Distributed Optimization and Games: Local Interactions for Global Impact
Communication networks face challenges like congestion and energy consumption, prompting the need for distributed algorithms for optimization. Local interactions among agents in networks have a global effect, similar to mechanisms observed in biology and economy. Resources include book chapters, slides, and evaluation methods for learning and assessment. Techniques studied in this course offer solutions to complex problems in various domains.
Uploaded on | 0 Views
Download Presentation
Please find below an Image/Link to download the presentation.
The content on the website is provided AS IS for your information and personal use only. It may not be sold, licensed, or shared on other websites without obtaining consent from the author. If you encounter any issues during the download, it is possible that the publisher has removed the file from their server.
You are allowed to download the files provided on this website for personal or commercial use, subject to the condition that they are used lawfully. All files are the property of their respective owners.
The content on the website is provided AS IS for your information and personal use only. It may not be sold, licensed, or shared on other websites without obtaining consent from the author.
E N D
Presentation Transcript
Distributed Optimization and Games (DOG) Giovanni Neglia
Communication Networks Many global optimization problems Avoid congestion, minimize energy consumption, minimize file distribution time But centralized optimization is unfeasible How to engineer distributed algorithms where each agent contributes to solve the problem using only local information?
Local interactions among agents in a network have a global effect Biology Economy
Info http://www-sop.inria.fr/members/Giovanni.Neglia/dog16/ (also look at 2015 course to know what s coming) giovanni.neglia@inria.fr
Every lesson A short test (10-15 minutes) about the previous lesson Some specific examples/case studies take-home lessons Techniques/concepts to study similar problems
Resources (all available online!) 1) Book chapters Kelly&Yudovina, Stochastic networks Srinivas Shakkottai and R. Srikant, Network Optimization and Control, David Easley and Jon Kleinberg, Networks, Crowds, and Markets J. R. Marden and J.S. Shamma, Game Theory and Distributed Control 2) Slides 3) Your notes
Evaluation in-class closed-book tests, top 5 out of 6 marks will count for 15% of the final mark scribe notes, 1-person teams need to prepare only once, 2-person teams need to prepare twice (15% final mark) I wait for your teams and your dates Previous scribe notes available on github 1 homework to be delivered at week 5 (15% final mark) Final exam (55% final mark)
