ASPICS:
Applying Statistical Physics to Inference in Compressed Sensing
This page contains codes, papers and data on the algorithms we have
been developing using statistical physics for inference in
interdisciplinary applications, with a special focus on compressed
sensing.
* February 2012: A MATLAB
implementation of our compressed sensing solver
We now also have
a fast MATLAB implementation of our algorithm. We are
still fine-tuning it, but if you can't wait, go ahead
and download the curent version. We would be more
than happy to receive comments and suggestions.
Our paper on a new (and powerful) approach to compressed sensing is
here . If you want to
try our algorithm, here is a c++
implementation . We have also a a
python implementation if you prefer. The fastest implementation
is the MATLAB one (see the note up there...)
We have been asked
(between other things) to give the data on the spinodal transition
that marks the limit of the performance of the EM-BP algorithm for
Gauss-Bernoulli signals: here they are.
When using a (non-structured) random matrix and a Gauss-Bernoulli
signal, this marks the limit of efficiency of sampling algorithm
such as Belief-Propagation, and it may be a limit for many other
approaches as well. This limit, however, is already beyond the
Donoho-Tanner transition. Of course, our seeding strategy allows
to break this limit. For reproducible research these are the data and command file we have
used in our study, which we give if you want to reproduce our
Figure.1 using the c++ code.
