Bøger udgivet af Society for Industrial & Applied Mathematics,U.S.

This second edition of the author's acclaimed textbook covers the major topics of computational linear algebra, including solution of a system of linear equations, least-squares solutions of linear systems, computation of eigenvalues, eigenvectors, and singular value problems.

Introduces the numerical technique of polynomial continuation, which is used to compute solutions to systems of polynomial equations. Originally published in 1987, it remains a useful starting point for the reader interested in learning how to solve practical problems without advanced mathematics.

Mathematical symmetry and chaos come together to form striking, beautiful colour images throughout this impressive work, which addresses how the dynamics of complexity can produce familiar universal patterns.

Introduces undergraduate students to optimization and its applications using relevant and realistic problems.

This self-contained textbook presents matrix analysis in the context of numerical computation with numerical conditioning of problems and numerical stability of algorithms at the forefront. It uses a unique combination of numerical insight and mathematical rigour.

Describes how techniques from the multi-disciplinary field of data mining can be used to address the modern problem of data overload in science and engineering domains.

The authors provide a comprehensive treatment of stochastic systems from the foundations of probability to stochastic optimal control.

Takes the reader step by step through the techniques of eliciting and analysing expert judgment, with special attention given to helping the reader develop elicitation methods and tools adaptable to a variety of unique situations and work areas.

Presents the design, analysis, and application of a variety of algorithms used to manage dynamical systems with unknown parameters.

Learning through doing is the foundation of this book, which allows readers to explore case studies as well as expository material. It provides a practical guide to the numerical solution of linear and nonlinear equations, differential equations, optimization problems, and eigenvalue problems.

This second edition has been updated and expanded to cover recent developments in applications and theory, including an elegant NP completeness argument by Uwe Naumann and a brief introduction to scarcity, a generalization of sparsity. There is also added material on checkpointing and iterative differentiation.

The first book on parallel MATLAB and the first parallel computing book focused on the design, code, debug, and test techniques required to quickly produce well-performing parallel programs. It presents a hands-on approach with numerous example programs.

A unified treatment of fluid mechanics, analysis and numerical analysis appropriate for first year graduate students.

An accessible exposition of social choices such as selecting a winning competitor, or dividing up resources.

Focuses on three primal DG methods, covering both theory and computation, and providing the basic tools for analysis.

Presents algorithms for using HMMs and explains the derivation of those algorithms for the dynamical systems community.

An authoritative reference on cooperative decision and control of unmanned aerial vehicles.

Introduces the applications, theory, and algorithms of linear and nonlinear optimization, with an emphasis on the practical aspects of the material. A supplemental website offers auxiliary data sets that are necessary for some of the exercises.

Financial mathematics and its calculus introduced in an accessible manner for undergraduate students.

This textbook develops, analyzes, and applies numerical methods for evolutionary, or time-dependent, differential problems. Both partial and ordinary differential equations are discussed from a unified viewpoint.

Analyses Lagrange multiplier theory and demonstrates its impact on the development of numerical algorithms for variational problems in function spaces.

Introduces the general theory of order statistics and their applications. The book covers topics such as distribution theory for order statistics from continuous and discrete populations, moment relations, bounds and approximations, order statistics in statistical inference and characterization results, and basic asymptotic theory

An excellent reference for researchers and students who need or want more than just the most basic elements of generalized (or pseudo-) inverse concepts. First published in 1979, the book remains up-to-date and readable.

This book was the first and remains the only book to give a comprehensive treatment of the behaviour of linear or nonlinear systems when they are connected in a closed-loop fashion, with the output of one system forming the input of the other.

Provides an introduction to a subject whose use has steadily increased over the past 40 years. An update of Ramon Moore's previous books on the topic, this book provides broad coverage of the subject as well as the historical perspective of one of the originators of modern interval analysis.

The absence of derivatives, often combined with the presence of noise or lack of smoothness, is a major challenge for optimization. This book explains how sampling and model techniques are used in derivative-free methods and how these methods are designed to efficiently and rigorously solve optimization problems.

Provides a comprehensive picture of model reduction, combining system theory with numerical linear algebra and computational considerations. It addresses the issue of model reduction and the resulting trade-offs between accuracy and complexity. Special attention is given to numerical aspects, simulation questions, and practical applications.

Written for anyone who wants to learn about using wavelets to analyse, process, and synthesize images and signals. Applications are described in detail and there are step-by-step instructions about how to construct and apply wavelets. It is the only mathematically-rigorous monograph written by a mathematician specifically for nonspecialists.

Shows how modern matrix methods can be applied in data mining and pattern recognition.

The Lanczos and conjugate gradient (CG) algorithms are fascinating numerical algorithms. This book presents the most comprehensive discussion to date of the use of these methods for computing eigenvalues and solving linear systems in both exact and floating point arithmetic.