The method advocates relaxing nonanticipativity requirements of the decisions up to a certain second-order aggregation to compute lower bounds. Then, this relaxation is refined progressively towards solving the stochastic program. Preliminary computational results are presented. The paper reviews the role of sensors as a variable depicting information in a stochastic program. Through examples, and with mathematical analysis of random measures, it is shown how policy variables affect the sensors in a given problem.
Sensors are amenable to mathematical derivations.
It is shown how the trade-off between the gain from introducing new information into the problem, and the cost of that action, can be addressed. Suggestions for further investigation are offered. Some advances in decomposition methods for stochastic linear programming Andrzej Ruszczynski Stochastic programming problems have very large dimension and characteristic structures which are tractable by decomposition. We review some new developments in cutting plane methods, augmented Lagrangian and splitting methods for linear multi-stage stochastic programming problems. Julia L. Higle Suvrajeet Sen Sampling and decomposition constitute two of the most successful approaches for addressing large-scale problems arising in statistics and optimization, respectively.