Linear Convergence in Hilbert's Projective Metric for Computing Augustin Information and a R\'enyi Information Measure
<|reference_start|>Linear Convergence in Hilbert's Projective Metric for Computing Augustin Information and a R\'enyi Information Measure: Consider the problems of computing the Augustin information and a R\'{e}nyi 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 R\'{e}nyi 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 $\alpha \in (1/2, 1) \cup (1, 3/2)$ and Kamatsuka et al.'s algorithm for the R\'{e}nyi information measure of order $\alpha \in [1/2, 1) \cup (1, \infty)$, using Hilbert's projective metric.<|reference_end|>
@article{tsai2024linear,
    title={Linear Convergence in Hilbert's Projective Metric for Computing Augustin
  Information and a R\'{e}nyi Information Measure},
    author={Chung-En Tsai and Guan-Ren Wang and Hao-Chung Cheng and Yen-Huan Li},
    journal={arXiv preprint arXiv:2409.02640},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.02640},
    primaryClass={math.OC cs.IT math.IT}
}