Algorithms for large-scale sparse reconstruction

TitleAlgorithms for large-scale sparse reconstruction
Publication TypeConference
Year of Publication2008
AuthorsMichael P. Friedlander
Conference NameSINBAD 2008
KeywordsSINBAD, SLIM
Abstract

Many signal processing applications seek to approximate a signal as a linear combination of only a few elementary atoms drawn from a large collection. This is known as sparse reconstruction, and the theory of compressed sensing allows us to pose it as a structured convex optimization problem. I will discuss the role of duality in revealing some unexpected and useful properties of these problems, and will show how they can lead to practical, large-scale algorithms. I will also describe some applications of these algorithms.

URLhttps://slim.gatech.edu/Publications/Private/Conferences/SINBAD/2008/friedlander2008SINBADafl/friedlander2008SINBADafl.pdf
Citation Keyfriedlander2008SINBADafl