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Author : Saul Brian Gelfand,Sanjoy K. Mitter,Center for Intelligent Control Systems (U.S.),Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Publisher : Unknown
Page : 70 pages
File Size : 54,8 Mb
Release : 1990
Category : Electronic
ISBN : OCLC:20940740
Author : Harold Kushner,G. George Yin
Publisher : Springer Science & Business Media
Page : 485 pages
File Size : 41,7 Mb
Release : 2006-05-04
Category : Mathematics
ISBN : 9780387217697
This book presents a thorough development of the modern theory of stochastic approximation or recursive stochastic algorithms for both constrained and unconstrained problems. This second edition is a thorough revision, although the main features and structure remain unchanged. It contains many additional applications and results as well as more detailed discussion.
Author : Lennart Ljung,Georg Ch Pflug,Harro Walk
Publisher : Birkhauser
Page : 128 pages
File Size : 47,8 Mb
Release : 1992
Category : Mathematics
ISBN : 0817627332
Author : Z.B. Zabinsky
Publisher : Springer Science & Business Media
Page : 236 pages
File Size : 40,6 Mb
Release : 2013-11-27
Category : Mathematics
ISBN : 9781441991829
The field of global optimization has been developing at a rapid pace. There is a journal devoted to the topic, as well as many publications and notable books discussing various aspects of global optimization. This book is intended to complement these other publications with a focus on stochastic methods for global optimization. Stochastic methods, such as simulated annealing and genetic algo rithms, are gaining in popularity among practitioners and engineers be they are relatively easy to program on a computer and may be cause applied to a broad class of global optimization problems. However, the theoretical performance of these stochastic methods is not well under stood. In this book, an attempt is made to describe the theoretical prop erties of several stochastic adaptive search methods. Such a theoretical understanding may allow us to better predict algorithm performance and ultimately design new and improved algorithms. This book consolidates a collection of papers on the analysis and de velopment of stochastic adaptive search. The first chapter introduces random search algorithms. Chapters 2-5 describe the theoretical anal ysis of a progression of algorithms. A main result is that the expected number of iterations for pure adaptive search is linear in dimension for a class of Lipschitz global optimization problems. Chapter 6 discusses algorithms, based on the Hit-and-Run sampling method, that have been developed to approximate the ideal performance of pure random search. The final chapter discusses several applications in engineering that use stochastic adaptive search methods.
Author : Andreas Albrecht,Kathleen Steinhöfel
Publisher : Springer
Page : 172 pages
File Size : 42,5 Mb
Release : 2003-11-20
Category : Mathematics
ISBN : 9783540398165
This book constitutes the refereed proceedings of the Second International Symposium on Stochastic Algorithms: Foundations and Applications, SAGA 2003, held in Hatfield, UK in September 2003. The 12 revised full papers presented together with three invited papers were carefully reviewed and selected for inclusion in the book. Among the topics addressed are ant colony optimization, randomized algorithms for the intersection problem, local search for constraint satisfaction problems, randomized local search and combinatorial optimization, simulated annealing, probabilistic global search, network communication complexity, open shop scheduling, aircraft routing, traffic control, randomized straight-line programs, and stochastic automata and probabilistic transformations.
Author : Panos M. Pardalos,Anatoly Zhigljavsky,Julius Žilinskas
Publisher : Springer
Page : 296 pages
File Size : 41,6 Mb
Release : 2016-11-04
Category : Mathematics
ISBN : 9783319299754
Current research results in stochastic and deterministic global optimization including single and multiple objectives are explored and presented in this book by leading specialists from various fields. Contributions include applications to multidimensional data visualization, regression, survey calibration, inventory management, timetabling, chemical engineering, energy systems, and competitive facility location. Graduate students, researchers, and scientists in computer science, numerical analysis, optimization, and applied mathematics will be fascinated by the theoretical, computational, and application-oriented aspects of stochastic and deterministic global optimization explored in this book. This volume is dedicated to the 70th birthday of Antanas Žilinskas who is a leading world expert in global optimization. Professor Žilinskas's research has concentrated on studying models for the objective function, the development and implementation of efficient algorithms for global optimization with single and multiple objectives, and application of algorithms for solving real-world practical problems.
