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Introduction to scientific computing with Matlab and Python tutorials

By: Xu, Sheng.
Publisher: Boca Raton : CRC Press/ Chapman & Hall, 2022Description: xv, 381 p. ; ill., 25 cm.ISBN: 9781032063157.Subject(s): MATLAB | Numerical analysis Data processing | Science Data processing | Python | Computer program language | Abel-Ruffini theorem | Absolute error | Basic midpoint rule | Composite Saussian quandrature rule | Euler's difference method | Gauss-Seidel (G-S) method | Initial value problem | Iteration matrix | Linear convergence | Mean value theorem | Quadrature rule | Runge function | Simpson's rule theoremDDC classification: 519.5 Summary: This textbook is written for the first introductory course on scientific computing. It covers elementary numerical methods for linear systems, root finding, interpolation, numerical integration, numerical differentiation, least squares problems, initial value problems and boundary value problems. It includes short Matlab and Python tutorials to quickly get students started on programming. It makes the connection between elementary numerical methods with advanced topics such as machine learning and parallel computing. This textbook gives a comprehensive and in-depth treatment of elementary numerical methods. It balances the development, implementation, analysis and application of a fundamental numerical method by addressing the following questions. Where is the method applied? How is the method developed? How is the method implemented? How well does the method work? The material in the textbook is made as self-contained and easy-to-follow as possible with reviews and remarks. The writing is kept concise and precise. Examples, figures, paper-and-pen exercises and programming problems are deigned to reinforce understanding of numerical methods and problem-solving skills.
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Item type Current location Call number Status Date due Barcode
Books 519.5 XUS (Browse shelf) Checked out 15/05/2024 033335

Includes bibliographical references and index.

This textbook is written for the first introductory course on scientific computing. It covers elementary numerical methods for linear systems, root finding, interpolation, numerical integration, numerical differentiation, least squares problems, initial value problems and boundary value problems. It includes short Matlab and Python tutorials to quickly get students started on programming. It makes the connection between elementary numerical methods with advanced topics such as machine learning and parallel computing. This textbook gives a comprehensive and in-depth treatment of elementary numerical methods. It balances the development, implementation, analysis and application of a fundamental numerical method by addressing the following questions. Where is the method applied? How is the method developed? How is the method implemented? How well does the method work? The material in the textbook is made as self-contained and easy-to-follow as possible with reviews and remarks. The writing is kept concise and precise. Examples, figures, paper-and-pen exercises and programming problems are deigned to reinforce understanding of numerical methods and problem-solving skills.

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