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A Branch and Bound Method for Stochastic Global Optimization
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In this article, a branch and bound method for solving stochastic global optimization problems is proposed, instead of deterministic bounds, which is based on the branch-and-bound method.Abstract:
A stochastic version of the branch and bound method is proposed for solving stochastic global optimization problems. The method, instead of deterministic bounds, uses stochastic upper and lower estimates of the optimal value of subproblems, to guide the partitioning process. Almost sure convergence of the method is proved and random accuracy estimates derived. Methods for constructing random bounds for stochastic global optimization problems are discussed. The theoretical considerations are illustrated with an example of a facility location problem.read more
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Book ChapterDOI
Global Optimization of Probabilities by the Stochastic Branch and Bound Method
TL;DR: In this article, the stochastic branch and bound method was extended to optimization problems with chance and expectation constraints, and they solved a problem of optimization of probabilities and a chance constrained programming problem with discrete decision variables.