Lazy OCO: Online Convex Optimization on a Switching Budget

Uri Sherman, Tomer Koren

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


We study a variant of online convex optimization where the player is permitted to switch decisions at most S times in expectation throughout T rounds. Similar problems have been addressed in prior work for the discrete decision set setting, and more recently in the continuous setting but only with an adaptive adversary. In this work, we aim to fill the gap and present computationally efficient algorithms in the more prevalent oblivious setting, establishing a regret bound of O(T/S) for general convex losses and O˜(T/S2) for strongly convex losses. In addition, for stochastic i.i.d. losses, we present a simple algorithm that performs logT switches with only a multiplicative logT factor overhead in its regret in both the general and strongly convex settings. Finally, we complement our algorithms with lower bounds that match our upper bounds in some of the cases we consider.
Original languageEnglish
Title of host publicationProceedings of Thirty Fourth Conference on Learning Theory
EditorsMikhail Belkin, Samory Kpotufe
Number of pages17
StatePublished - 2021

Publication series

NameProceedings of Machine Learning Research
ISSN (Electronic)2640-3498


Dive into the research topics of 'Lazy OCO: Online Convex Optimization on a Switching Budget'. Together they form a unique fingerprint.

Cite this