A sampling-based algorithm for two-stage stochastic programming
Conference
·
OSTI ID:36635
We describe an algorithm for two-stage stochastic linear programming with uncertainty in the right-hand-side. The algorithm draws on techniques from bounding and approximation methods as well as sampling-based schemes. In particular, we successively refine a partition of the sample space, and through Jensen`s inequality, generate deterministically valid lower bounds on the optimal objective function value. Upper bound estimates are formed via Monte Carlo sampling with the use of a control variates variance reduction scheme. The algorithm lends itself to a stopping rule theory that ensures an asymptotically valid confidence interval for the quality of the proposed solution. Preliminary computational results will be reported.
- OSTI ID:
- 36635
- Report Number(s):
- CONF-9408161--
- Country of Publication:
- United States
- Language:
- English
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