Sparse recovery by means of nonnegative least squares

Simon Foucart, David Koslicki

    Research output: Contribution to journalArticlepeer-review

    51 Scopus citations


    This letter demonstrates that sparse recovery can be achieved by an ℓ1-minimization ersatz easily implemented using a conventional nonnegative least squares algorithm. A connection with orthogonal matching pursuit is also highlighted. The preliminary results call for more investigations on the potential of the method and on its relations to classical sparse recovery algorithms.

    Original languageEnglish (US)
    Article number6750023
    Pages (from-to)498-502
    Number of pages5
    JournalIEEE Signal Processing Letters
    Issue number4
    StatePublished - Apr 2014

    All Science Journal Classification (ASJC) codes

    • Signal Processing
    • Electrical and Electronic Engineering
    • Applied Mathematics


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