Efficient Optimization of Strongly Convex Functions with Linear Constraints using Accelerated Randomized Bregman-Kaczmarz Method

The authors propose an accelerated randomized Bregman-Kaczmarz method to efficiently solve linearly constrained optimization problems with strongly convex (possibly non-smooth) objective functions. They provide theoretical analysis showing linear convergence rates and demonstrate the superior efficiency of the proposed method compared to existing approaches.