{"title":"基于线性化Bregman算法的稀疏化步宽自适应","authors":"M. Lunglmayr, M. Huemer","doi":"10.1109/SSP.2018.8450706","DOIUrl":null,"url":null,"abstract":"Iterative algorithms based on linearized Bregman iterations allow efficiently solving sparse estimation problems. Especially the Kaczmarz and sparse least mean squares filter (LMS) variants are very suitable for implementation in digital hardand software. However, when analyzing the error of such algorithms over the iterations one realizes that especially at early iterations only small error reductions occur. To improve this behavior, we propose to use sparsity-enabled step width adaption. We show simulations results demonstrating that this approach significantly improves the performance of sparse Kaczmarz and sparse LMS algorithms.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Sparsity-Enabled Step Width Adaption For Linearized Bregman Based Algorithms\",\"authors\":\"M. Lunglmayr, M. Huemer\",\"doi\":\"10.1109/SSP.2018.8450706\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Iterative algorithms based on linearized Bregman iterations allow efficiently solving sparse estimation problems. Especially the Kaczmarz and sparse least mean squares filter (LMS) variants are very suitable for implementation in digital hardand software. However, when analyzing the error of such algorithms over the iterations one realizes that especially at early iterations only small error reductions occur. To improve this behavior, we propose to use sparsity-enabled step width adaption. We show simulations results demonstrating that this approach significantly improves the performance of sparse Kaczmarz and sparse LMS algorithms.\",\"PeriodicalId\":330528,\"journal\":{\"name\":\"2018 IEEE Statistical Signal Processing Workshop (SSP)\",\"volume\":\"3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE Statistical Signal Processing Workshop (SSP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SSP.2018.8450706\",\"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 IEEE Statistical Signal Processing Workshop (SSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SSP.2018.8450706","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Sparsity-Enabled Step Width Adaption For Linearized Bregman Based Algorithms
Iterative algorithms based on linearized Bregman iterations allow efficiently solving sparse estimation problems. Especially the Kaczmarz and sparse least mean squares filter (LMS) variants are very suitable for implementation in digital hardand software. However, when analyzing the error of such algorithms over the iterations one realizes that especially at early iterations only small error reductions occur. To improve this behavior, we propose to use sparsity-enabled step width adaption. We show simulations results demonstrating that this approach significantly improves the performance of sparse Kaczmarz and sparse LMS algorithms.