Generalized Bounds for Convex Multistage Stochastic Programs
Kuhn, Daniel.
Generalized Bounds for Convex Multistage Stochastic Programs [electronic resource] / by Daniel Kuhn ; edited by M. Beckmann, H. P. Kȭnzi, G. Fandel, W. Trockel, A. Basile, A. Drexl, H. Dawid, K. Inderfurth, W. Kȭrsten, U. Schittko. - XII, 190 p. 21 illus. online resource. - Lecture Notes in Economics and Mathematical Systems, 548 0075-8442 ; . - Lecture Notes in Economics and Mathematical Systems, 548 .
Basic Theory of Stochastic Optimization -- Convex Stochastic Programs -- Barycentric Approximation Scheme -- Extensions -- Applications in the Power Industry -- Conclusions.
ZDB-2-SBE
This work was completed during my tenure as a scientific assistant and d- toral student at the Institute for Operations Research at the University of St. Gallen. During that time, I was involved in several industry projects in the field of power management, on the occasion of which I was repeatedly c- fronted with complex decision problems under uncertainty. Although usually hard to solve, I quickly learned to appreciate the benefit of stochastic progr- ming models and developed a strong interest in their theoretical properties. Motivated both by practical questions and theoretical concerns, I became p- ticularly interested in the art of finding tight bounds on the optimal value of a given model. The present work attempts to make a contribution to this important branch of stochastic optimization theory. In particular, it aims at extending some classical bounding methods to broader problem classes of practical relevance. This book was accepted as a doctoral thesis by the University of St. Gallen in June 2004.1 am particularly indebted to Prof. Dr. Karl Frauendorfer for - pervising my work. I am grateful for his kind support in many respects and the generous freedom I received to pursue my own ideas in research. My gratitude also goes to Prof. Dr. Georg Pflug, who agreed to co-chair the dissertation committee. With pleasure I express my appreciation for his encouragement and continuing interest in my work.
9783540269014
10.1007/b138260 doi
Economics.
Mathematical optimization.
Distribution (Probability theory).
Economics/Management Science.
Operations Research/Decision Theory.
Optimization.
Probability Theory and Stochastic Processes.
Economic Theory.
HD30.23
658.40301
Generalized Bounds for Convex Multistage Stochastic Programs [electronic resource] / by Daniel Kuhn ; edited by M. Beckmann, H. P. Kȭnzi, G. Fandel, W. Trockel, A. Basile, A. Drexl, H. Dawid, K. Inderfurth, W. Kȭrsten, U. Schittko. - XII, 190 p. 21 illus. online resource. - Lecture Notes in Economics and Mathematical Systems, 548 0075-8442 ; . - Lecture Notes in Economics and Mathematical Systems, 548 .
Basic Theory of Stochastic Optimization -- Convex Stochastic Programs -- Barycentric Approximation Scheme -- Extensions -- Applications in the Power Industry -- Conclusions.
ZDB-2-SBE
This work was completed during my tenure as a scientific assistant and d- toral student at the Institute for Operations Research at the University of St. Gallen. During that time, I was involved in several industry projects in the field of power management, on the occasion of which I was repeatedly c- fronted with complex decision problems under uncertainty. Although usually hard to solve, I quickly learned to appreciate the benefit of stochastic progr- ming models and developed a strong interest in their theoretical properties. Motivated both by practical questions and theoretical concerns, I became p- ticularly interested in the art of finding tight bounds on the optimal value of a given model. The present work attempts to make a contribution to this important branch of stochastic optimization theory. In particular, it aims at extending some classical bounding methods to broader problem classes of practical relevance. This book was accepted as a doctoral thesis by the University of St. Gallen in June 2004.1 am particularly indebted to Prof. Dr. Karl Frauendorfer for - pervising my work. I am grateful for his kind support in many respects and the generous freedom I received to pursue my own ideas in research. My gratitude also goes to Prof. Dr. Georg Pflug, who agreed to co-chair the dissertation committee. With pleasure I express my appreciation for his encouragement and continuing interest in my work.
9783540269014
10.1007/b138260 doi
Economics.
Mathematical optimization.
Distribution (Probability theory).
Economics/Management Science.
Operations Research/Decision Theory.
Optimization.
Probability Theory and Stochastic Processes.
Economic Theory.
HD30.23
658.40301