Convex Feasibility Methods for Compressed Sensing

We present a computationally-efficient method for recovering sparse signals from a series of noisy observations, known as the problem of compressed sensing (CS). CS theory requires solving a convex constrained minimization problem. We propose to transform this optimization problem into a convex feasibility problem (CFP), and solve it using subgradient projection methods, which are iterative, fast, robust and convergent schemes for solving CFPs. As opposed to some of the recently-introduced CS algorithms, such as Bayesian CS and gradient projections for sparse reconstruction, which become inefficient as the problem dimension and sparseness degree increase, the newly-proposed methods exhibit a marked robustness with respect to these factors. This renders the subgradient projection methods highly viable for large-scale compressible scenarios.

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