text Link:
Related URLs: https://www.sci ... 016003220304257 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2020-09
Published Online Date: 2020-06-13
Accepted Date: 2020-06-08
Authors: Rebollo-Neira, Laura (ORCID Profile 0000-0002-7420-8977)
Rozlovznik, Miroslav
Sasmal, Pradip

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