{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:48:32Z","timestamp":1760237312203,"version":"build-2065373602"},"reference-count":17,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,4,9]],"date-time":"2020-04-09T00:00:00Z","timestamp":1586390400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>In this paper, the Feasibility Pump is adapted for the problem of sparse representations of signals affected by Gaussian noise. This adaptation is tested and then compared to Orthogonal Matching Pursuit (OMP) and the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA). The feasibility pump recovers the true support much better than the other two algorithms and, as the SNR decreases and the support size increases, it has a smaller recovery and representation error when compared with its competitors. It is observed that, in order for the algorithm to be efficient, a regularization parameter and a weight term for the error are needed.<\/jats:p>","DOI":"10.3390\/a13040088","type":"journal-article","created":{"date-parts":[[2020,4,9]],"date-time":"2020-04-09T14:42:03Z","timestamp":1586443323000},"page":"88","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Feasibility Pump Algorithm for Sparse Representation under Gaussian Noise"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6136-5543","authenticated-orcid":false,"given":"Florin Ilarion","family":"Miertoiu","sequence":"first","affiliation":[{"name":"Department of Automatic Control and Computers, University Politehnica of Bucharest, 313 Spl. Independen\u0163ei, 060042 Bucharest, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4555-1714","authenticated-orcid":false,"given":"Bogdan","family":"Dumitrescu","sequence":"additional","affiliation":[{"name":"Department of Automatic Control and Computers, University Politehnica of Bucharest, 313 Spl. Independen\u0163ei, 060042 Bucharest, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1007\/s10107-004-0570-3","article-title":"The feasibility pump","volume":"104","author":"Fischetti","year":"2005","journal-title":"Math. 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Optim."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13675-018-0109-7","article-title":"Ten years of feasibility pump, and counting","volume":"7","author":"Berthold","year":"2019","journal-title":"EURO J. Comput. Optim."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"5615243","DOI":"10.1155\/2019\/5615243","article-title":"Feasibility Pump Algorithm for Sparse Representation under Laplacian Noise","volume":"2019","author":"Miertoiu","year":"2019","journal-title":"Math. Probl. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1405","DOI":"10.1109\/TSP.2015.2496367","article-title":"Exact sparse approximation problems via mixed-integer programming: Formulations and computational performance","volume":"64","author":"Bourguignon","year":"2016","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_14","first-page":"49","article-title":"LASSO tuning parameter selection","volume":"57","author":"Kirkland","year":"2015","journal-title":"Annu. Proc. S. Afr. Stat. Assoc. Conf."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1109\/ACCESS.2015.2430359","article-title":"A survey of sparse representation: Algorithms and applications","volume":"3","author":"Zhang","year":"2015","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2231","DOI":"10.1109\/TIT.2004.834793","article-title":"Greed is good: Algorithmic results for sparse approximation","volume":"50","author":"Tropp","year":"2004","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1137\/080716542","article-title":"A fast iterative shrinkage-thresholding algorithm for linear inverse problems","volume":"2","author":"Beck","year":"2009","journal-title":"SIAM J. 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