Stochastic Global Optimization: 9 (Springer Optimization and Its Applications, 9) - Tapa dura

Acerca del autor

Nina Golyandina received her MSc and PhD degrees in mathematics from St. Petersburg State University, Russia, in 1985 and 1998, respectively. She began working at the same university in 1985, where she is currently an Associate Professor of Statistical Modeling. Her main research interests are in statistical modeling and applied statistics, especially time series investigation by means of singular spectrum analysis. Dr. Golyandina is the coauthor of 2 monographs on singular spectrum analysis and of more than 30 research papers in refereed journals related to applied probability and statistics. Anton Korobeynikov received his MSc and PhD degrees in applied mathematics from St. Petersburg State University, Russia, in 2007 and 2010, respectively. He began working at St. Petersburg State University in 2007, where he currently holds the position of Associate Professor of Statistical Modeling. Dr. Korobeynikov's main research interests include computational and appliedstatistics, including time series analysis, efficient implementation of algorithms of computational statistics, and probabilistic methods in bioinformatics. He is the original author of the R-package Rssa for singular spectrum analysis. Anatoly Zhigljavsky graduated from the Faculty of Mathematics, St. Petersburg State University, in 1976. He received his PhD in applied probability in 1981 and was a Professor of Statistics at the same university from 1989 to 1997. Since 1997 he has been a Professor and Chair of Statistics at Cardiff University. Anatoly Zhigljavsky is the author or co-author of 9 monographs on the topics of SSA for time series analysis, stochastic global optimization, optimal experimental design and dynamical systems; he is the editor/co-editor of 8 books on various topics and the author of over 150 research papers in peer-reviewed journals. He has organized several major conferences on time series analysis, experimental design and global optimization, and serves on the editorial board of two journals: Journal of Global Optimization, and Statistics and Its Interface. He is also the director of the Centre for Optimisation and Its Applications at Cardiff University.

De la contraportada

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.