Prediction in Online Convex Optimization for Parametrizable Objective Functions

Published

Journal Article

© 2019 IEEE. Many techniques for online optimization problems involve making decisions based solely on presently available information: fewer works take advantage of potential predictions. In this paper, we discuss the problem of online convex optimization for parametrizable objectives, i.e. optimization problems that depend solely on the value of a parameter at a given time. We introduce a new regularity for dynamic regret based on the accuracy of predicted values of the parameters and show that, under mild assumptions, accurate prediction can yield tighter bounds on dynamic regret. Inspired by recent advances on learning how to optimize, we also propose a novel algorithm to simultaneously predict and optimize for parametrizable objectives and study its performance using numerical experiments.

Full Text

Duke Authors

Cited Authors

  • Ravier, RJ; Calderbank, AR; Tarokh, V

Published Date

  • December 1, 2019

Published In

Volume / Issue

  • 2019-December /

Start / End Page

  • 2455 - 2460

International Standard Serial Number (ISSN)

  • 0743-1546

Digital Object Identifier (DOI)

  • 10.1109/CDC40024.2019.9030013

Citation Source

  • Scopus