
- 324 pages
- English
- PDF
- Available on iOS & Android
About this book
This monograph deals with theoretical fundamentals and numerical methods of optimizing nondetermined models of systems. The main body of this work is devoted to investigation and optimization of system models under incomplete information. Much consideration is given to one-, two- and multistage problems of stochastic programming, solution methods and problems of solution stability. Optimization problems with fuzzy variables and optimization problems in function spaces are investigated. Examples are given for implementation of specific models of optimization under incomplete information.The book is based on lectures delivered by the author since 1965 for undergraduates and postgraduates at St. Petersburg (Leningrad) State University.
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Table of contents
- CONTENTS
- Introduction
- Chapter 1 Risk and Uncertainty in the Complex Systems
- Chapter 2 Chance-constrained Stochastic Programming Problems
- Chapter 3 Two-stage Stochastic Programming Problems
- Chapter 4 Multistage Stochastic Programming Problems
- Chapter 5 Game approach to Stochastic Programming Problems
- Chapter 6 Existense of Solution and its Optimality in Stochastic Programming Problems
- Chapter 7 Stability of Solutions in Stochastic Programming Problems
- Chapter 8 Methods for Solving Infinite and Semi-infinite Programming Problems
- Chapter 9 Optimization on Fuzzy Sets
- Chapter 10 Optimization of Nonlinear Programming Problems with Nonuniquely Defined Variables
- Chapter 11 Optimization Problems in Function Spaces
- Conclusion
- BIBLIOGRAPHY
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