Combinatorial Optimization under Uncertainty
Seminar for Bachelor and Master Students
|Time & Room||Wed 12-14 in 02.04.011|
TopicsThe theory of combinatorial optimization provides powerful tools for modeling, understanding, and solving optimization problems that arise in all kinds of applications. A characteristic feature of many real-world applications is a certain degree of uncertainty regarding the problem data. Such uncertain problem data may be stochastic parameters or incrementally revealed online information.
The purpose of this seminar is to read and understand recent results from the literature on combinatorial optimization under uncertainty and related topics. This includes topics from online optimization, stochastic optimization and robustness models.
PrerequisitesParticipants are expected to have good knowledge of standard techniques and algorithms in the area of linear and combinatorial optimization. Ideally, they participated in the courses combinatorial Optimization MA4502 and/or Discrete Optimization MA3502.
ScheduleWe have the following (tentative) schedule. We begin always at 12:30.
|14.10.15||Kick-Off Meeting: 5-minute presentations|
|28.10.15||Online Dial-a-Ride: Lucian Riediger|
|04.11.15||Robust Randomized Matchings: Michael Heptner|
|11.11.15||Stochastic Scheduling I: Martin Sperr|
|18.11.15||Robust Network Flows: Dominik Bruckner|
|25.11.15||Stochastic Scheduling II: Lukáš Folwarczný|
|09.12.15||Online Replacement Path Problem: Veronika Kreuzer|
|16.12.15||Stochastic Knapsack: Jonathan Roth|
|20.01.16||Online Appointment Scheduling: Raphael Ullmann|