Quantized sub-Gaussian random matrices are still RIP!

I have always been intrigued by the fact that, in Compressed Sensing (CS), beyond Gaussian random matrices, a couple of other unstructured random matrices respecting, with high probability (whp), the Restricted Isometry Property (RIP) look like “quantized” version of the Gaussian case, i.

1000th visit and some Compressed Sensing "humour"

As detected by Igor Carron, this blog has reached its 1000th visit ! Well, perhaps it’s 1000th robot visit ;-) Yesterday I found some very funny (math) jokes on Bjørn’s maths blog about “How to catch a lion in the Sahara desert” with some … mathematical tools.

SPGL1 and TV: Answers from SPGL1 Authors

Following the writing of my previous post, which obtained various interesting comments (many thanks to Gabriel Peyré, Igor Carron and Pierre Vandergheynst), I sent a mail to Michael P. Friedlander and Ewout van den Berg to point them this article and possibly obtain their point of views.

SPGL1 and TV minimization?

Recently, I was using the SPGL1 toolbox to recover some “compressed sensed” images. As a reminder, SPGL1 implements the method described in “Probing the Pareto Frontier for basis pursuit solutions” of Michael P.

Matching Pursuit Before Computer Science

I have recently discovered some interesting references about techniques designed around 1938 which could, in my opinion, be considered as (a variant of) the Matching Pursuit algorithm. This is maybe something well known in the literature of the right scientific field, but here is anyway what I recently found.

First news

So, as many researchers in the world, I have just opened my own Science 2.0. blog : this “Le petit chercheur illustré”. An approximative English translation would be “The Illustrated (report of a) Small Researcher”.