Tuesday, May 21
8:00-10:00 AM
Salon B

MS17
Approximation and Computation in Stochastic Programming
(Invited Minisymposium)

Stochastic programming is a useful tool for planning under uncertainty. Applications may be found in finance, energy systems, transportation, capital expansion, inventory management, scheduling, to name a few. The mathematical technology is based on large scale optimization, linear or quadratic programming, with special techniques for exploiting problem structure and parallelism. The basic difficulties in the field concern the approximation of the probability distribution and the solution of the resulting large scale problem. Applications have been slow to develop due to the complexity of the problem the method is addressing (multiple stage decisions under uncertainty). But in the fields most accustomed to coping with risk and uncertainty, like finance, there is a growing conviction that this is the only technology that can address all the issues. The speakers in the symposium will discuss issues of approximation and computation unique to the field.

Organizer: Alan J. King
IBM T.J. Watson Research Center

Stochastic Programming Models in Practice
John R. Birge, University of Michigan
Epigraphical Limit Laws in Stochatic Programming
Lisa Dorf, University of California, Davis
Density Estimation: A Bayesian Approach
Michael X. Dong, University of California, Davis
Simulating Empirical Distributions for Multistage Stochastic Programs
Alan J. King, Organizer

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LMH, 3/15/96