FIXED-POINT CONTINUATION APPLIED TO COMPRESSED SENSING:IMPLEMENTATION AND NUMERICAL EXPERIMENTS  

作　　者：Elaine T.Hale 

机构地区：[1]Department of Computational and Applied Mathematics Rice University

出　　处：《Journal of Computational Mathematics》2010年第2期170-194,共25页计算数学（英文）

基　　金：supported by an NSF VIGRE grant (DMS-0240058);supported in part by NSF CAREER Award DMS-0748839 and ONR Grant N00014-08-1-1101;supported in part by NSF Grant DMS-0811188 and ONR Grant N00014-08-1-1101

摘　　要：Fixed-point continuation （FPC） is an approach, based on operator-splitting and continuation, for solving minimization problems with l1-regularization：min ｜｜x｜｜1＋uf（x）.We investigate the application of this algorithm to compressed sensing signal recovery, in which f（x） = 1/2｜｜Ax-b｜｜2M,A∈m×n and m≤n. In particular, we extend the original algorithm to obtain better practical results, derive appropriate choices for M and u under a given measurement model, and present numerical results for a variety of compressed sensing problems. The numerical results show that the performance of our algorithm compares favorably with that of several recently proposed algorithms.Fixed-point continuation （FPC） is an approach, based on operator-splitting and continuation, for solving minimization problems with l1-regularization：min ｜｜x｜｜1＋uf（x）.We investigate the application of this algorithm to compressed sensing signal recovery, in which f（x） = 1/2｜｜Ax-b｜｜2M,A∈m×n and m≤n. In particular, we extend the original algorithm to obtain better practical results, derive appropriate choices for M and u under a given measurement model, and present numerical results for a variety of compressed sensing problems. The numerical results show that the performance of our algorithm compares favorably with that of several recently proposed algorithms.

关 键 词：l1 regularization Fixed-point algorithm CONTINUATION Compressed sensing Numerical experiments. 

分 类 号：O241[理学—计算数学]