arXiv Analytics

Sign in

arXiv:1909.05207 [cs.LG]AbstractReferencesReviewsResources

Introduction to Online Convex Optimization

Elad Hazan

Published 2019-09-07Version 1

This manuscript portrays optimization as a process. In many practical applications the environment is so complex that it is infeasible to lay out a comprehensive theoretical model and use classical algorithmic theory and mathematical optimization. It is necessary as well as beneficial to take a robust approach, by applying an optimization method that learns as one goes along, learning from experience as more aspects of the problem are observed. This view of optimization as a process has become prominent in varied fields and has led to some spectacular success in modeling and systems that are now part of our daily lives.

Comments: arXiv admin note: text overlap with arXiv:1909.03550
Categories: cs.LG, math.OC, stat.ML
Related articles: Most relevant | Search more
arXiv:2102.03803 [cs.LG] (Published 2021-02-07)
Lazy OCO: Online Convex Optimization on a Switching Budget
arXiv:2310.11880 [cs.LG] (Published 2023-10-18)
Online Convex Optimization with Switching Cost and Delayed Gradients
arXiv:1804.04529 [cs.LG] (Published 2018-04-12)
Online convex optimization and no-regret learning: Algorithms, guarantees and applications