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.
Risk and uncertainty in the complex systems; chance-constrained stochastic programming; two-stage stochastic programming problems; multistage stochastic programming problems; game approach to stochastic programming problems; existence of solution and its optimality in stochastic programming problems; methods for solving infinite and semi-infinite programming problems; optimization of fuzzy sets; optimization of nonlinear programming problems with nonuniquely defined variables; optimization problems in function spaces.