Introduction to
Online Convex Optimization

Graduate text in machine learning and optimization

Elad Hazan


Current version: Apr 10 2016      First version: Oct 6 2014.

Draft of a course-book that arose from lectures given at the Technion, 2010-2014.


This is an Internet draft. Some chapters are more finished than others. References and attributions are very preliminary, our apologies in advance for any omissions (but please do point them out to us)
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

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