Computational Complexity versus Statistical Performance on sparse recovery problems
TITLE: Computational Complexity versus Statistical Performance on sparse recovery problems.
AUTHORS: Vincent Roulet, Nicolas Boumal, Alexandre d'Aspremont
ABSTRACT: We show that several classical quantities controlling compressed sensing performance directly match classical parameters controlling algorithmic complexity. We first describe linearly convergent restart schemes on firstorder methods solving a broad range of compressed sensing problems, where sharpness at the optimum controls convergence speed. We show that for sparse recovery problems, this sharpness can be written as a condition number, given by the ratio between true signal sparsity and the largest signal size that can be recovered by the observation matrix. In a similar vein, Renegar's condition number is a datadriven complexity measure for convex programs, generalizing classical condition numbers for linear systems. We show that for a broad class of compressed sensing problems, the worst case value of this algorithmic complexity measure taken over all signals matches the restricted singular value of the observation matrix which controls robust recovery performance. Overall, this means in both cases that, in compressed sensing problems, a single parameter directly controls both computational complexity and recovery performance. Numerical experiments illustrate these points using several classical algorithms.
STATUS: Submitted.
ArXiv PREPRINT: ArXiv 1506.03295
PAPER: Computational Complexity versus Statistical Performance on sparse recovery problems in pdf.
NOTE: An earlier subset of these results were published under the title “Renegar's Condition Number and Compressed Sensing Performance”
