Linear Convergence in Hilbert's Projective Metric for Computing Augustin Information and a Rényi Information Measure

Consider the problems of computing the Augustin information and a Rényi information measure of statistical independence, previously explored by Lapidoth and Pfister (IEEE Information Theory Workshop, 2018) and Tomamichel and Hayashi (IEEE Trans. Inf. Theory, 64(2):1064–1082, 2018). Both quantities are defined as solutions to optimization problems and lack closed-form expressions. This paper analyzes two iterative algorithms: Augustin’s fixed-point iteration for computing the Augustin information, and the algorithm by Kamatsuka et al. (arXiv:2404.10950) for the Rényi information measure. Previously, it was only known that these algorithms converge asymptotically. We establish the linear convergence of Augustin’s algorithm for the Augustin information of order $\alpha \in (1\mathrm{/}2,1)\cup (1,3\mathrm{/}2)$ and Kamatsuka et al.'s algorithm for the Rényi information measure of order $\alpha \in [1\mathrm{/}2,1)\cup (1,\mathrm{\infty })$, using Hilbert’s projective metric.