I. Dassios K. Fountoulakis and J. Gondzio
In this paper we are interested in the solution of Compressed Sensing (CS) problems where the signals to be recovered are sparse in coherent and redundant dictionaries. CS problems of this type are convex with non-smooth and non-separable regularization term, therefore a specialized solver is required. We propose a primal-dual Newton Conjugate Gradients (pdNCG) method. We prove global convergence and fast local rate of convergence for pdNCG. Moreover, well-known properties of CS problems are exploited for the development of provably effective preconditioning techniques that speed-up the approximate solution of linear systems which arise. Numerical results are presented on CS problems which demonstrate the performance of pdNCG compared to a state-of-the-art existing solver.
Key words: compressed sensing, L1-regularization, total variation, second-order methods, perturbation analysis.