Qingshan Liu, Wei Zhang, Jiang Xiong, Bingrong Xu, Long Cheng
{"title":"基于投影的约束L1最小化优化算法及其在稀疏信号重构中的应用","authors":"Qingshan Liu, Wei Zhang, Jiang Xiong, Bingrong Xu, Long Cheng","doi":"10.1109/ICIST.2018.8426078","DOIUrl":null,"url":null,"abstract":"In this paper, a projection-base algorithm is proposed for solving the constrained L1-minimization problem. Furthermore, the algorithm is utilized to sparse signal reconstruction. The L1 -minimization is first converted into some equations which are described by the projections onto a hyer box set and the nonnegative quadrant. Then a iterative algorithm is proposed for solving the L1 -minimization problem. Next, the algorithm is applied to sparse signal reconstruction described as an L1- minimization problem subject to L∞ -norm noise constraint, or equivalently bound constraint. Finally, several experiments are presented to show the performance of the proposed algorithm.","PeriodicalId":331555,"journal":{"name":"2018 Eighth International Conference on Information Science and Technology (ICIST)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Projection-Based Algorithm for Constrained L1- Minimization Optimization with Application to Sparse Signal Reconstruction\",\"authors\":\"Qingshan Liu, Wei Zhang, Jiang Xiong, Bingrong Xu, Long Cheng\",\"doi\":\"10.1109/ICIST.2018.8426078\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, a projection-base algorithm is proposed for solving the constrained L1-minimization problem. Furthermore, the algorithm is utilized to sparse signal reconstruction. The L1 -minimization is first converted into some equations which are described by the projections onto a hyer box set and the nonnegative quadrant. Then a iterative algorithm is proposed for solving the L1 -minimization problem. Next, the algorithm is applied to sparse signal reconstruction described as an L1- minimization problem subject to L∞ -norm noise constraint, or equivalently bound constraint. Finally, several experiments are presented to show the performance of the proposed algorithm.\",\"PeriodicalId\":331555,\"journal\":{\"name\":\"2018 Eighth International Conference on Information Science and Technology (ICIST)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 Eighth International Conference on Information Science and Technology (ICIST)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIST.2018.8426078\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 Eighth International Conference on Information Science and Technology (ICIST)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIST.2018.8426078","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Projection-Based Algorithm for Constrained L1- Minimization Optimization with Application to Sparse Signal Reconstruction
In this paper, a projection-base algorithm is proposed for solving the constrained L1-minimization problem. Furthermore, the algorithm is utilized to sparse signal reconstruction. The L1 -minimization is first converted into some equations which are described by the projections onto a hyer box set and the nonnegative quadrant. Then a iterative algorithm is proposed for solving the L1 -minimization problem. Next, the algorithm is applied to sparse signal reconstruction described as an L1- minimization problem subject to L∞ -norm noise constraint, or equivalently bound constraint. Finally, several experiments are presented to show the performance of the proposed algorithm.