Global Optimization with Non-Convex Constraints

Sequential and Parallel Algorithms

Inbunden, Engelska, 2000

AvRoman G. Strongin,Yaroslav D. Sergeyev,Roman G Strongin,Yaroslav D Sergeyev

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Presents a new approach to global non-convex constrained optimization. Problem dimensionality is reduced via space-filling curves and to economize the search, constraint is accounted separately (penalties are not employed). The multicriteria case is also considered. All techniques are generalized for (non-redundant) execution on multiprocessor systems.

Produktinformation

Utgivningsdatum2000-10-31
Mått155 x 235 x 45 mm
Vikt1 270 g
FormatInbunden
SpråkEngelska
SerieNonconvex Optimization and Its Applications
Antal sidor704
Upplaga2000
FörlagKluwer Academic Publishers
ISBN9780792364900

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Preface. Acknowledgements. Part One: Global Optimization Algorithms as Decision Procedures. Theoretical Background and Core Univariate Case. 1. Introduction. 2. Global Optimization Algorithms as Statistical Decision Procedures - The Information Approach. 3. Core Global Search Algorithm and Convergence Study. 4. Global Optimization Methods as Bounding Procedures - The Geometric Approach. Part Two: Generalizations for Parallel Computing, Constrained and Multiple Criteria Problems. 5. Parallel Global Optimization Algorithms and Evaluation of the Efficiency of Parallelism. 6. Global Optimization under Non-Convex Constraints - The Index Approach. 7. Algorithms for Multiple Criteria Multiextremal Problems. Part Three: Global Optimization in Many Dimensions. Generalizations through Peano Curves. 8. Peano-Type Space-Filling Curves as Means for Multivariate Problems. 9. Multidimensional Parallel Algorithms. 10. Multiple Peano Scannings and Multidimensional Problems. References. List of Algorithms. List of Figures. List of Tables. Index.