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Titlebook: Recent Results in Stochastic Programming; Proceedings, Oberwol Peter Kall,András Prékopa Conference proceedings 1980 Springer-Verlag Berlin

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11#
發(fā)表于 2025-3-23 11:47:12 | 只看該作者
Solving Complete Fixed Recourse Problems by Successive DiscretizationWe consider the recourse problem of stochastic linear programming ..
12#
發(fā)表于 2025-3-23 17:16:29 | 只看該作者
Computation of Multiple Normal Probabilitiesal normal probability distribution function. Conventional numerical methods and these Monte Carlo techniques are discussed and compared. The othononormalized estimators turn out to provide us with the most powerful technique that gives two digits accuracy in 1 sec for the 20 dimensional and in 10 sec’s for the 50 dimensional case.
13#
發(fā)表于 2025-3-23 20:19:07 | 只看該作者
14#
發(fā)表于 2025-3-23 23:39:09 | 只看該作者
Conditions for Optimality in Multi-Stage Stochastic Programming Problemsqualizing — show the essential difference between finite-stage and ∞-stage stochastic programs..Moreover, it is demonstrated how a recursive structure of the problem can give a reformulation of the conditions. These reformulated conditions may be used for the construction of numerical solution techniques.
15#
發(fā)表于 2025-3-24 04:04:02 | 只看該作者
Duality in Stochastic Programming Applied to the Design and Operation of Reservoirsr the years. In the search for an optimal capacity (a design problem) or an optimal operational plan, one always has to keep certain constraints in mind. This makes these reservoir problems a special class of stochastic programming problems.
16#
發(fā)表于 2025-3-24 09:25:18 | 只看該作者
Chance Constrained Inventory Model for an Asphalt Mixing Problemd the optimal hot depot capacities for which the probability of overflow, in a given time period, does not exceed a prescribed level. Chance constrained models are formulated with joint resp. individual probabilities.
17#
發(fā)表于 2025-3-24 12:29:50 | 只看該作者
18#
發(fā)表于 2025-3-24 15:08:01 | 只看該作者
Stochastic — Parametric Linear Programs IIwhich usually appear in the “data” of practical optimization problems: “objective” (random) indeterminacy and “conventional” (parametric) indeterminacy. The former originates in the inherent random nature of some of the coefficients of a linear program and is expressed in the model by allowing some
19#
發(fā)表于 2025-3-24 19:34:48 | 只看該作者
20#
發(fā)表于 2025-3-25 00:08:32 | 只看該作者
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