| Dynamic Stochastic Optimization 2004 Edition Contributor(s): Marti, Kurt (Editor), Ermoliev, Yuri (Editor), Pflug, Georg Ch (Editor) |
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ISBN: 3540405062 ISBN-13: 9783540405061 Publisher: Springer OUR PRICE: $104.49 Product Type: Paperback Published: October 2003 Annotation: This volume considers optimal stochastic decision processes from the viewpoint of stochastic programming. It focuses on theoretical properties and on approximate or numerical solution techniques for time-dependent optimization problems with random parameters (multistage stochastic programs, optimal stochastic decision processes). Methods for finding approximate solutions of probabilistic and expected cost based deterministic substitute problems are presented. Besides theoretical and numerical considerations, the proceedings volume contains selected refereed papers on many practical applications to economics and engineering: risk, risk management, portfolio management, finance, insurance-matters and control of robots.
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| Additional Information |
| BISAC Categories: - Gardening - Science | System Theory - Business & Economics | Operations Research |
| Dewey: 003 |
| LCCN: 2003058581 |
| Series: Lecture Notes in Economic and Mathematical Systems |
| Physical Information: 0.72" H x 6.14" W x 9.21" (1.07 lbs) 336 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: Uncertainties and changes are pervasive characteristics of modern systems involving interactions between humans, economics, nature and technology. These systems are often too complex to allow for precise evaluations and, as a result, the lack of proper management (control) may create significant risks. In order to develop robust strategies we need approaches which explic- itly deal with uncertainties, risks and changing conditions. One rather general approach is to characterize (explicitly or implicitly) uncertainties by objec- tive or subjective probabilities (measures of confidence or belief). This leads us to stochastic optimization problems which can rarely be solved by using the standard deterministic optimization and optimal control methods. In the stochastic optimization the accent is on problems with a large number of deci- sion and random variables, and consequently the focus ofattention is directed to efficient solution procedures rather than to (analytical) closed-form solu- tions. Objective and constraint functions of dynamic stochastic optimization problems have the form of multidimensional integrals of rather involved in- that may have a nonsmooth and even discontinuous character - the tegrands typical situation for "hit-or-miss" type of decision making problems involving irreversibility ofdecisions or/and abrupt changes ofthe system. In general, the exact evaluation of such functions (as is assumed in the standard optimization and control theory) is practically impossible. Also, the problem does not often possess the separability properties that allow to derive the standard in control theory recursive (Bellman) equations. |