Efficient Online Linear Optimization with Approximation Algorithms

TL;DR

This paper introduces new algorithms for online linear optimization that leverage approximation algorithms, achieving low regret with minimal oracle calls, applicable to NP-hard offline problems.

Contribution

It provides the first algorithms with poly-logarithmic oracle complexity and sublinear regret bounds for both full information and bandit settings in approximate online linear optimization.

Findings

Achieves $O(T^{-1/3})$ $oldsymbol{ ext{regret}}$ bounds.

Uses only $O( ext{log } T)$ oracle calls per iteration.

Applicable to NP-hard offline problems with efficient approximation algorithms.

Abstract

We revisit the problem of \textit{online linear optimization} in case the set of feasible actions is accessible through an approximated linear optimization oracle with a factor $\alpha$ multiplicative approximation guarantee. This setting is in particular interesting since it captures natural online extensions of well-studied \textit{offline} linear optimization problems which are NP-hard, yet admit efficient approximation algorithms. The goal here is to minimize the $\alpha$\textit{-regret} which is the natural extension of the standard \textit{regret} in \textit{online learning} to this setting. We present new algorithms with significantly improved oracle complexity for both the full information and bandit variants of the problem. Mainly, for both variants, we present $\alpha$-regret bounds of $O(T^{-1/3})$, were $T$ is the number of prediction rounds, using only $O(\log{T})$ calls to the approximation oracle per iteration, on average. These are the first results to obtain both average oracle complexity of $O(\log{T})$ (or even poly-logarithmic in $T$) and $\alpha$-regret bound $O(T^{-c})$ for a constant $c>0$, for both variants.