The purpose of this course is to introduce students to techniques for distributed optimization in networks, also in the case when agents have different and conflicting utilities.
Teacher: Giovanni Neglia
Frank Kelly and Elena Yudovina, Stochastic networks, freely available here,
Srinivas Shakkottai and R. Srikant, Network Optimization and Control, available here,
David Easley and Jon Kleinberg, a preprint is available here,
J. R. Marden and J.S. Shamma, Game Theory and Distributed Control, Handbook of Game Theory Vol. 4, edited by Peyton Young and Shmuel Zamir, Elsevier Science, 2013, a preprint is available here,
Evaluation: 20% classwork (a 10 minutes test at every lesson, only 5 best marks will be considered), 20% homework to be delivered at week 4, 60% final exam. Marks are here (please send me an email with the pseudonym you want to be referred by).
You can freely use the slides below for your presentations, but I would like to be informed and please acknowledge the source in your presentation. Any comment is welcome.
First lesson (December 20, 2017): introduction to the course [pptx], an electrical example (Kelly&Yudovina, section 4.1). Scribe notes from previous year [pdf]. Crash course on Lagrange multipliers (A gentle introduction to the interpretation of Lagrange multipliers).
Second lesson (January 10, 2018): crash course on convex optimization. Road traffic models (Kelly&Yudovina, section 4.2). Scribe notes from previous year [pdf1], [pdf2].
Third lesson (January 11, 2017): Network Utility Maximization (NUM), dynamics of the distributed approach to capacity sharing (Kelly&Yudovina, section 7.3), what is TCP doing (Kelly&Yudovina, sections 7.4 and 7.5). Scribe notes from previous year [pdf1].