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Generalized Bounds for Convex Multistage Stochastic Programs

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This book investigates convex multistage stochastic programs whose objective and constraint functions exhibit a generalized nonconvex dependence on the random parameters. Although the classical Jensen and Edmundson-Madansky type bounds or their extensions are generally not available for such problems, tight bounds can systematically be constructed under mild regularity conditions. A distinct primal-dual symmetry property is revealed when the proposed bounding method is applied to linear stochastic programs. Exemplary applications are studied to assess the performance of the theoretical concepts in situations of practical relevance. It is shown how market power, lognormal stochastic processes, and risk-aversion can be properly handled in a stochastic programming framework. Numerical experiments show that the relative gap between the bounds can typically be reduced to a few percent at reasonable problem dimensions.
   
type book (English)
   
keywords Approximation Technique, Convex Multistage Stochastic Program, Nonconvexities, Numerical Solution, Regularization
   
date of appearance 2005
publisher Springer-Verlag (Berlin, DE)
series title Lecture Notes in Economics and Mathematical Systems (548)
ISBN 3-540-22540-4
page(s) 190
citation Kuhn, D. (2005). Generalized Bounds for Convex Multistage Stochastic Programs. Berlin, DE: Springer-Verlag. - ISBN 3-540-22540-4.