{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:19:56Z","timestamp":1760242796711,"version":"build-2065373602"},"reference-count":11,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2016,11,22]],"date-time":"2016-11-22T00:00:00Z","timestamp":1479772800000},"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>We propose a two-stage method to test the robustness of the generalized approximate message passing algorithm (GAMP). A pursuit process based on the marginal posterior probability is inserted in the standard GAMP algorithm to find the support of a sparse vector, and a revised GAMP process is used to estimate the amplitudes of the support. The numerical experiments with simulation and real world data confirm the robustness and performance of our proposed algorithm.<\/jats:p>","DOI":"10.3390\/a9040079","type":"journal-article","created":{"date-parts":[[2016,11,22]],"date-time":"2016-11-22T11:13:07Z","timestamp":1479813187000},"page":"79","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Two-Stage Method to Test the Robustness of the Generalized Approximate Message Passing Algorithm"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1806-1560","authenticated-orcid":false,"given":"Qingshan","family":"You","sequence":"first","affiliation":[{"name":"School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Yongjie","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Qun","family":"Wan","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]}],"member":"1968","published-online":{"date-parts":[[2016,11,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Eldar, Y.C., and Kutyniok, G. (2012). Compressed Sensing: Theory and Applications, Cambridge University Press.","DOI":"10.1017\/CBO9780511794308"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"18914","DOI":"10.1073\/pnas.0909892106","article-title":"Message passing algorithms for compressed sensing","volume":"106","author":"Donoho","year":"2009","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_3","unstructured":"Rangan, S. (August, January 31). Generalized approximate message passing for estimation with random linear mixing. Proceedings of the IEEE International Symposium on Information Theory (ISIT), St. Petersburg, Russia."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Vila, J., Schniter, P., Rangan, S., Krzakala, F., and Zdeborov\u00e1, L. (2015, January 19\u201324). Adaptive damping and mean removal for the generalized approximate message passing algorithm. 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Proceedings of the Asilomar Conference on Signals, Systems, and Computers (SS&C), Pacific Grove, CA, USA.","DOI":"10.1109\/ACSSC.2011.6190117"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2200000001","article-title":"Graphical models, exponential families, and variational inference","volume":"1","author":"Wainwright","year":"2008","journal-title":"Found. Trends Mach. Learn."},{"key":"ref_9","unstructured":"Pati, Y.C., Rezaiifar, R., and Krishnaprasad, P.S. (1993, January 1\u20133). Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition. Proceedings of the Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1109\/TIT.2005.862083","article-title":"Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information","volume":"52","author":"Romberg","year":"2006","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_11","unstructured":"Barbier, J. Statistical Physics and Approximate Message Passing Algorithms for Sparse Linear Estimation Problems in Signal Processing and Coding Theory. [Ph.D. Thesis, Universit\u00e9 Paris Diderot\u2013Paris VII]."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/9\/4\/79\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:27:08Z","timestamp":1760210828000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/9\/4\/79"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,11,22]]},"references-count":11,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2016,12]]}},"alternative-id":["a9040079"],"URL":"https:\/\/doi.org\/10.3390\/a9040079","relation":{},"ISSN":["1999-4893"],"issn-type":[{"type":"electronic","value":"1999-4893"}],"subject":[],"published":{"date-parts":[[2016,11,22]]}}}