{"title":"贪婪信号恢复审查","authors":"D. Needell, J. Tropp, R. Vershynin","doi":"10.1109/ACSSC.2008.5074572","DOIUrl":null,"url":null,"abstract":"The two major approaches to sparse recovery are L1-minimization and greedy methods. Recently, Needell and Vershynin developed regularized orthogonal matching pursuit (ROMP) that has bridged the gap between these two approaches. ROMP is the first stable greedy algorithm providing uniform guarantees. Even more recently, Needell and Tropp developed the stable greedy algorithm compressive sampling matching pursuit (CoSaMP). CoSaMP provides uniform guarantees and improves upon the stability bounds and RIC requirements of ROMP. CoSaMP offers rigorous bounds on computational cost and storage. In many cases, the running time is just O(N log N), where N is the ambient dimension of the signal. This review summarizes these major advances.","PeriodicalId":416114,"journal":{"name":"2008 42nd Asilomar Conference on Signals, Systems and Computers","volume":"38 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"99","resultStr":"{\"title\":\"Greedy signal recovery review\",\"authors\":\"D. Needell, J. Tropp, R. Vershynin\",\"doi\":\"10.1109/ACSSC.2008.5074572\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The two major approaches to sparse recovery are L1-minimization and greedy methods. Recently, Needell and Vershynin developed regularized orthogonal matching pursuit (ROMP) that has bridged the gap between these two approaches. ROMP is the first stable greedy algorithm providing uniform guarantees. Even more recently, Needell and Tropp developed the stable greedy algorithm compressive sampling matching pursuit (CoSaMP). CoSaMP provides uniform guarantees and improves upon the stability bounds and RIC requirements of ROMP. CoSaMP offers rigorous bounds on computational cost and storage. In many cases, the running time is just O(N log N), where N is the ambient dimension of the signal. This review summarizes these major advances.\",\"PeriodicalId\":416114,\"journal\":{\"name\":\"2008 42nd Asilomar Conference on Signals, Systems and Computers\",\"volume\":\"38 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"99\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 42nd Asilomar Conference on Signals, Systems and Computers\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ACSSC.2008.5074572\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 42nd Asilomar Conference on Signals, Systems and Computers","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACSSC.2008.5074572","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The two major approaches to sparse recovery are L1-minimization and greedy methods. Recently, Needell and Vershynin developed regularized orthogonal matching pursuit (ROMP) that has bridged the gap between these two approaches. ROMP is the first stable greedy algorithm providing uniform guarantees. Even more recently, Needell and Tropp developed the stable greedy algorithm compressive sampling matching pursuit (CoSaMP). CoSaMP provides uniform guarantees and improves upon the stability bounds and RIC requirements of ROMP. CoSaMP offers rigorous bounds on computational cost and storage. In many cases, the running time is just O(N log N), where N is the ambient dimension of the signal. This review summarizes these major advances.