Introduction to Online Convex Optimization
Machine Learning
2023-08-08 v3 Optimization and Control
Machine Learning
Abstract
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.
Cite
@article{arxiv.1909.05207,
title = {Introduction to Online Convex Optimization},
author = {Elad Hazan},
journal= {arXiv preprint arXiv:1909.05207},
year = {2023}
}
Comments
arXiv admin note: text overlap with arXiv:1909.03550