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

Focuses on the motivation and ideas behind algorithms rather than on detailed analyses of them. This book presents an overview of methods and software for solving mathematical problems arising in computational modeling and data analysis, including proper problem formulation, selection of effective solution algorithms, and interpretation of results.

Focuses on the underlying mathematics necessary to use Sage efficiently and is illustrated with concrete examples. Part I is accessible to high school and undergraduate students and Parts II, III, and IV are suitable for graduate students, teachers, and researchers.

Provides an overview of the myriad methods for applying dynamical systems techniques to PDEs and highlights the impact of PDE methods on dynamical systems. Also included are many nonlinear evolution equations, which have been benchmark models across the sciences, and examples and techniques to strengthen preparation for research.

This informal introduction to computational fluid dynamics and practical guide to numerical simulation of transport phenomena covers the derivation of the governing equations, construction of finite element approximations, and qualitative properties of numerical solutions, among other topics.

This single-volume textbook covers the fundamentals of linear and nonlinear functional analysis, illustrating most of the basic theorems with numerous applications to linear and nonlinear partial differential equations and to selected topics from numerical analysis and optimization theory.

Prompted by recent developments in inverse theory, this book is a completely rewritten version of a 1987 book by the same author. In this version there are many algorithmic details for Monte Carlo methods, least-squares discrete problems, and least-squares problems involving functions.

A thorough, up-to-date treatment of the behaviour of numerical algorithms in finite precision arithmetic. The book combines algorithmic derivations, perturbation theory, and rounding error analysis, all enlivened by historical perspective and informative quotations.

Offers an introduction to both computational inverse problems and uncertainty quantification (UQ) for inverse problems. The book also presents more advanced material on Bayesian methods and UQ, including Markov chain Monte Carlo sampling methods for UQ in inverse problems.

Offers an elementary yet comprehensive introduction to the numerical solution of partial differential equations (PDEs). The book provides detailed descriptions of the four major classes of discretization methods for PDEs and runnable MATLAB (R) code for each of the discretization methods and exercises.

Not a collection of proceedings, but 11 papers on topics that emerged from a September 1989 conference in Houston on mathematical and computational issues in geophysical fluid and solid mechanics. The discussions include a semi-linear heat equation subject to the specification of energy.

A great deal can be learned through modelling and mathematical analysis about real-life phenomena. Scientific computing also becomes more effective if it is preceded by preliminary analysis. These advantages of mathematical modelling are demonstrated by models of historical importance in an easily understandable way.

Written for high school students who have some experience with computation and an interest in math modelling, this handbook is designed to take you from basic graphic calculator or spreadsheet experience to the next level, which includes programming basics, working with large data sets, and visualization techniques.

Eigenvalue computations are ubiquitous in science and engineering. John Francis's implicitly shifted QR algorithm has been the method of choice for small to medium sized eigenvalue problems since its invention in 1959. This book presents a new view of this classical algorithm.

Provides a self-contained, comprehensive study of the main first-order methods that are frequently used in solving large-scale problems. The author has gathered, reorganized, and synthesized (in a unified manner) many results that are currently scattered throughout the literature, many of which cannot be typically found in optimization books.

Provides a basic introduction to probability and statistics for uncertainty quantification in the context of inverse problems, as well as an introduction to statistical regularization of inverse problems. The author covers basic statistical inference, introduces the framework of ill-posed inverse problems, and explains statistical questions that arise in their applications.

For over a quarter of a century, high-gain observers have been used extensively in the design of output feedback control of nonlinear systems. This book presents a clear, unified treatment of the theory of high-gain observers and their use in feedback control. Also provided is a discussion of the separation principle for nonlinear systems.

Provides an introduction to representative nonrelativistic quantum control problems and their theoretical analysis and solution via modern computational techniques. The quantum theory framework is based on the Schroedinger picture, and the optimization theory, which focuses on functional spaces, is based on the Lagrange formalism.

Contains a comprehensive review of the objectives, methods, and practical implementation of credit and behavioural scoring. The authors review principles of the statistical and operations research methods used in building scorecards, as well as the advantages and disadvantages of each approach. The book also covers practical problems encountered in building, using, and monitoring scorecards.

Presents the main achievements that have emerged in regularization theory over the past 50 years, focusing on linear ill-posed problems and the development of methods that can be applied to them. This volume discusses state-of-the-art inverse problems theory, focusing on numerically relevant aspects, and presents diverse real-world applications, important test cases, and possible pitfalls.

Are you familiar with the IEEE floating point arithmetic standard? Would you like to understand it better? This book gives a broad overview of numerical computing, in a historical context, with a special focus on the IEEE standard for binary floating point arithmetic.

AMS-IMS-SIAM Joint Summer Research Conference Papers on Fast Algorithms in Mathematics, Computer Science and Engineering.

A unique comprehensive review of axiomatic consensus theory in biomathematics as it has developed over the past 30 years.

This compact yet thorough tutorial is the perfect introduction to the basic concepts of solving partial differential equations (PDEs) using parallel numerical methods.

Provides an understandable introduction to one approach to design sensitivity computation and illustrates some of the important mathematical and computational issues inherent in using the sensitivity equation method (SEM) for partial differential equations.

The Fortran language standard has undergone significant upgrades in recent years (1990, 1995, 2003, and 2008) and this book illustrates many of these improvements through practical solutions to a number of scientific and engineering problems.

This unique book addresses advanced linear algebra using invariant subspaces as the central notion and main tool.

Presents the latest ground-breaking theoretical foundation to shape optimization in a form that can be used by the engineering and scientific communities. It also clearly explains the state-of-the-art developments in a mathematical language that will attract mathematicians to open questions in this important field.

An overview of classical reservoir engineering and basic reservoir simulation methods for students and practitioners.

Provides a mathematically rigorous introduction to the fundamental ideas of modern statistics for readers without a calculus background. It is the only book at this level to introduce readers to modern concepts of hypothesis testing and estimation, covering basic concepts of finite, discrete models of probability and elementary statistical methods.