Analysis of the Self Projected Matching Pursuit Algorithm

Laura Rebollo-Neira*, Miroslav Rozlovznik, Pradip Sasmal

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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
JournalJournal of The Franklin Institute
Issue number13
Early online date13 Jun 2020
Publication statusPublished - Sept 2020

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