Introduction to
Online Convex Optimization

Graduate text in machine learning and optimization

Elad Hazan

Current version: Sept 5 2016      First version: Oct 6 2014.

A course-book that arose from lectures given at the Technion, 2010-2014.

This version was published as a survey in the Foundation and Trends series.
Please send bugs, typos, missing references or general comments to
ehazan @ - thank you!

Not to be reproduced or distributed without the author's permission

Contains original illustrations and artwork by Udi Aharoni

This manuscript concerns the view of optimization as a process. In many practical applications the environment is so complex that it is infeasible to lay a comprehensive theoretical model and use classical algorithmic theory and mathematical optimization. It is necessary as well as beneficial to take a robust approach: apply 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 led to some spectacular success in modeling and systems that are now part of our daily lives.

datasets for exercises