{"title":"一种基于非光滑优化信赖域方法的压缩感知重构算法","authors":"Enming Dong, Jianping Li, Jinjie Liu","doi":"10.1109/FSKD.2012.6233910","DOIUrl":null,"url":null,"abstract":"The signal reconstruction problems of Compressed Sensing is equal to a nonsmooth optimization problem. Since the original signal is sparse, a new l 1 -Minimization reconstruction algorithm is proposed based on modified trust region method of nonsmooth optimization. The algorithm can also reconstruct signal in super-linear convergence rate. Simulation results show that the algorithm is robust in reconstructing the original signal.","PeriodicalId":337941,"journal":{"name":"International Conference on Fuzzy Systems and Knowledge Discovery","volume":"311 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Compressed Sensing reconstruct algorithm based on trust region method of nonsmooth optimization\",\"authors\":\"Enming Dong, Jianping Li, Jinjie Liu\",\"doi\":\"10.1109/FSKD.2012.6233910\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The signal reconstruction problems of Compressed Sensing is equal to a nonsmooth optimization problem. Since the original signal is sparse, a new l 1 -Minimization reconstruction algorithm is proposed based on modified trust region method of nonsmooth optimization. The algorithm can also reconstruct signal in super-linear convergence rate. Simulation results show that the algorithm is robust in reconstructing the original signal.\",\"PeriodicalId\":337941,\"journal\":{\"name\":\"International Conference on Fuzzy Systems and Knowledge Discovery\",\"volume\":\"311 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-05-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Conference on Fuzzy Systems and Knowledge Discovery\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FSKD.2012.6233910\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Fuzzy Systems and Knowledge Discovery","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FSKD.2012.6233910","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Compressed Sensing reconstruct algorithm based on trust region method of nonsmooth optimization
The signal reconstruction problems of Compressed Sensing is equal to a nonsmooth optimization problem. Since the original signal is sparse, a new l 1 -Minimization reconstruction algorithm is proposed based on modified trust region method of nonsmooth optimization. The algorithm can also reconstruct signal in super-linear convergence rate. Simulation results show that the algorithm is robust in reconstructing the original signal.
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