6 research outputs found
Compressed Sensing with Upscaled Vector Approximate Message Passing
Recently proposed Vector Approximate Message Passing (VAMP) demonstrates a
great reconstruction potential at solving compressed sensing related linear
inverse problems. VAMP provides high per-iteration improvement, can utilize
powerful denoisers like BM3D, has rigorously defined dynamics and is able to
recover signals sampled by highly undersampled and ill-conditioned linear
operators. Yet, its applicability is limited to relatively small problem sizes
due to necessity to compute the expensive LMMSE estimator at each iteration. In
this work we consider the problem of upscaling VAMP by utilizing Conjugate
Gradient (CG) to approximate the intractable LMMSE estimator and propose a
CG-VAMP algorithm that can efficiently recover large-scale data. We derive
evolution models of certain key parameters of CG-VAMP and use the theoretical
results to develop fast and practical tools for correcting, tuning and
accelerating the CG algorithm within CG-VAMP to preserve all the main
advantages of VAMP, while maintaining reasonable and controllable computational
cost of the algorithm
Decentralized Generalized Approximate Message-Passing for Tree-Structured Networks
Decentralized generalized approximate message-passing (GAMP) is proposed for
compressed sensing from distributed generalized linear measurements in a
tree-structured network. Consensus propagation is used to realize average
consensus required in GAMP via local communications between adjacent nodes.
Decentralized GAMP is applicable to all tree-structured networks that do not
necessarily have central nodes connected to all other nodes. State evolution is
used to analyze the asymptotic dynamics of decentralized GAMP for zero-mean
independent and identically distributed Gaussian sensing matrices. The state
evolution recursion for decentralized GAMP is proved to have the same fixed
points as that for centralized GAMP when homogeneous measurements with an
identical dimension in all nodes are considered. Furthermore, existing
long-memory proof strategy is used to prove that the state evolution recursion
for decentralized GAMP with the Bayes-optimal denoisers converges to a fixed
point. These results imply that the state evolution recursion for decentralized
GAMP with the Bayes-optimal denoisers converges to the Bayes-optimal fixed
point for the homogeneous measurements when the fixed point is unique.
Numerical results for decentralized GAMP are presented in the cases of linear
measurements and clipping. As examples of tree-structured networks, a
one-dimensional chain and a tree with no central nodes are considered.Comment: submitted to IEEE Trans. Inf. Theor