Adaptive Composite Online Optimization: Predictions in Static and Dynamic Environments

• 成果类型: Article
• 署名作者: Scroccaro, Pedro Zattoni; Kolarijani, Arman Sharifi; Esfahani, Peyman Mohajerin
• 署名单位: Delft University of Technology
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2023.3237486
• 发表日期: 2023
• 页码: 2906-2921
• 关键词: Prediction algorithms; COSTS; Heuristic algorithms; STANDARDS; mirrors; Convex functions; predictive models; Composite costs; dynamic environments; online convex optimization (OCO); predictions; real-time control

摘要

In the past few years, online convex optimization (OCO) has received notable attention in the control literature thanks to its flexible real-time nature and powerful performance guarantees. In this article, we propose new step-size rules and OCO algorithms that simultaneously exploit gradient predictions, function predictions and dynamics, features particularly pertinent to control applications. The proposed algorithms enjoy static and dynamic regret bounds in terms of the dynamics of the reference action sequence, gradient prediction error, and function prediction error, which are generalizations of known regularity measures from the literature. We present results for both convex and strongly convex costs. We validate the performance of the proposed algorithms in a trajectory tracking case study, as well as portfolio optimization using real-world datasets.