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. Link to a softcopy in pdf format, free of charge:



Link to a paperback:

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

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

List of errata since published version (Foundations and Trends version)

Contains original illustrations and artwork by Udi Aharoni



About:
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