Jort Gemmeke continues on providing us some information from SPARS11. Here is the latest refering to the last post about an implementation of the Approximate Message Passing (AMP). There is another, probably more sturdy and supported instance, the Generalized Approximate Message Passing (GAMP) MATLAB package:
Oops, missed this one somehow. Referred to in Volkan's oral presentation today.
From the Gamp wiki:
Overview: Generalized Approximate Message Passing (GAMP) is an approximate, but computationally efficient method for estimation problems withlinear mixing. In the linear mixing problem an unknown vector, , with independent components, is first passed through linear transform and then observed through a general probabilistic, componentwise measurement channel to yield a measurement vector . The problem is to estimate and from and . This problem arises in a range of applications including compressed sensing.Optimal solutions to linear mixing estimation problems are, in general, computationally intractable as the complexity of most brute force algorithms grows exponentially in the dimension of the vector . GAMP approximately performs the estimation through a Gaussian approximation of loopy belief propagation that reduces the vector-valued estimation problem to a sequence of scalar estimation problems on the components of the vectors and .This project is intended to develop the theory and applications of GAMP. We also provide open source MATLAB software for others who would like to experiment with the algorithm.
The contributors of this fine package include:
- Sundeep Rangan, NYU-Poly (main contact person)
- Alyson Fletcher, UC Berkeley
- Vivek Goyal, MIT
- Ulugbek Kamilov, EPFL
- Jason Parker, Ohio State
- Phil Schniter, Ohio State