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Numerical Analysis: A Graduate Course (CMS/CAIMS Books in Mathematics, 4, Band 4) - Softcover

Stewart, David E.

 
9783031081231: Numerical Analysis: A Graduate Course (CMS/CAIMS Books in Mathematics, 4, Band 4)
Softcover
ISBN 10: 3031081234 ISBN 13: 9783031081231
Verlag: Springer, 2023

Inhaltsangabe

This book aims to introduce graduate students to the many applications of numerical computation, explaining in detail both how and why the included methods work in practice. The text addresses numerical analysis as a middle ground between practice and theory, addressing both the abstract mathematical analysis and applied computation and programming models instrumental to the field. While the text uses pseudocode, Matlab and Julia codes are available online for students to use, and to demonstrate implementation techniques. The textbook also emphasizes multivariate problems alongside single-variable problems and deals with topics in randomness, including stochastic differential equations and randomized algorithms, and topics in optimization and approximation relevant to machine learning. Ultimately, it seeks to clarify issues in numerical analysis in the context of applications, and presenting accessible methods to students in mathematics and data science. 

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Über die Autorin bzw. den Autor

David Stewart is a Professor of Mathematics at the University of Iowa specializing in the area of numerical analysis. Much of his research work can be found in Dynamics with Inequalities: impacts and hard constraints (SIAM), which is on differential equations with discontinuities. His interests also include numerical optimization, mathematical modeling, and other aspects of differential equations.


Von der hinteren Coverseite

This book aims to introduce graduate students to the many applications of numerical computation, explaining in detail both how and why the included methods work in practice. The text addresses numerical analysis as a middle ground between practice and theory, addressing both the abstract mathematical analysis and applied computation and programming models instrumental to the field. While the text uses pseudocode, Matlab and Julia codes are available online for students to use, and to demonstrate implementation techniques. The textbook also emphasizes multivariate problems alongside single-variable problems and deals with topics in randomness, including stochastic differential equations and randomized algorithms, and topics in optimization and approximation relevant to machine learning. Ultimately, it seeks to clarify issues in numerical analysis in the context of applications, and presenting accessible methods to students in mathematics and data science. 

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Verlag
Springer
Erscheinungsdatum
2023
Sprache
Englisch
ISBN 10 
3031081234
ISBN 13 
9783031081231
Einband
Taschenbuch
Auflage
1
Anzahl der Seiten
647
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