Work

Online convex optimization using coordinate descent algorithms

Year: 2024

Type: article

Abstract: This paper considers the problem of online optimization where the objective function is time-varying. In particular, we extend coordinate descent type algorithms to the online case, where the objective function varies after a finite number of iterations of the algorithm. Instead of solving the problem exactly at each time step, we only apply a finite number of iterations at each time step. Commonly used notions of regret are used to measure the performance of the online algorithm. Moreover, coordinate descent algorithms with different updating rules are considered, including both deterministic and stochastic rules that are developed in the literature of classical offline optimization. A thorough regret analysis is given for each case. Finally, numerical simulations are provided to illustrate the theoretical results.

Source: Automatica

DOI: 10.1016/j.automatica.2024.111681

Authors: Yankai Lin, Iman Shames, Dragan Nešić

Institutions: Eindhoven University of Technology, Australian National University, The University of Melbourne

Language: English

Topic: Advanced Bandit Algorithms Research

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: hybrid

Estimated APC paid: $3,610

Funders: Université de Lorraine, Australian Government, Multidisciplinary University Research Initiative, Australian Research Council