Eftychios Pnevmatikakis: Dec 5

Tomorrow at 1PM I'm going to present some overview of the recent work on approximate message passing algorithms (AMP) with applications to compressed sensing (CS). 

I'm going to start with a brief overview of message passing algorithms [1] and then show how it was used in [2] to derive an AMP algorithm for the standard CS setup (basis pursuit, lasso). 

The time permitting I'm going to briefly present some extensions of this methodology to the case of more general graphical models [3]. 

Material will be drawn from the following sources:

[1] Kschischang, Frank R., Brendan J. Frey, and H-A. Loeliger. "Factor graphs and the sum-product algorithm." Information Theory, IEEE Transactions on 47.2 (2001): 498-519.

[2] Donoho, David L., Arian Maleki, and Andrea Montanari. "Message-passing algorithms for compressed sensing." Proceedings of the National Academy of Sciences 106.45 (2009): 18914-18919.

[3] Rangan, Sundeep, et al. "Hybrid approximate message passing with applications to structured sparsity." arXiv preprint arXiv:1111.2581 (2011).