000			02887 a2200301 4500
003			NISER
005			20260122144843.0
008			260121b |||||||| |||| 00| 0 hin d
020			_a9783031313424 _qPaperback
040			_aNISER LIBRARY _beng _cNISER LIBRARY
082	0	4	_a517.521 _bDAM-N
100	1		_aD'Ambrosio, Raffaele
245	1	0	_aNumerical approximation of ordinary differential problems : _bfrom deterministic to stochastic numerical methods
260			_aCham, Switzerland : _bSpringer, _c2023.
300			_axiv, 385 pages : _billustrations (62 b/w illustrations) ; _c23.5 cm.
490			_aUNITEXT - la mathematica per il 3+2 : _vv. 148 _x2038-5714
504			_aIncludes bibliographical references and index.
520			_aThis book is focused on the numerical discretization of ordinary differential equations (ODEs), under several perspectives. The attention is first conveyed to providing accurate numerical solutions of deterministic problems. Then, the presentation moves to a more modern vision of numerical approximation, oriented to reproducing qualitative properties of the continuous problem along the discretized dynamics over long times. The book finally performs some steps in the direction of stochastic differential equations (SDEs), with the intention of offering useful tools to generalize the techniques introduced for the numerical approximation of ODEs to the stochastic case, as well as of presenting numerical issues natively introduced for SDEs. The book is the result of an intense teaching experience as well as of the research carried out in the last decade by the author. It is both intended for students and instructors: for the students, this book is comprehensive and ratherself-contained; for the instructors, there is material for one or more monographic courses on ODEs and related topics. In this respect, the book can be followed in its designed path and includes motivational aspects, historical background, examples and a software programs, implemented in Matlab, that can be useful for the laboratory part of a course on numerical ODEs/SDEs. The book also contains the portraits of several pioneers in the numerical discretization of differential problems, useful to provide a framework to understand their contributes in the presented fields. Last, but not least, rigor joins readability in the book.
650		0	_aMathematical analysis
650		0	_aMathematics _xData processing
650		0	_aNumerical analysis
650		0	_aAnalysis
650		0	_aComputational Mathematics and Numerical Analysis
650		0	_aStochastic Numerics
856	4	1	_3Table of contents _uhttps://link.springer.com/content/pdf/bfm:978-3-031-31343-1/1
856	4	1	_3Reviews _uhttps://www.goodreads.com/book/show/124033936-numerical-approximation-of-ordinary-differential-problems?ref=nav_sb_ss_1_13#CommunityReviews
942			_cBK _2udc
999			_c36785 _d36785