Online Convex Optimization
Graduate text in machine learning and optimization
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
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.