Titlebook: Stochastic Programming; Numerical Techniques Kurt Marti,Peter Kall Book 1995 Springer-Verlag Berlin Heidelberg 1995 Stochastische Optimieru

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CUMB

Eviction

Strong Convexity and Directional Derivatives of Marginal Values in Two-Stage Stochastic ProgrammingTwo-stage stochastic programs with random right-hand side are considered. Verifiable sufficient conditions for the existence of second-order directional derivatives of marginal values are presented. The central role of the strong convexity of the expected recourse function as well as of a Lipschitz stability result for optimal sets is emphasized.

有限

Computer Support for Modeling in Stochastic Linear ProgrammingThe purpose of the paper is to discuss the modeling process in stochastic linear programming (SLP) and to point out the SLP-specific features of computer support to this process.

权宜之计

Statistical Characterization of Granular AssembliesOn the basis of experimental results a numerical model was elaborated to follow the changes of the microstructure of granular assemblies. The distributions of internal forces and displacements were not possible to determine so we tried to characterize the assemblies with the singles of neighbouring contacts.

slow-wave-sleep

On Boundary — Initial Value Problem for Linear Hyperbolic Thermoelasticity Equations with Control ofIn this paper we consider the boundary-initial value problem for linear hyperbolic thermoelasticity equations with control of temperature..The obstacles for temperature are formulated in the term of maximal monotone set. Existence and uniqueness of the solution of this problem are proved in Sobolev Spaces.

季雨

Stochastic Optimization Models for Machining OperationsIn the paper the deterministic optimization models are summarized first. In these models the main decision variables are the . and .. Then we treat tool life as a random parameter and select a probability model that defines the tool life variations. Finally stochastic optimization models are defined for determining machining operations.

CANT

ALLAY

On the Regularized Decomposition Method for Stochastic Programming Problemsed. The method uses a quadratic regularizing term to stabilize the master but is still finitely convergent. Its practical performance is illustrated with numerical results for large real world problems.

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