Online Optimization in Dynamic Environments: Improved Regret Rates for Strongly Convex Problems

TL;DR

This paper improves online optimization methods for strongly convex functions in dynamic environments by deriving tighter regret bounds that depend on the parameter's rate of change, supported by numerical experiments.

Contribution

It introduces a new regret bound for online gradient descent in strongly convex settings that accounts for the path-length of the evolving parameter.

Findings

Regret bounds depend on the path-length of the parameter.

Proposed method outperforms existing approaches in dynamic scenarios.

Numerical experiments validate theoretical improvements.

Abstract

In this paper, we address tracking of a time-varying parameter with unknown dynamics. We formalize the problem as an instance of online optimization in a dynamic setting. Using online gradient descent, we propose a method that sequentially predicts the value of the parameter and in turn suffers a loss. The objective is to minimize the accumulation of losses over the time horizon, a notion that is termed dynamic regret. While existing methods focus on convex loss functions, we consider strongly convex functions so as to provide better guarantees of performance. We derive a regret bound that captures the path-length of the time-varying parameter, defined in terms of the distance between its consecutive values. In other words, the bound represents the natural connection of tracking quality to the rate of change of the parameter. We provide numerical experiments to complement our theoretical findings.