ACT Seminar - Evdokia Nikolova/Texas A&M University, CSE Dept., "Risk-averse Combinatorial Optimization," ACES 3.408
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Type o
f Talk: ACT Seminar
Speaker/Affiliation: Evdokia Nikolova/Texas A&M Un
iversity
Talk Audience: UTCS Faculty, Graduate Students, Undergradua
te Students, and Outside Interested Parties
Date/Time: Friday, April
27, 2012, 11:00 am
Location: ACES 3.408
Host: Vijaya Ramachand
ran
Talk Title: Risk-averse Combinatorial Optimization
Talk Abstr
act:
Optimization has played a key role in making the task of decision ma
king from art to science in the past century. An important challenge that s
till remains is our ability to incorporate the uncertainty in our knowledge
and risk-aversion in our objective. A simple but insightful example of thi
s is encapsulated in the decision question: given a number of route choices
, which shall I choose? Interestingly, this simple question (easily solva
ble in a deterministic setting) becomes highly non-trivial when we incorpor
ate the uncertainty of delays and the individual''s risk-aversion. This pri
marily stems from the combinatorial nature of the problem coupled with the
non-convexity of the objective.
In this talk I explain how to solve th
is reliable route planning problem, and mention how its solution has been
adapted in the MIT CarTel system for routing, which incorporates real traf
fic information (cartel.csail.mit.edu). I then show how the solution extend
s to a general framework of risk-averse combinatorial optimization, for wh
ich I present exact and approximation algorithms. These general-purpose alg
orithms can also cope with combinatorial problems that are NP-hard, whose
deterministic versions we only know how to approximate. At the end, I tou
ch upon how the risk-averse framework provides a foundation for studying eq
uilibria in stochastic network games.
Speaker Bio:
Evdokia Nikolo
va is an Assistant Professor at the Computer Science & Engineering Departme
nt at Texas A&M University. Previously she was a postdoctoral associate in
the Computer Science and Artificial Intelligence Laboratory at MIT. She gra
duated with a BA in Applied Mathematics with Economics from Harvard Univers
ity, MS in Mathematics from Cambridge University, U.K. and Ph.D. in Compu
ter Science from MIT. She is interested in algorithms arising in stochastic
optimization, networks and economics with applications to complex systems
.
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