| Stochastic Optimization: Algorithms and Applications 2001 Edition Contributor(s): Uryasev, Stanislav (Editor), Pardalos, Panos M. (Editor) |
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ISBN: 0792369513 ISBN-13: 9780792369516 Publisher: Springer OUR PRICE: $208.99 Product Type: Hardcover - Other Formats Published: May 2001 Annotation: Stochastic programming is the study of procedures for decision making under the presence of uncertainties and risks. Stochastic programming approaches have been successfully used in a number of areas such as energy and production planning, telecommunications, and transportation. Recently, the practical experience gained in stochastic programming has been expanded to a much larger spectrum of applications including financial modeling, risk management, and probabilistic risk analysis. Major topics in this volume include: (1) advances in theory and implementation of stochastic programming algorithms; (2) sensitivity analysis of stochastic systems; (3) stochastic programming applications and other related topics. Audience: Researchers and academies working in optimization, computer modeling, operations research and financial engineering. The book is appropriate as supplementary reading in courses on optimization and financial engineering. |
| Additional Information |
| BISAC Categories: - Mathematics | Game Theory - Mathematics | Linear & Nonlinear Programming - Computers | Computer Science |
| Dewey: 519.3 |
| LCCN: 2001029572 |
| Series: Applied Optimization |
| Physical Information: 1" H x 6.14" W x 9.21" (1.78 lbs) 435 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: Stochastic programming is the study of procedures for decision making under the presence of uncertainties and risks. Stochastic programming approaches have been successfully used in a number of areas such as energy and production planning, telecommunications, and transportation. Recently, the practical experience gained in stochastic programming has been expanded to a much larger spectrum of applications including financial modeling, risk management, and probabilistic risk analysis. Major topics in this volume include: (1) advances in theory and implementation of stochastic programming algorithms; (2) sensitivity analysis of stochastic systems; (3) stochastic programming applications and other related topics. Audience: Researchers and academies working in optimization, computer modeling, operations research and financial engineering. The book is appropriate as supplementary reading in courses on optimization and financial engineering. |