Sequential Importance Sampling Algorithms for Dynamic Stochastic Programming
DOI: 10.1007/S10958-006-0058-1
关键词: Context (language use) 、 Stochastic programming 、 Node (circuits) 、 Mathematics 、 Algorithm 、 Importance sampling 、 Uniform norm 、 Expected value of perfect information 、 Conditional expectation 、 Mathematical optimization 、 Gibbs sampling
摘要: This paper gives a comprehensive treatment of EVPI-based sequential importance sampling algorithms for dynamic (multistage) stochastic programming problems. Both theory and computational are discussed. Under general assumptions it is shown that both an expected value perfect information (EVPI) process the corresponding marginal EVPI (the supremum norm conditional expectation its generalized derivative) nonanticipative nonnegative supermartingales. These processes used as criteria in class treated paper. When their values negligible at node current sample problem scenario tree, scenarios descending from replaced by single next iteration. On other hand, high lead to increasing number node. small asymptotic properties estimates arising established, former evaluated numerically context financial planning problem. Finally, future research described. Bibliography: 49 titles.
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