Global Optimization with Non-Convex Constraints

Sequential and Parallel Algorithms

AvRoman G. Strongin,Yaroslav D. Sergeyev

Inbunden, Engelska, 2000

Del i serien Nonconvex Optimization and Its Applications

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Häftad

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Beskrivning

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

Utgivningsdatum:2000-10-31
Mått:155 x 235 x 45 mm
Vikt:1 270 g
Format:Inbunden
Språk:Engelska
Serie:Nonconvex Optimization and Its Applications
Antal sidor:704
Upplaga:2000
Förlag:Kluwer Academic Publishers
ISBN:9780792364900

Utforska kategorier

Optimering inom Naturvetenskap och teknik
Tillämpad matematik inom Naturvetenskap och teknik
Programmeringsböcker inom Data och IT

Innehållsförteckning

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.