A branch and bound method for stochastic global optimization

Vladimir I. Norkin, Georg Ch Pflug, Andrzej Ruszczyński

Research output: Contribution to journalArticlepeer-review

209 Scopus citations


A stochastic branch and bound method for solving stochastic global optimization problems is proposed. As in the deterministic case, the feasible set is partitioned into compact subsets. To guide the partitioning process the method uses stochastic upper and lower estimates of the optimal value of the objective function in each subset. Convergence of the method is proved and random accuracy estimates derived. Methods for constructing stochastic upper and lower bounds are discussed. The theoretical considerations are illustrated with an example of a facility location problem.

Original languageEnglish (US)
Pages (from-to)425-450
Number of pages26
JournalMathematical Programming, Series B
Issue number3
StatePublished - Nov 1 1998

All Science Journal Classification (ASJC) codes

  • Software
  • Mathematics(all)


  • Branch and bound method
  • Facility location
  • Global optimization
  • Stochastic programming


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