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Scientific Computing.. Vol. II,. Eig...

Trangenstein, John A.

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Scientific Computing.. Vol. II,. Eigenvalues and optimization

Record Type:	Electronic resources : Monograph/item
Title/Author:	Scientific Computing./ by John A. Trangenstein.
remainder title:	Eigenvalues and optimization
Author:	Trangenstein, John A.
Published:	Cham :Springer International Publishing : : 2017.,
Description:	xxvi, 600 p. :ill., digital ;24 cm.
[NT 15003449]:	1. Eigenvalues and Eigenvectors -- 2. Iterative Linear Algebra -- 3. Nonlinear Systems -- 4. Constrained Optimization -- References -- Author Index.
Contained By:	Springer eBooks
Subject:	Eigenvalues. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-69107-7
ISBN:	9783319691077

Scientific Computing.. Vol. II,. Eigenvalues and optimization
Trangenstein, John A.

Scientific Computing.Vol. II,Eigenvalues and optimization[electronic resource] /Eigenvalues and optimizationby John A. Trangenstein. - Cham :Springer International Publishing :2017. - xxvi, 600 p. :ill., digital ;24 cm. - Texts in computational science and engineering,191611-0994 ;. - Texts in computational science and engineering ;19..

1. Eigenvalues and Eigenvectors -- 2. Iterative Linear Algebra -- 3. Nonlinear Systems -- 4. Constrained Optimization -- References -- Author Index.

This is the second of three volumes providing a comprehensive presentation of the fundamentals of scientific computing. This volume discusses more advanced topics than volume one, and is largely not a prerequisite for volume three. This book and its companions show how to determine the quality of computational results, and how to measure the relative efficiency of competing methods. Readers learn how to determine the maximum attainable accuracy of algorithms, and how to select the best method for computing problems. This book also discusses programming in several languages, including C++, Fortran and MATLAB. There are 49 examples, 110 exercises, 66 algorithms, 24 interactive JavaScript programs, 77 references to software programs and 1 case study. Topics are introduced with goals, literature references and links to public software. There are descriptions of the current algorithms in LAPACK, GSLIB and MATLAB. This book could be used for a second course in numerical methods, for either upper level undergraduates or first year graduate students. Parts of the text could be used for specialized courses, such as nonlinear optimization or iterative linear algebra.

ISBN: 9783319691077

Standard No.: 10.1007/978-3-319-69107-7doiSubjects--Topical Terms:

631789
Eigenvalues.

LC Class. No.: QA193

Dewey Class. No.: 512.9436

Scientific Computing.. Vol. II,. Eigenvalues and optimization
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245 1 0 $a Scientific Computing. $n Vol. II, $p Eigenvalues and optimization $h [electronic resource] / $c by John A. Trangenstein.
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505 0 $a 1. Eigenvalues and Eigenvectors -- 2. Iterative Linear Algebra -- 3. Nonlinear Systems -- 4. Constrained Optimization -- References -- Author Index.
520 $a This is the second of three volumes providing a comprehensive presentation of the fundamentals of scientific computing. This volume discusses more advanced topics than volume one, and is largely not a prerequisite for volume three. This book and its companions show how to determine the quality of computational results, and how to measure the relative efficiency of competing methods. Readers learn how to determine the maximum attainable accuracy of algorithms, and how to select the best method for computing problems. This book also discusses programming in several languages, including C++, Fortran and MATLAB. There are 49 examples, 110 exercises, 66 algorithms, 24 interactive JavaScript programs, 77 references to software programs and 1 case study. Topics are introduced with goals, literature references and links to public software. There are descriptions of the current algorithms in LAPACK, GSLIB and MATLAB. This book could be used for a second course in numerical methods, for either upper level undergraduates or first year graduate students. Parts of the text could be used for specialized courses, such as nonlinear optimization or iterative linear algebra.
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650 2 4 $a Computational Mathematics and Numerical Analysis. $3 891040
650 2 4 $a Ordinary Differential Equations. $3 891264
650 2 4 $a Optimization. $3 891104
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