Bregman-Divergence-Based Arimoto-Blahut Algorithm

We generalize the generalized Arimoto-Blahut algorithm to a general function defined over Bregman-divergence system. In existing methods, when linear constraints are imposed, each iteration needs to solve a convex minimization. Exploiting our obtained algorithm, we propose a minimization-free-iteration algorithm. This algorithm can be applied to classical and quantum rate-distortion theory. We numerically apply our method to the derivation of the optimal conditional distribution in the rate-distortion theory.