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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    © 2020, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International


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