Using R for numerical analysis in science and engineering / Victor A. Bloomfield, University of Minnesota, Minneapolis, USA.
By: Bloomfield, Victor A [author.].
Description: xxii, 335 pages : illustrations ; 25 cm.ISBN: 143988448X (hardback : acidfree paper); 9781439884485 (hardback : acidfree paper).Subject(s): Engineering -- Data processing | Numerical analysis | R (Computer program language) | Science -- Data processing | MATHEMATICS / General | MATHEMATICS / Number Systems | MATHEMATICS / Probability & Statistics / GeneralGenre/Form: Print books.DDC classification: 518.0285/5133 Summary: "The complex mathematical problems faced by scientists and engineers rarely can be solved by analytical approaches, so numerical methods are often necessary. There are many books that deal with numerical methods for scientists and engineers; their content is fairly standardized: solution of systems of linear algebraic equations and nonlinear equations, finding eigenvalues and eigenfunctions, interpolation and curve fitting, numerical differentiation and integration, optimization, solution of ordinary differential equations and partial differential equations, and Fourier analysis. Sometimes statistical analysis of data is included, as it should be. As powerful personal computers have become virtually universal on the desks of scientists and engineers, computationally intensive Monte Carlo methods are joining the numerical analysis armamentarium. If there are many books on these well-established topics, why am I writing another one? The answer is to propose and demonstrate the use of a language relatively new to the field: R. My approach in this book is not to present the standard theoretical treatments that underlie the various numerical methods used by scientists and engineers. There are many fine books and online resources that do that, including one that uses R: Owen Jones, Robert Maillardet, and Andrew Robinson. Introduction to Scientific Programming and Simulation Using R. Chapman & Hall/CRC, Boca Raton, FL, 2009. Instead, I have tried to write a guide to the capabilities of R and its add-on packages in the realm of numerical methods, with simple but useful examples of how the most pertinent functions can be employed in practical situations"--Summary: "This book shows how the free and open-source R environment can be used as a powerful and comprehensive platform for the kinds of numerical analysis that are traditionally employed by MATLAB®. With R code fully integrated, the book offers brief descriptions of basic approaches and emphasizes detailed worked examples. It covers functions in the base installation of R as well as those in contributed packages, which greatly enhance the numerical analysis capabilities of R"--| Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|
| On Shelf | Q183.9 .B56 2014 (Browse shelf) | Available | AU0000000004410 |
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| Q181 .G536 2015 Reading and writing in science : tools to develop disciplinary literacy / | Q182.3 .C647 2021 Get your data from experiments / | Q183.3.A1 R828 2019 How we teach science : what's changed, and why it matters / | Q183.9 .B56 2014 Using R for numerical analysis in science and engineering / | Q183.9 .F37 2008 Mathematical principles for scientific computing and visualization / | Q183.9 .H58 2015 Learning scientific programming with Python / | Q183.9 .P734 2017 Getting started with MATLAB : a quick introduction for scientists and engineers / |
"A Chapman & Hall book."
Includes bibliographical references (pages 329-330) and index.
"The complex mathematical problems faced by scientists and engineers rarely can be solved by analytical approaches, so numerical methods are often necessary. There are many books that deal with numerical methods for scientists and engineers; their content is fairly standardized: solution of systems of linear algebraic equations and nonlinear equations, finding eigenvalues and eigenfunctions, interpolation and curve fitting, numerical differentiation and integration, optimization, solution of ordinary differential equations and partial differential equations, and Fourier analysis. Sometimes statistical analysis of data is included, as it should be. As powerful personal computers have become virtually universal on the desks of scientists and engineers, computationally intensive Monte Carlo methods are joining the numerical analysis armamentarium. If there are many books on these well-established topics, why am I writing another one? The answer is to propose and demonstrate the use of a language relatively new to the field: R. My approach in this book is not to present the standard theoretical treatments that underlie the various numerical methods used by scientists and engineers. There are many fine books and online resources that do that, including one that uses R: Owen Jones, Robert Maillardet, and Andrew Robinson. Introduction to Scientific Programming and Simulation Using R. Chapman & Hall/CRC, Boca Raton, FL, 2009. Instead, I have tried to write a guide to the capabilities of R and its add-on packages in the realm of numerical methods, with simple but useful examples of how the most pertinent functions can be employed in practical situations"--
"This book shows how the free and open-source R environment can be used as a powerful and comprehensive platform for the kinds of numerical analysis that are traditionally employed by MATLAB®. With R code fully integrated, the book offers brief descriptions of basic approaches and emphasizes detailed worked examples. It covers functions in the base installation of R as well as those in contributed packages, which greatly enhance the numerical analysis capabilities of R"--