Titlebook: Stochastic Programming Methods and Technical Applications; Proceedings of the 3 Kurt Marti,Peter Kall Conference proceedings 1998 Springer-

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Bounds for and Approximations to Stochastic Linear Programs with Recourse — Tutorial —ient properties under mild assumptions (like e.g. convexity, smoothness), causes difficulties in computational solution procedures. Therefore we usually replace .(•) by successively improved lower and upper bounding functions more amenable to optimization procedures, the involved bounding functions

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Optimal Power Generation under Uncertainty via Stochastic Programmingtric power under uncertain load are introduced: (i) a dynamic model for the short-term operation and (ii) a power production planning model. In both cases the presence of stochastic data in the optimization model leads to multi-stage and two-stage stochastic programs respectively. Both stochastic pr

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Position and Controller Optimization for Robotic Parts Mating strains and ensure successful completion without damage of the contacting bodies. On the other hand, the dynamic behavior of a manipulator depends strongly on its position and the gains of its joint controllers. Hence, varying these parameters for an optimized performance during manipulation seems

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A Note on Multifunctions in Stochastic Programmingng on a probability measure. Surely, the probability measure can be considered as a parameter of such problems and, moreover, it is reasonable to investigate stability with respect to it. However, to investigate the stability of the above mentioned problems it means, mostly, to investigate simultane

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