Numerical Methods (Third Edition) ISBN
Computation is an indispensable tool in the analysis and exploration of a wide range of physical phenomena. This book presents an exhaustive exposition of the various numerical methods used in scientific and engineering applications. It emphasizes the practical aspects of numerical computation and discusses various techniques in sufficient detail to enable their implementation in solving a wide range of problems. An important addition in this revised third edition is a chapter on basic statistics. More than 100 worked-out examples illustrate a variety of numerical algorithms. The limitations of the algorithms are also discussed, as are pitfalls in numerical computations. A special feature is the inclusion of a discussion of techniques for error-estimation. In addition, more than 500 unsolved problems (with answers) of varying difficulty are included, and more than 200 computer programs in FORTRAN and C, covering all topics, are provided as supplementary material online. These give the book a strong pedagogic focus. Examples and exercises are drawn from areas as diverse as fluid mechanics, celestial mechanics, and seismology. This book will be extremely useful for graduate students and researchers in all branches of science and engineering.
Numerical Methods (Third Edition) ISBN
High resolution upwind and centred methods are today a mature generation of computational techniques applicable to a wide range of engineering and scientific disciplines, Computational Fluid Dynamics (CFD) being the most prominent up to now. This textbook gives a comprehensive, coherent and practical presentation of this class of techniques. The book is designed to provide readers with an understanding of the basic concepts, some of the underlying theory, the ability to critically use the current research papers on the subject, and, above all, with the required information for the practical implementation of the methods. Direct applicability of the methods include: compressible, steady, unsteady, reactive, viscous, non-viscous and free surface flows. For this third edition the book was thoroughly revised and contains substantially more, and new material both in its fundamental as well as in its applied parts.
Numerical Methods for Engineers and Scientists Using MATLAB, 3rd edition, provides the reader with a broad knowledge of the fundamentals of numerical methods utilized in various disciplines in engineering and science. The powerful software MATLAB is introduced at the outset and is assimilated throughout the book to perform symbolic, graphical, and numerical tasks. The textbook, written at the junior/senior level, methodically covers a wide range of techniques ranging from curve fitting a set of data to numerically solving initial- and boundary-value problems. Each method is accompanied by at least one fully worked-out example, followed by either a user-defined function or a MATLAB script file.
Numerical Recipes is the generic title of a series of books on algorithms and numerical analysis by William H. Press, Saul A. Teukolsky, William T. Vetterling and Brian P. Flannery. In various editions, the books have been in print since 1986. The most recent edition was published in 2007.
The Numerical Recipes books cover a range of topics that include both classical numerical analysis (interpolation, integration, linear algebra, differential equations, and so on), signal processing (Fourier methods, filtering), statistical treatment of data, and a few topics in machine learning (hidden Markov model, support vector machines). The writing style is accessible and has an informal tone. The emphasis is on understanding the underlying basics of techniques, not on the refinements that may, in practice, be needed to achieve optimal performance and reliability. Few results are proved with any degree of rigor, although the ideas behind proofs are often sketched, and references are given. Importantly, virtually all methods that are discussed are also implemented in a programming language, with the code printed in the book. Each version is keyed to a specific language.
Numerical Methods for Engineers and Scientists, 3rd Edition provides engineers with a more concise treatment of the essential topics of numerical methods while emphasizing MATLAB use. The third edition includes a new chapter, with all new content, on Fourier Transform and a new chapter on Eigenvalues (compiled from existing Second Edition content). The focus is placed on the use of anonymous functions instead of inline functions and the uses of subfunctions and nested functions. This updated edition includes 50% new or updated Homework Problems, updated examples, helping engineers test their understanding and reinforce key concepts.
Introduction to ordinary differential equations. First and second order linear differential equations, systems of linear differential equations, Laplace transform, numerical methods, applications. (This course is considered upper division with respect to the requirements for the major and minor in mathematics.)
Time-permitting, we will briefly examine numerical methods for partial differential equations and relevant implementation thereof, e.g., in Matlab, as well as some select examples of nonlinear partial differential equations and the traveling or standing wave solutions possible therein.
A practical overview of computational methods used in science, statistics, industry, and machine learning. Topics will include: an introduction to python programming and software for scientific computing such as NumPy and LAPACK, numerical linear algebra, optimization and root-finding, approximation of functions by splines and trigonometric polynomials, and the Fast Fourier Transform. Applications may include regression problems in statistics, audio and image processing, and the calculation of properties of molecules. Homework will be assigned frequently. Each assignment will involve both mathematical theory and python programming. There will be no exams. Instead, each student will pursue an open-ended project related to a topic discussed in class.
Introduction to the application of computational methods to models arising in science and engineering, concentrating mainly the numerical solution of ordinary, partial differential equations, and stochastic simulations of particle systems. Topics include finite differences, finite elements, spectral methods, boundary value problems, ODE integrators, and fast Fourier transforms. If time permits we will discuss nonlinear optimisation methods with applications to data science.
This third version of the book contains the standard elements from a first(and second) course in numerical analysis.The level is starting undergraduate and is somewhat raised towards the end.Chapter 1 serves as an introductionincluding definitions, theorems, and proofs from calculus(others calculus issues are scattered throughout the book and some proofs are moved to an appendix, but numerics are on the forefront throughout),elements from (rounding) error analysis, anda brief history of computing (other historical remarks are added as footnotes throughout).Chapter 2 is a teaser showing some elementary numerics and how they can be applied to solve more complex numerical problems,which is a strategy followed in subsequent chapters too:divided differences approximating a derivative are used inEuler's method, linear interpolation is used in the trapezoidal rule, and tridiagonal systems areapplied to solve boundary value problems.The following chapters go through the different standard problems and methods inall details and variations: root finding (ch. 3), interpolation andapproximation by polynomials and splines (ch. 4),numerical integration including polynomial interpolatory rules, Gaussian quadratureand Romberg integration (ch. 5),ordinary differential equations includes Runge-Kutta, multistep methods,adaptive methods, boundary value methods and finite elements (ch. 6).The remaining chaptersdeal with linear and nonlinear systems of equations (ch. 7),eigenvalue problems (ch. 8), partial differential equations (ch. 9),and spectral methods (ch. 10). They introduce the basic elements as before,but they are a bit less elaborate, especially when it comes to more advancedaspects. For example QR factorization isnot fully explained but still used as an eigenvalue method and theshorter chapters 9 and 10 are basically surveys.
This course provides an in-depth view of practical algorithms for solving large-scale linear systems of equations arising in the numerical implementation of various problems in mathematics, engineering and other applications. The dimension of such systems can easily exceed a million equations. On the other hand, many such systems are sparse, in the sense that each equation involves only a small number of unknowns. The course will focus on iterative methods, i.e. methods providing sequences of approximated solutions converging to that of the original problem. 041b061a72