Adaptive Composite Online Optimization

Predictions in Static and Dynamic Environments

Journal article (2023)

Authors

P. Zattoni Scroccaro Team Peyman Mohajerin Esfahani - Mechanical, Maritime and Materials Engineering

Arman Sharifi K. Team Tamas Keviczky - Mechanical, Maritime and Materials Engineering

P. Mohajerin Esfahani Team Peyman Mohajerin Esfahani - Mechanical, Maritime and Materials Engineering

Research Group

Team Peyman Mohajerin Esfahani (Mechanical, Maritime and Materials Engineering) (TU Delft)

DOI: https://doi.org/10.1109/TAC.2023.3237486

Convex functions Real-time control Predictions Dynamic environments Heuristic algorithms Costs Predictive models Standards Mirrors Prediction algorithms Online convex optimization Composite costs

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http://resolver.tudelft.nl/uuid:ada86683-0518-4441-ac76-95124abbed87

Published Date

2023

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Delft Center for Systems and Control

Research Group

Team Peyman Mohajerin Esfahani

Abstract

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.

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