When Francesca invited me to give a talk at Supelec this past week, I was reminded that I really would have liked to have seen an expository presentation of compressive sensing that was not using always the same figures. In particular, Compressive sensing is always presented as some sort of convex programming / L_1 minimization issue which is certainly interesting but that doesn't allow for an exposition of the fact that, nowadays, the use of belief propagation or greedy solvers is not entirely relevant to that picture anymore. After the presentation I made at Supelec this week, I added a few things that I felt I did not have time to talk about. Without further due, here is an "improved" version of that presentation.
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