Author : Ali H. Sayed
Publisher : Cambridge University Press
Page : 1106 pages
File Size : 40,8 Mb
Release : 2022-12-22
Category : Technology & Engineering
ISBN : 9781009218139
This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This first volume, Foundations, introduces core topics in inference and learning, such as matrix theory, linear algebra, random variables, convex optimization and stochastic optimization, and prepares students for studying their practical application in later volumes. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 600 end-of-chapter problems (including solutions for instructors), 100 figures, 180 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Inference and Learning, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.
Author : Anatoly Zhigljavsky,Antanasz Zilinskas
Publisher : Springer
Page : 0 pages
File Size : 45,5 Mb
Release : 2010-11-23
Category : Mathematics
ISBN : 1441944850
This book examines the main methodological and theoretical developments in stochastic global optimization. It is designed to inspire readers to explore various stochastic methods of global optimization by clearly explaining the main methodological principles and features of the methods. Among the book’s features is a comprehensive study of probabilistic and statistical models underlying the stochastic optimization algorithms.
Author : Anatoly Zhigljavsky,Antanas Žilinskas
Publisher : Springer
Page : 262 pages
File Size : 55,6 Mb
Release : 2007-11-26
Category : Mathematics
ISBN : 0387740228
This book examines the main methodological and theoretical developments in stochastic global optimization. It is designed to inspire readers to explore various stochastic methods of global optimization by clearly explaining the main methodological principles and features of the methods. Among the book’s features is a comprehensive study of probabilistic and statistical models underlying the stochastic optimization algorithms.
Author : S. Bhatnagar,H.L. Prasad,L.A. Prashanth
Publisher : Springer
Page : 310 pages
File Size : 55,8 Mb
Release : 2012-08-11
Category : Technology & Engineering
ISBN : 9781447142850
Stochastic Recursive Algorithms for Optimization presents algorithms for constrained and unconstrained optimization and for reinforcement learning. Efficient perturbation approaches form a thread unifying all the algorithms considered. Simultaneous perturbation stochastic approximation and smooth fractional estimators for gradient- and Hessian-based methods are presented. These algorithms: • are easily implemented; • do not require an explicit system model; and • work with real or simulated data. Chapters on their application in service systems, vehicular traffic control and communications networks illustrate this point. The book is self-contained with necessary mathematical results placed in an appendix. The text provides easy-to-use, off-the-shelf algorithms that are given detailed mathematical treatment so the material presented will be of significant interest to practitioners, academic researchers and graduate students alike. The breadth of applications makes the book appropriate for reader from similarly diverse backgrounds: workers in relevant areas of computer science, control engineering, management science, applied mathematics, industrial engineering and operations research will find the content of value.
Author : Lennart Ljung
Publisher : Unknown
Page : 134 pages
File Size : 54,9 Mb
Release : 1974
Category : Convergence
ISBN : OCLC:185345881
Author : Harold Kushner,G. George Yin
Publisher : Springer Science & Business Media
Page : 512 pages
File Size : 40,7 Mb
Release : 2003-07-17
Category : Mathematics
ISBN : 0387008942
This book presents a thorough development of the modern theory of stochastic approximation or recursive stochastic algorithms for both constrained and unconstrained problems. This second edition is a thorough revision, although the main features and structure remain unchanged. It contains many additional applications and results as well as more detailed discussion.
Author : Aimo Törn,A. Zhilinskas
Publisher : Unknown
Page : 274 pages
File Size : 40,8 Mb
Release : 1989
Category : Mathematics
ISBN : UOM:39015012051804
Author : Jacques Azema,Michel Emery,Marc Yor
Publisher : Springer Science & Business Media
Page : 344 pages
File Size : 44,7 Mb
Release : 1997-04-14
Category : Mathematics
ISBN : 3540626344
The 31 papers collected here present original research results obtained in 1995-96, on Brownian motion and, more generally, diffusion processes, martingales, Wiener spaces, polymer measures.
Author : Bartlomiej Beliczynski,Andrzej Dzielinski,Marcin Iwanowski,Bernadete Ribeiro
Publisher : Springer
Page : 854 pages
File Size : 49,9 Mb
Release : 2007-07-03
Category : Computers
ISBN : 9783540716181
This two volume set constitutes the refereed proceedings of the 8th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2007, held in Warsaw, Poland, in April 2007. Coverage in the first volume includes evolutionary computation, genetic algorithms, and particle swarm optimization. The second volume covers neural networks, support vector machines, biomedical signal and image processing, biometrics, computer vision.