# Bregman-divergence-based Arimoto-Blahut algorithm

arXiv: https://arxiv.org/abs/2408.05454 · HTML: https://mlsanity.com/paper/2408.05454/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2024.

## Built on by (most cited first, 2 of 2)

- [Structured quantum learning via em algorithm for Boltzmann machines](https://mlsanity.com/paper/2507.21569/) (2025)
- [Expectation-Maximization as a Spectrally Governed Relaxation Flow](https://mlsanity.com/paper/2605.07818/) (2026)

## Abstract (arXiv)

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.