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Numerical analysis = a graduate course /

Stewart, David.

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Numerical analysis = a graduate course /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Numerical analysis/ by David E. Stewart.
其他題名:	a graduate course /
作者:	Stewart, David.
出版者:	Cham :Springer International Publishing : : 2022.,
面頁冊數:	xv, 632 p. :ill., digital ;24 cm.
內容註:	Basics of mathematical computation -- Computing with Matrices and Vectors -- Solving nonlinear equations -- Approximations and interpolation -- Integration and differentiation -- Differential equations -- Randomness -- Optimization -- Appendix A: What you need from analysis.
Contained By:	Springer Nature eBook
標題:	Numerical analysis. -
電子資源:	https://doi.org/10.1007/978-3-031-08121-7
ISBN:	9783031081217

Numerical analysis = a graduate course /
Stewart, David.

Numerical analysisa graduate course /[electronic resource] :by David E. Stewart. - Cham :Springer International Publishing :2022. - xv, 632 p. :ill., digital ;24 cm. - CMS/CAIMS books in mathematics,v. 42730-6518 ;. - CMS/CAIMS books in mathematics ;v. 4..

Basics of mathematical computation -- Computing with Matrices and Vectors -- Solving nonlinear equations -- Approximations and interpolation -- Integration and differentiation -- Differential equations -- Randomness -- Optimization -- Appendix A: What you need from analysis.

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.

ISBN: 9783031081217

Standard No.: 10.1007/978-3-031-08121-7doiSubjects--Topical Terms:

517751
Numerical analysis.

LC Class. No.: QA297

Dewey Class. No.: 518

Numerical analysis = a graduate course /
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520 $a 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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