Stochastic Global Optimization (Springer Optimization and Its Applications, 9, Band 9) - Hardcover

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Anatoly Zhigljavsky has received his BSc, MSc and PhD degrees in mathematics and statistics at Faculty of Mathematics, St.Petersburg State University. He became professor of statistics at the St.Petersburg State University in 1989. Since 1997 he is a professor, Chair in Statistics at Cardiff University. Anatoly Zhigljavsky is the author or co-author of 11 monographs on the topics of time series analysis, stochastic global optimization, optimal experimental design and dynamical systems; he is the editor/co-editor of 9 books on various topics and the author of more than 150 research papers in refereed journals. He has organized several major conferences on time series analysis, experimental design and global optimization. In 2019, he has received a prestigious Constantine Caratheodory award by the International Society for Global Optimization for his contribution to stochastic optimization. Antanas ?ilinskas is member of Lithuanian Academy of Sciences and professor of informatics at the Institute of Data Science and Digital Technologies of Vilnius university. His research interests include global and multi-objective optimization, visualization of multidimensional data, and optimal engineering design. He is author or co-author of several well-known monographs in optimization. His scientific achievements in global optimization are marked by the Caratheodory prize of the International Society of Global Optimization (2017). Prof. ?ilinskas is a member of editorial boards of numerous international scientific journals. He also paid a lot of attention to teaching students and organizing studies of informatics, has prepared several textbooks on optimization and informatics.

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This book presents the main methodological and theoretical developments in stochastic global optimization. The extensive text is divided into four chapters; the topics include the basic principles and methods of global random search, statistical inference in random search, Markovian and population-based random search methods, methods based on statistical models of multimodal functions and principles of rational decisions theory.

Key features:

* Inspires readers to explore various stochastic methods of global optimization by clearly explaining the main methodological principles and features of the methods;

* Includes a comprehensive study of probabilistic and statistical models underlying the stochastic optimization algorithms;

* Expands upon more sophisticated techniques including random and semi-random coverings, stratified sampling schemes, Markovian algorithms and population based algorithms;

*Provides a thorough description of the methods based on statistical models of objective function;

*Discusses criteria for evaluating efficiency of optimization algorithms and difficulties occurring in applied global optimization.

Stochastic Global Optimization is intended for mature researchers and graduate students interested in global optimization, operations research, computer science, probability, statistics, computational and applied mathematics, mechanical and chemical engineering, and many other fields where methods of global optimization can be used.