ISBN: 978-0-470-72335-7
Hardcover
488 pages
May 2008
Authored by one of the world’s leading authorities on numerical methods this update of one of the standard references on numerical analysis, outlines recent developments in the field and presenting a detailed overview of the area. The only book to provide both a detailed treatment of Runge-Kutta methods and a thorough exposition of general linear methods, it also provides practical guidance on solving equations associated with general linear methods, thus providing assistance to those who wish to develop their own computer code.
Accompanied by a website hosting solutions to problems and slides for use in teaching
Illustrated throughout by worked examples of key algorithms.
Presents practical guidance on solving equations associated with general linear methods
Gives an introductory overview of the field before going on to describe recent developments.
All methods are illustrated with detailed examples and problems sets.
ISBN: 978-0-471-75800-6
Hardcover
608 pages
March 2008
Explore the latest applications of optimization theory and methods
Optimization is central to any problem involving decision making in many disciplines, such as engineering, mathematics, statistics, economics, and computer science. Now, more than ever, it is increasingly vital to have a firm grasp of the topic due to the rapid progress in computer technology, including the development and availability of user-friendly software, high-speed and parallel processors, and networks. Fully updated to reflect modern developments in the field, An Introduction to Optimization, Third Edition fills the need for an accessible, yet rigorous, introduction to optimization theory and methods.
The book begins with a review of basic definitions and notations and also provides the related fundamental background of linear algebra, geometry, and calculus. With this foundation, the authors explore the essential topics of unconstrained optimization problems, linear programming problems, and nonlinear constrained optimization. An optimization perspective on global search methods is featured and includes discussions on genetic algorithms, particle swarm optimization, and the simulated annealing algorithm. In addition, the book includes an elementary introduction to artificial neural networks, convex optimization, and multi-objective optimization, all of which are of tremendous interest to students, researchers, and practitioners.
Additional features of the Third Edition include:
New discussions of semidefinite programming and Lagrangian algorithms
A new chapter on global search methods
A new chapter on multipleobjective optimization
New and modified examples and exercises in each chapter as well as an updated bibliography containing new references
An updated Instructor's Manual with fully worked-out solutions to the exercises
Numerous diagrams and figures found throughout the text complement the written presentation of key concepts, and each chapter is followed by MATLABR exercises and drill problems that reinforce the discussed theory and algorithms. With innovative coverage and a straightforward approach, An Introduction to Optimization, Third Edition is an excellent book for courses in optimization theory and methods at the upper-undergraduate and graduate levels. It also serves as a useful, self-contained reference for researchers and professionals in a wide array of fields.
ISBN: 978-0-470-01497-4
Hardcover
456 pages
May 2008
This book covers all the latest advances, as well as more established methods, in the application of statistical and optimisation methods within modern industry. These include applications from a range of industries that include micro-electronics, chemical, automotive, engineering, food, component assembly, household goods and plastics. Methods range from basic graphical approaches to generalised modelling, from designed experiments to process control. Solutions cover produce and process design, through manufacture to packaging and delivery, from single responses to multivariate problems.
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ISBN: 978-0-470-06030-8
Hardcover
432 pages
May 2008
Bayesian Networks, the result of the convergence of artificial intelligence with statistics, are growing in popularity. Their versatility and modelling power is now employed across a variety of fields for the purposes of analysis, simulation, prediction and diagnosis.
This book provides a general introduction to Bayesian networks, defining and illustrating the basic concepts with pedagogical examples and twenty real-life case studies drawn from a range of fields including medicine, computing, natural sciences and engineering.
Designed to help analysts, engineers, scientists and professionals taking part in complex decision processes to successfully implement Bayesian networks, this book equips readers with proven methods to generate, calibrate, evaluate and validate Bayesian networks.
The book:
Provides the tools to overcome common practical challenges such as the treatment of missing input data, interaction with experts and decision makers, determination of the optimal granularity and size of the model.
Highlights the strengths of Bayesian networks whilst also presenting a discussion of their limitations.
Compares Bayesian networks with other modelling techniques such as neural networks, fuzzy logic and fault trees.
Describes, for ease of comparison, the main features of the major Bayesian network software packages: Netica, Hugin, Elvira and Discoverer, from the point of view of the user.
Offers a historical perspective on the subject and analyses future directions for research.
Written by leading experts with practical experience of applying Bayesian networks in finance, banking, medicine, robotics, civil engineering, geology, geography, genetics, forensic science, ecology, and industry, the book has much to offer both practitioners and researchers involved in statistical analysis or modelling in any of these fields.
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