Sinopsis
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.
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