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John Birge
University Talk
Decomposition Methods for Dynamic, Stochastic Optimization
October 17, 2006 12:15 pm
Abstract:
Many practical decision-making problems can be represented as discrete-time, dynamic stochastic optimization models or (multi-stage stochastic programs). In general, such problems are quite hard to solve, but certain special cases lead to efficient algorithms and approximations. In this talk, I will briefly describe the models, common applications, and complexity results. I will present in more detail, methods based on value-function approximations assuming convexity and serial independence of the data process and extensions to infinite-horizon problems.
If you have questions, or would like to meet the speaker, please contact Ponda at 4-1994 or pondabarnes@tti-c.org. For information on future TTI-C talks or events, please go to the TTI-C Events page.