Systems Optimization Methodology - Part II (System Optimization Methodology)

This is a survey of recent results on weighted inequalities in Lorentz and Orlicz spaces and Zygmund classes - rapidly growing offshoots of the theory of harmonic analysis operators. The volume brings together material scattered among journals, much of it previously only available in Russian 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 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. This monograph deals with theoretical fundamentals and numerical methods of optimizing nondetermined models of systems. The main body of the work is devoted to investigation and optimization of system models under incomplete information. Covering the subject of systems optimization methodology, this work discusses areas including: philosophical problems of the methodology for systems modelling; decomposition and aggregation; and vector optimization. Nowadays the deterministic approach prevails in investigation and optimization for purposes of planning, designing and managing complex systems in economy, technology and defense.