Stochastic Programming - András Prékopa

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Résumé :
Stochastic programming - the science that provides us with tools to design and control stochastic systems with the aid of mathematical programming techniques - lies at the intersection of statistics and mathematical programming. The book Stochastic Programming is a comprehensive introduction to the field and its basic mathematical tools. While the mathematics is of a high level, the developed models offer powerful applications, as revealed by the large number of examples presented. The material ranges form basic linear programming to algorithmic solutions of sophisticated systems problems and applications in water resources and power systems, shipbuilding, inventory control, etc. Audience: Students and researchers who need to solve practical and theoretical problems in operations research, mathematics, statistics, engineering, economics, insurance, finance, biology and environmental protection.

Biographie:
Prof. Andr?s Pr?kopa is a Professor at Rutgers University in the Department of Statistics. Prof. J?nos Mayer is a Professor at University of Zurich in the Department of Business Adminstration. Prof. Be?ta Strazicky is currently retired but was a Professor at Szent Istv?n University. Prof. Istv?n De?k is a Professor at Corvinus University of Budapest in the Department of Computer Science. J?nos Hoffer works at Allianz Hung?ria Insurance Company in the IT Quality Assurance Section. ?goston N?meth works in Ex-Lh Ltd. B?la Potecz is retired but previously worked for?MAVIR Hungarian Independent Transmission Operator Company Ltd....

Sommaire:
1. General Theory of Linear Programming. 2. Convex Polyhedra. 3. Special Problems and Methods. 4. Logconcave and Quasi-Concave Measures. 5. Moment Problems. 6. Bounding and Approximation of Probabilities. 7. Statistical Decisions. 8. Static Stochastic Programming Models. 9. Solutions of the Simple Recourse Problem. 10. Convexity Theory of Probabilistic Constrained Problems. 11. Programming under Probabilistic Constraint and Maximizing Probabilities under Constraints. 12. Two-Stage Stochastic Programming Problems. 13. Multi-Stage Stochastic Programming Problems. 14. Special Cases and Selected Applications. 15. Distribution Problems. The Multivariate Normal Distribution.