  The Stochastic Primal-Dual Hybrid Gradient (SPDHG) was proposed by Chambolle
et al. (2018) and is an efficient algorithm to solve some nonsmooth large-scale
optimization problems. In this paper we prove its almost sure convergence for
convex but not necessarily strongly convex functionals. We also look into its
application to parallel Magnetic Resonance Imaging reconstruction in order to
test performance of SPDHG. Our numerical results show that for a range of
settings SPDHG converges significantly faster than its deterministic
counterpart.
