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＜E-Book＞
Numerical Analysis: A Graduate Course / by David E. Stewart
(CMS/CAIMS Books in Mathematics. ISSN:27306518 ; 4)

Edition	1st ed. 2022.
Publisher	(Cham : Springer International Publishing : Imprint: Springer)
Year	2022
Language	English
Size	XV, 632 p. 114 illus., 66 illus. in color : online resource
Authors	*Stewart, David E author SpringerLink (Online service)
Subjects	LCSH:Numerical analysis LCSH:Differential equations FREE:Numerical Analysis FREE:Differential Equations
Notes	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. HTTP:URL=https://doi.org/10.1007/978-3-031-08121-7

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