3 research outputs found
Adaptive Algorithm for Sparse Signal Recovery
Spike and slab priors play a key role in inducing sparsity for sparse signal
recovery. The use of such priors results in hard non-convex and mixed integer
programming problems. Most of the existing algorithms to solve the optimization
problems involve either simplifying assumptions, relaxations or high
computational expenses. We propose a new adaptive alternating direction method
of multipliers (AADMM) algorithm to directly solve the presented optimization
problem. The algorithm is based on the one-to-one mapping property of the
support and non-zero element of the signal. At each step of the algorithm, we
update the support by either adding an index to it or removing an index from it
and use the alternating direction method of multipliers to recover the signal
corresponding to the updated support. Experiments on synthetic data and
real-world images show that the proposed AADMM algorithm provides superior
performance and is computationally cheaper, compared to the recently developed
iterative convex refinement (ICR) algorithm