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Traditional numerical analysis books concentrate either on the mathematical or programming aspects of numerical algorithms. This textbook is different inasmuch as it emphasizes the relevance of these techniques to the real world and the use of a widely available library of numerical software in their application. The book consists of 22 carefully graded projects which will lead the reader through the techniques typically taught as part of a first course in numerical analysis. Throughout the reader is presented with projects which reflect very real problems that occur in science and industry. At the same time, the reader becomes accustomed to using a good library of numerical software when writing their programs. It is a theme of this book that the use of a solid, robust and bug-free software library will improve computational results and minimize the effort of programming. By integrating the use of the NAG (Numerical Algorithms Group) FORTRAN library into the projects, students will develop experience and expertise in the use of a software library and, by practical example, be better prepared for working further with numerical analysis libraries. This lively and entertaining text will provide a valuable complement to more traditional numerical analysis books. Answers to exercises are included as well as full documentation of the relevant library routines used.
Traditional numerical analysis books concentrate either on the mathematical or programming aspects of numerical algorithms. This textbook is different inasmuch as it emphasizes the relevance of these techniques to the real world and the use of a widely available library of numerical software in their application. The book consists of 22 carefully graded projects which will lead the reader through the techniques typically taught as part of a first course in numerical analysis. Throughout the reader is presented with projects which reflect very real problems that occur in science and industry. At the same time, the reader becomes accustomed to using a good library of numerical software when writing their programs. It is a theme of this book that the use of a solid, robust and bug-free software library will improve computational results and minimize the effort of programming. By integrating the use of the NAG (Numerical Algorithms Group) FORTRAN library into the projects, students will develop experience and expertise in the use of a software library and, by practical example, be better prepared for working further with numerical analysis libraries. This lively and entertaining text will provide a valuable complement to more traditional numerical analysis books. Answers to exercises are included as well as full documentation of the relevant library routines used.
Praise for the First Edition ". . . outstandingly appealing with regard to its style, contents, considerations of requirements of practice, choice of examples, and exercises."—Zentralblatt MATH ". . . carefully structured with many detailed worked examples."—The Mathematical Gazette The Second Edition of the highly regarded An Introduction to Numerical Methods and Analysis provides a fully revised guide to numerical approximation. The book continues to be accessible and expertly guides readers through the many available techniques of numerical methods and analysis. An Introduction to Numerical Methods and Analysis, Second Edition reflects the latest trends in the field, includes new material and revised exercises, and offers a unique emphasis on applications. The author clearly explains how to both construct and evaluate approximations for accuracy and performance, which are key skills in a variety of fields. A wide range of higher-level methods and solutions, including new topics such as the roots of polynomials, spectral collocation, finite element ideas, and Clenshaw-Curtis quadrature, are presented from an introductory perspective, and the Second Edition also features: Chapters and sections that begin with basic, elementary material followed by gradual coverage of more advanced material Exercises ranging from simple hand computations to challenging derivations and minor proofs to programming exercises Widespread exposure and utilization of MATLAB An appendix that contains proofs of various theorems and other material The book is an ideal textbook for students in advanced undergraduate mathematics and engineering courses who are interested in gaining an understanding of numerical methods and numerical analysis.
This book differs from traditional numerical analysis texts in that it focuses on the motivation and ideas behind the algorithms presented rather than on detailed analyses of them. It presents a broad 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. In the 20 years since its original publication, the modern, fundamental perspective of this book has aged well, and it continues to be used in the classroom. This Classics edition has been updated to include pointers to Python software and the Chebfun package, expansions on barycentric formulation for Lagrange polynomial interpretation and stochastic methods, and the availability of about 100 interactive educational modules that dynamically illustrate the concepts and algorithms in the book. Scientific Computing: An Introductory Survey, Second Edition is intended as both a textbook and a reference for computationally oriented disciplines that need to solve mathematical problems.
Makes Numerical Programming More Accessible to a Wider AudienceBearing in mind the evolution of modern programming, most specifically emergent programming languages that reflect modern practice, Numerical Programming: A Practical Guide for Scientists and Engineers Using Python and C/C++ utilizes the author's many years of practical research and tea
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This text provides an application oriented introduction to the numerical methods for partial differential equations. It covers finite difference, finite element, and finite volume methods, interweaving theory and applications throughout. The book examines modern topics such as adaptive methods, multilevel methods, and methods for convection-dominated problems and includes detailed illustrations and extensive exercises.
This innovative text helps demystify numerical modelling for early-stage physics and engineering students. It takes a hands-on, project-based approach, with each chapter focusing on an intriguing physics problem taken from classical mechanics, electrodynamics, thermodynamics, astrophysics, and quantum mechanics. To solve these problems, students must apply different numerical methods for themselves, building up their knowledge and practical skills organically. Each project includes a discussion of the fundamentals, the mathematical formulation of the problem, an introduction to the numerical methods and algorithms, and exercises, with solutions available to instructors. The methods presented focus primarily on differential equations, both ordinary and partial, as well as basic mathematical operations. Developed over many years of teaching a computational modelling course, this stand-alone book equips students with an essential numerical modelling toolkit for today's data-driven landscape, and gives them new ways to explore science and engineering.
This textbook introduces key numerical algorithms used for problems arising in three core areas of scientific computing: calculus, differential equations, and linear algebra. Theoretical results supporting the derivation and error analysis of algorithms are given rigorous justification in the text and exercises, and a wide variety of detailed computational examples further enhance the understanding of key concepts. Numerical Mathematics includes topics not typically discussed in similar texts at this level, such as a Fourier-based analysis of the trapezoid rule, finite volume methods for the 2D Poisson problem, the Nyström method for approximating the solution of integral equations, and the relatively new FEAST method for targeting clusters of eigenvalues and their eigenvectors. An early emphasis is given to recognizing or deducing orders of convergence in practice, which is essential for assessing algorithm performance and debugging computational software. Numerical experiments complement many of the theorems concerning convergence, illustrating typical behavior of the associated algorithms when the assumptions of the theorems are satisfied and when they are not. This book is intended for advanced undergraduate and beginning graduate students in mathematics seeking a solid foundation in the theory and practice of scientific computing. Students and researchers in other disciplines who want a fuller understanding of the principles underlying these algorithms will also find it useful. The text is divided into three parts, corresponding to numerical methods for problems in calculus, differential equations, and linear algebra. Each part can be used for a one-term course (quarter or semester), making the book suitable for a two- or three-term sequence in numerical analysis or for largely independent courses on any of the three main topics.