Second-Order Online Nonconvex Optimization

Antoine Lesage-Landry*, Joshua A. Taylor, Iman Shames

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)

Abstract

We present the online Newton's method, a single-step second-order method for online nonconvex optimization. We analyze its performance and obtain a dynamic regret bound that is linear in the cumulative variation between round optima. We show that if the variation between round optima is limited, the method leads to a constant regret bound. In the general case, the online Newton's method outperforms online convex optimization algorithms for convex functions and performs similarly to a specialized algorithm for strongly convex functions. We simulate the performance of the online Newton's method on a nonlinear, nonconvex moving target localization example and find that it outperforms a first-order approach.

Original languageEnglish
Pages (from-to)4866-4872
Number of pages7
JournalIEEE Transactions on Automatic Control
Volume66
Issue number10
DOIs
Publication statusPublished - Oct 2021
Externally publishedYes

Fingerprint

Dive into the research topics of 'Second-Order Online Nonconvex Optimization'. Together they form a unique fingerprint.

Cite this