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

This the first book devoted to real-time optimization for systems governed by PDEs, focuses on new formulations, methods, and algorithms needed to facilitate real-time, PDE-constrained optimization.

Focuses on elliptic boundary value problems in domains with non-smooth boundaries and on problems with mixed boundary conditions. Its contents are essential for an understanding of the behavior of numerical methods for partial differential equations (PDEs) on two-dimensional domains with corners.

Presents a systematic theory of Taylor expansions of evolutionary-type stochastic partial differential equations (SPDEs). The authors show how Taylor expansions can be used to derive higher order numerical methods for SPDEs, with a focus on pathwise and strong convergence.

Provides readers with information necessary to construct spectral NWP models; a self-contained, well-documented, coded spectral NWP model; and theoretical and practical exercises, some of which include solutions.

A comprehensive book that brings the reader up-to-date with the cutting edge of the subject.

Describes and analyses all available alternating projection methods for solving the general problem of finding a point in the intersection of several given sets belonging to a Hilbert space. For each method the authors describe and analyse convergence, speed of convergence, acceleration techniques, stopping criteria, and applications.

A concise introduction to computational methods for solving large linear systems of equations.

Implicit filtering is a way to solve bound-constrained optimization problems for which derivative information is not available. Here, the author describes the algorithm, its convergence theory, and a new MATLAB(R) implementation, and includes three case studies.

An overview of some of the main results and recent developments in the field of nonlinear water waves.

The revised edition of a classic text which presents the best practices of developing multigrid solvers for large-scale computational problems.

Addresses modern nonlinear programming (NLP) concepts and algorithms, especially as they apply to challenging applications in chemical process engineering. The author provides a firm grounding in fundamental NLP properties and algorithms, and relates them to real-world problem classes in process optimization.

A revised textbook for introductory courses in numerical methods, MATLAB and technical computing, which emphasises the use of mathematical software.

This volume deals with the numerical simulation of the behavior of continuous media by augmented Lagrangian and operator-splitting methods.

Solutions of the wave equation or Maxwell's equations in boundary value and free space problems are analyzed. Hyperbolic systems in domains going off to infinity are studied. New results on Maxwell's equations and nonstar shaped reflecting bodies are included.

A comprehensive introduction to the mathematics of parameter continuation which emphasises theoretical rigor, algorithm development and software engineering.

Presents flow control and optimization as a subdiscipline of computational mathematics and computational engineering. It introduces the development and analysis of several approaches for solving flow control and optimization problems through the use of modern CFD and optimization methods.

The performance of a process - for example, how an aircraft consumes fuel - can be enhanced when the most effective controls and operating points for the process are determined. This book provides a rigorous introduction to analysing these processes and finding the best modes of control and operation for them.

Provides concrete examples of how to justify the validity of every single digit of a numerical answer. Methods range from carefully designed computer experiments to a posteriori error estimates and computer-assisted proofs based on interval arithmetic.

A concise, rigorous, yet accessible, account of the fundamentals of constrained optimization theory.

Originally published in 1970, this is a classic textbook that provides a solid foundation for learning about dynamical systems and encourages students to develop a reliable intuition for problem solving. Since linearity lies at the heart of much of the mathematical analysis used in applications, a firm grounding in its central ideas is essential.

Provides a careful and rigorous presentation of the mathematics underlying subdivision surfaces as used in computer graphics and animation, explaining the concepts necessary to easily read the subdivision literature. It also organises subdivision methods in a unique and unambiguous hierarchy in order to provide insight and understanding.

This introduction to computer-based problem-solving using the MATLAB(R) environment is highly recommended for students wishing to learn the concepts and develop the programming skills that are fundamental to computational science and engineering (CSE).

Provides an overview of state-of-the-art registration techniques from theory to practice, plus numerous exercises designed to enhance readers' understanding of the principles and mechanisms of the described techniques.

A handbook for MATLAB which gives a focused approach to the software for students and professional researchers.

A very broad coverage of the most applicable aspects of stochastic processes.

This mathematically rigorous book remains the standard reference on the linear complementarity problem. Its comprehensive treatment of the computation of equilibria arising from engineering, economics, and finance, plus chapter-ending exercises and "Notes and References" sections make it equally useful for a graduate-level course or for self-study.

Sport and statistics collide in this collection of articles (from American Statistical Association publications) on using statistics to analyze sport.

Provides an introduction to the practical treatment of inverse problems by means of numerical methods, with a focus on basic mathematical and computational aspects. To solve inverse problems, the authors demonstrate that insight about them goes hand in hand with algorithms.

A comprehensive treatment of optimization problems involving uncertain parameters for which stochastic models are available.

Radar imaging is a mathematically rich subject with many interesting applications and a large variety of challenging, mathematical open problems. The goal of this book is to provide mathematicians with the background they need to work in the field, building on the foundation of the underlying partial differential equations.