000			01612 a2200241 4500
003			OSt
005			20250106120416.0
008			250103b xxu||||| |||| 00| 0 eng d
020			_a9783031081231
040			_cIIT Kanpur
041			_aeng
082			_a518 _bSt49n
100			_aStewart, David E.
245			_aNumerical analysis _ba graduate course _cDavid E. Stewart
260			_bSpringer _c2022 _aSwitzerland
300			_axv, 632p
440			_aCMS/CAIMS Books in mathematics
490			_a/ edited by Karl Dilcher ...[et al.] _v; v. 4
520			_aThis 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.
650			_aNumerical analysis
650			_aMathematics
942			_cBK
999			_c567318 _d567318