Online Convex Optimization
Graduate text in machine learning and optimization
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 @ 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.