Introduction to
Online Convex Optimization

Graduate text in machine learning and optimization

Versions & history

Upcoming version 2.0, to be published with MIT Press, has new chapters, expanded exposition on optimization, and expanded teaching materials.

The first version was published as a survey in the Foundation and Trends series. The most complete version, including errata by various contributors, is the arxiv version.
You
can also purchase a paperback on Amazon. (depreciated in anticipation for V2.0)

This graduate text arose from lectures given at the Technion, 2010-2014. It developed further in Princeton University, and served as the basis for a graduate-level course in machine learning and optimization. It is and always will be available free of charge as a contribution to the scientific community.

Abstract:

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.

Contains original illustrations and artwork by Udi Aharoni