Estimation with random linear mixing, belief propagation and compressed sensing

We apply Guo and Wang's relaxed belief propagation (BP) method to the estimation of a random vector from linear measurements followed by a componentwise probabilistic measurement channel. The relaxed BP method is a Gaussian approximation of standard BP that offers significant computational savings for dense measurement matrices. The main contribution of this paper is to extend Guo and Wang's relaxed BP method and analysis to general (non-AWGN) output channels. Specifically, we present detailed equations for implementing relaxed BP for general channels and show that the relaxed BP has an identical asymptotic large sparse limit behavior as standard BP as predicted by the Guo and Wang's state evolution (SE) equations. Applications are presented to compressed sensing and estimation with bounded noise.