Computational Methods for Sparse Solution of Linear Inverse Problems
The goal of the sparse approximation problem is to approximate a target signal using a linear combination of a few elementary signals drawn from a fixed collection. This paper surveys the major practical algorithms for sparse approximation. Specific attention is paid to computational issues, to the circumstances in which individual methods tend to perform well, and to the theoretical guarantees available. Many fundamental questions in electrical engineering, statistics, and applied mathematics can be posed as sparse approximation problems, making these algorithms versatile and relevant to a plethora of applications.
© 2010 IEEE. Manuscript received March 16, 2009; revised December 10, 2009; accepted February 11, 2010. Date of publication April 29, 2010; date of current version May 19, 2010. The work of J. A. Tropp was supported by the Office of Naval Research (ONR) under Grant N00014-08-1-2065. The work of S. J. Wright was supported by the National Science Foundation (NSF) under Grants CCF-0430504, DMS-0427689, CTS-0456694, CNS-0540147, and DMS-0914524.
Published - Tropp2010p10179P_Ieee.pdf