Wednesday, 16:15 - 16:40 h, Room: MA 141


Guzin Bayraksan
A sequential bounding method for a class of two-stage stochastic programs

Coauthors: David P. Morton, Peguy Pierre-Louis


In this talk, we present an algorithm for two-stage stochastic programming with a convex second stage program and with uncertainty in the right-hand side. The algorithm draws on techniques from deterministically-valid bounding and approximation methods as well as sampling-based approaches. In particular, we sequentially refine a partition of the support of the random vector and, through Jensen’s inequality, generate deterministically-valid lower bounds on the optimal objective function value. An upper bound estimator is formed through a stratified Monte Carlo sampling procedure that includes the use of a control variate variance reduction scheme. We present stopping rules that ensure an asymptotically valid confidence interval on the quality of the proposed solution and illustrate the algorithm via computational results.


Talk 3 of the invited session Wed.3.MA 141
"Algorithms and applications for stochastic programming" [...]
Cluster 22
"Stochastic optimization" [...]


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