Imperial College London > Talks@ee.imperial > COMMSP Seminar > Compressive Sensing via L1-minimization: Theory, Models, and Algorithms

Compressive Sensing via L1-minimization: Theory, Models, and Algorithms

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Compressive Sensing (CS) is an emerging methodology in signal and image processing that utilizes sparsity in signal representations to reduce the number of linear measurements needed for signal encoding. In CS, L1-minimization plays a central role in signal decoding. So far, the theory of CS has largely been built on the notion of Restricted Isometry Property or RIP , which unfortunately does not preserve a fundamental row-transformation invariance. We present a non-RIP analysis, including some new extensions, that preserve this invariance. We also discuss the choice of L1-models under practical and noisy environments. Finally, we introduce a 3-line algorithm, derived from the classic alternating direction method approach, that can efficiently solve six different L1 models in CS.

( A Matlab package for solving six L1-minimization models is available at: )

BIOGRAPHY : Yin Zhang is a professor of Computational and Applied Mathematics at Rice University in Houston, Texas, USA . He received his PhD degree in Applied Mathematics from the State University of New York at Stony Brook in 1987. He served as an associate editor for SIAM Journal of Optimization, Journal (1996-2005), Journal of Optimization Theory and Applications (2000 – present), Journal of Computational Mathematics (2007 – present), and Mathematical Programming Computation (2008 – present).

This talk is part of the COMMSP Seminar series.

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