The goal of this course is giving the student a background in the methodologies in the advanced design of mechanisms using non-lineal convex optimization and game theory. The program will cover from basic concepts related to convexity, convex optimization problems, Game Theory, Nash Equilibria, to applications of these methodologies to networking such as resource allocation, back-pressure models, power-control models compressive sensing, game theory in wireless networks, pricing models in networks, game theory in routing problems or incentives in P2P systems.
Hores setmanals
Teoria
4
Problemes
0
Laboratori
0
Aprenentatge dirigit
0
Aprenentatge autònom
7
Competències
Competències Tècniques de cada especialitat
Xarxes de computadors i sistemes distribuïts
CEE2.1 - Capacitat per a entendre els models, problemes i algoritmes relacionats amb els sistemes distribuïts, així com poder dissenyar i avaluar algoritmes i sistemes que tractin la problemàtica de la distribució i ofereixin serveis distribuïts.
CEE2.2 - Capacitat d'entendre els models, problemes i algoritmes relacionats amb les xarxes de computadors, així com poder dissenyar i avaluar algoritmes, protocols i sistemes que tractin la problemàtica de la xarxes de comunicació entre computadors.
CEE2.3 - Capacitat d'entendre els models, problemes i eines matemàtiques que permeten analitzar, dissenyar i avaluar xarxes de computadors i sistemes distribuïts.
Competències Transversals
Raonament
CTR6 - Capacitat de raonament crític, lògic i matemàtic. Capacitat de resoldre problemes en la seva àrea d'estudi. Capacitat d'abstracció: capacitat de crear i utilitzar models que reflecteixin situacions reals. Capacitat de dissenyar i realitzar experiments senzills, i analitzar-ne i interpretar-ne els resultats. Capacitat d'anàlisi, de síntesi i d'avaluació.
Objectius
Capacity to formulate a convex optimization problem
Competències relacionades:
CTR6,
CEE2.3,
Capacity to apply convex optimization to networking problems.
Competències relacionades:
CTR6,
CEE2.1,
CEE2.2,
CEE2.3,
Capacity to understand what game theory is and how a game is solved.
Competències relacionades:
CTR6,
CEE2.3,
Capacity to apply game theory to networking problems
Competències relacionades:
CTR6,
CEE2.3,
Continguts
Convex Optimization basics
Convex sets, convex functions, convex optimization problems (COP) and duality (Lagrange dual function, KKT optimality conditions), methods for solving COP's (General Descent Methods, Interior Point Methods)
Convex Optimization Applications to networking
Exxamples on Resource allocation in networks, back-pressure, Power control, Publish-subscribe in DTN, Compressive Sensing.
Game Theory basics
Strategic and Extensive Forms of a Game, Non cooperative Games (Nash pure and mixed equilibria, correlated equilibria), Cooperative Games (core of a game, Shapley values, Nash Arbitration scheme), cost-sharing (Braess Paradox, Price of Anarchy and Stability), Auctions.
Game Theory Applications to Networking
Wireless Networking games, Energy-Efficient Power Control games, pricing, P2P games
During the initial sessions of each topic, the main results will be explained in the blackboard. the student will solve some exercises to prove their skills in the topic. Finally, there will be some sessions devoted to discuss in the classroom models taken from research papers that apply the related topics.
Mètode d'avaluació
The evaluation is based on different activities
- Short projects and presentations in which the student has to deliver and defend the obtained results (P)
- A final exam (FE)
Each of the activities will be evaluated (0=
The final mark for the course (FM) will be:
FM= 0.60xP+0.4xFE
Where P=(1/N) x Sum (Pi) with i=1,...N
with Pi the projects and oral presentation mark. There will be a minimum of 2 practical projects and 1 oral presentation.