Algorithms for sparse reconstruction

Event Sponsor: 
LANS Informal Seminar
Start Date: 
Sep 24 2008 (All day)
Building/Room: 
Building 221, Room A-261
Location: 
Argonne National Laboratory
Speaker(s): 
Michael Friedlander
Speaker(s) Title: 
University of British Columbia
Host: 

Many imaging and compressed-sensing 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. The basis pursuit (BP) approach minimizes the 1-norm of the solution, and the BP denoising (BPDN) approach balances it against the least-squares fit. 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.

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