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Introduction to scientific computing...

Holmes, Mark H.

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Introduction to scientific computing and data analysis

Record Type:	Electronic resources : Monograph/item
Title/Author:	Introduction to scientific computing and data analysis/ by Mark H. Holmes.
Author:	Holmes, Mark H.
Published:	Cham :Springer International Publishing : : 2023.,
Description:	xvi, 554 p. :ill., digital ;24 cm.
[NT 15003449]:	Preface -- Preface to Second Edition -- Introduction to Scientific Computing -- Solving a Nonlinear Equation -- Matrix Equations -- Eigenvalue Problems -- Interpolation -- Numerical Integration -- Initial Value Problems -- Optimization: Regression -- Optimization: Descent Methods -- Data Analysis -- Taylor's Theorem -- Vector and Matrix Summary -- Answers -- References -- Index.
Contained By:	Springer Nature eBook
Subject:	Science - Data processing. -
Online resource:	https://doi.org/10.1007/978-3-031-22430-0
ISBN:	9783031224300

Introduction to scientific computing and data analysis
Holmes, Mark H.

Introduction to scientific computing and data analysis[electronic resource] /by Mark H. Holmes. - Second edition. - Cham :Springer International Publishing :2023. - xvi, 554 p. :ill., digital ;24 cm. - Texts in computational science and engineering,v. 132197-179X ;. - Texts in computational science and engineering ;v. 13..

Preface -- Preface to Second Edition -- Introduction to Scientific Computing -- Solving a Nonlinear Equation -- Matrix Equations -- Eigenvalue Problems -- Interpolation -- Numerical Integration -- Initial Value Problems -- Optimization: Regression -- Optimization: Descent Methods -- Data Analysis -- Taylor's Theorem -- Vector and Matrix Summary -- Answers -- References -- Index.

This textbook provides an introduction to numerical computing and its applications in science and engineering. The topics covered include those usually found in an introductory course, as well as those that arise in data analysis. This includes optimization and regression-based methods using a singular value decomposition. The emphasis is on problem solving, and there are numerous exercises throughout the text concerning applications in engineering and science. The essential role of the mathematical theory underlying the methods is also considered, both for understanding how the method works, as well as how the error in the computation depends on the method being used. The codes used for most of the computational examples in the text are available on GitHub. This new edition includes material necessary for an upper division course in computational linear algebra.

ISBN: 9783031224300

Standard No.: 10.1007/978-3-031-22430-0doiSubjects--Topical Terms:

534323
Science
--Data processing.

LC Class. No.: Q183.9

Dewey Class. No.: 502.85

Introduction to scientific computing and data analysis
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245 1 0 $a Introduction to scientific computing and data analysis $h [electronic resource] / $c by Mark H. Holmes.
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300 $a xvi, 554 p. : $b ill., digital ; $c 24 cm.
490 1 $a Texts in computational science and engineering, $x 2197-179X ; $v v. 13
505 0 $a Preface -- Preface to Second Edition -- Introduction to Scientific Computing -- Solving a Nonlinear Equation -- Matrix Equations -- Eigenvalue Problems -- Interpolation -- Numerical Integration -- Initial Value Problems -- Optimization: Regression -- Optimization: Descent Methods -- Data Analysis -- Taylor's Theorem -- Vector and Matrix Summary -- Answers -- References -- Index.
520 $a This textbook provides an introduction to numerical computing and its applications in science and engineering. The topics covered include those usually found in an introductory course, as well as those that arise in data analysis. This includes optimization and regression-based methods using a singular value decomposition. The emphasis is on problem solving, and there are numerous exercises throughout the text concerning applications in engineering and science. The essential role of the mathematical theory underlying the methods is also considered, both for understanding how the method works, as well as how the error in the computation depends on the method being used. The codes used for most of the computational examples in the text are available on GitHub. This new edition includes material necessary for an upper division course in computational linear algebra.
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