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Stochastic and global optimization

✍ Scribed by Dzemyda G., Saltenis V., Zhilinskas A. (eds.)

Publisher
Springer
Year
2002
Tongue
English
Leaves
250
Series
Nonconvex Optimization and Its Applications
Category
Library
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✦ Synopsis

This book is dedicated to the 70th birthday of Professor J. Mockus, whose scientific interests include theory and applications of global and discrete optimization, and stochastic programming. The papers for the book were selected because they relate to these topics and also satisfy the criterion of theoretical soundness combined with practical applicability. In addition, the methods for statistical analysis of extremal problems are covered. Although statistical approach to global and discrete optimization is emphasized, applications to optimal design and to mathematical finance are also presented. The results of some subjects (e.g., statistical models based on one-dimensional global optimization) are summarized and the prospects for new developments are justified. Audience: Practitioners, graduate students in mathematics, statistics, computer science and engineering.

✦ Table of Contents

Table of Contents......Page 6
The Jubilee of Prof. Dr. Habil. Jonas Mockus......Page 8
1. Topographical Differential Evolution Using Pre-calculated Differentials......Page 14
2. Optimal Tax Depreciation in Stochastic Investment Model......Page 32
3. Global Optimisation of Chemical Process Flowsheets......Page 46
4. One-Dimensional Global Optimization Based on Statistical Models......Page 62
5. Animated Visual Analysis of Extremal Problems......Page 78
6. Test Problems for Lipschitz Univariate Global Optimization with Multiextremal Constraints......Page 106
7. Numerical Techniques in Applied Multistage Stochastic Programming......Page 124
8. On the Efficiency and Effectiveness of Controlled Random Search......Page 142
9. Discrete Backtracking Adaptive Search for Global Optimization......Page 160
10. Parallel Branch-and-bound Attraction Based Methods for Global Optimization......Page 188
11. On Solution of Stochastic Linear Programs by Discretization Methods......Page 202
12. The Structure of Multivariate Models and the Range of Definition......Page 222
13. Optimality Criteria for Investment Projects Under Uncertainty......Page 234

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