Linear convergence of the randomized sparse Kaczmarz method

The randomized version of the Kaczmarz method for the solution of consistent linear systems is known to converge linearly in expectation. And even in the possibly inconsistent case, when only noisy data is given, the iterates are expected to reach an error threshold in the order of the noise-level with the same rate as in the noiseless case. In this work we show that the same also holds for the iterates of the recently proposed randomized sparse Kaczmarz method for recovery of sparse solutions. Furthermore we consider the more general setting of convex feasibility problems and their solution by the method of randomized Bregman projections. This is motivated by the observation that, similarly to the Kaczmarz method, the Sparse Kaczmarz method can also be interpreted as an iterative Bregman projection method to solve a convex feasibility problem. We obtain expected sublinear rates for Bregman projections with respect to a general strongly convex function. Moreover, even linear rates are expected for Bregman projections with respect to smooth or piecewise linear-quadratic functions, and also the regularized nuclear norm, which is used in the area of low rank matrix problems.