Online Convex Optimization Using Coordinate Descent Algorithms
arXiv:2201.10017 · doi:10.1016/j.automatica.2024.111681
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.
Accepted for publication in Automatica
References in corpus (9)
- An Online Convex Optimization Approach to Dynamic Network Resource Allocation
- Online Distributed Optimization on Dynamic Networks
- Improved Dynamic Regret for Non-degenerate Functions
- Regret and Cumulative Constraint Violation Analysis for Distributed Online Constrained Convex Optimization
- Second-order Online Nonconvex Optimization
- Distributed Online Optimization in Time-Varying Unbalanced Networks without Explicit Subgradients
- Setpoint Tracking with Partially Observed Loads
- Predictive Online Convex Optimization
- Adaptive Algorithms for Online Convex Optimization with Long-term Constraints