From Contraction Theory to Fixed Point Algorithms on Riemannian and Non-Euclidean Spaces

Autor:	Bullo, Francesco, Cisneros-Velarde, Pedro, Davydov, Alexander, Jafarpour, Saber
Rok vydání:	2021
Předmět:	Mathematics - Optimization and Control Electrical Engineering and Systems Science - Systems and Control
Druh dokumentu:	Working Paper
DOI:	10.1109/CDC45484.2021.9682883
Popis:	The design of fixed point algorithms is at the heart of monotone operator theory, convex analysis, and of many modern optimization problems arising in machine learning and control. This tutorial reviews recent advances in understanding the relationship between Demidovich conditions, one-sided Lipschitz conditions, and contractivity theorems. We review the standard contraction theory on Euclidean spaces as well as little-known results for Riemannian manifolds. Special emphasis is placed on the setting of non-Euclidean norms and the recently introduced weak pairings for the $\ell_1$ and $\ell_\infty$ norms. We highlight recent results on explicit and implicit fixed point schemes for non-Euclidean contracting systems. Comment: Paper in the invited tutorial session "Contraction Theory for Machine Learning" at 60th IEEE Conference on Decision and Control, 2021
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2110.03623 Zobrazit plný text záznamu View this record from Arxiv