Analysis of the Self Projected Matching Pursuit Algorithm

Laura Rebollo-Neira; Miroslav Rozlovznik; Pradip  Sasmal

doi:10.1016/j.jfranklin.2020.06.006

Analysis of the Self Projected Matching Pursuit Algorithm

Laura Rebollo-Neira^*
, Miroslav Rozlovznik
, Pradip Sasmal

^*Corresponding author for this work

Aston University

Indian Institute of Science, Bangalore
Institute of Mathematics, Czech Academy of Sciences

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

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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 language	English
Pages (from-to)	8980-8994
Number of pages	15
Journal	Journal of The Franklin Institute
Volume	357
Issue number	13
Early online date	13 Jun 2020
DOIs	https://doi.org/10.1016/j.jfranklin.2020.06.006
Publication status	Published - Sept 2020

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10.1016/j.jfranklin.2020.06.006

Analysis of the Self Projected Matching Pursuit Algorithm
© 2020, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
Accepted author manuscript, 421 KBLicence: CC BY-NC-ND 3.0

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AB - 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.

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Analysis of the Self Projected Matching Pursuit Algorithm

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