Efficient Least Residual Greedy Algorithms for Sparse Recovery

Guy Leibovitz, Raja Giryes*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


We present a novel stagewise strategy for improving greedy algorithms for sparse recovery. We demonstrate its efficiency both for synthesis and analysis sparse priors, where in both cases we demonstrate its computational efficiency and competitive reconstruction accuracy. In the synthesis case, we also provide theoretical guarantees for the signal recovery that are on par with the existing perfect reconstruction bounds for the relaxation based solvers and other sophisticated greedy algorithms.

Original languageEnglish
Article number9072472
Pages (from-to)3707-3722
Number of pages16
JournalIEEE Transactions on Signal Processing
StatePublished - 2020


FundersFunder number
Horizon 2020 Framework Programme757497
European Research Council


    • Approximation algorithms
    • approximation error
    • compressed sensing
    • greedy algorithms
    • least mean square methods
    • matching pursuit algorithms


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