000			02085nam a22003618i 4500
001			CR9781139108188
005			20250919142046.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			110713s2014||||enk o ||1 0|eng|d
020			_a9781139108188 (ebook)
020			_z9781107021082 (hardback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aQA297 _b.M526 2014
082	0	0	_a518 _223
100	1		_aMiller, G., _eauthor.
245	1	0	_aNumerical analysis for engineers and scientists / _cG. Miller, Department of Chemical Engineering and Materials Science, University of California, Davis.
246	3		_aNumerical Analysis for Engineers & Scientists
264		1	_aCambridge : _bCambridge University Press, _c2014.
300			_a1 online resource (x, 572 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aTitle from publisher's bibliographic system (viewed on 05 Oct 2015).
520			_aStriking a balance between theory and practice, this graduate-level text is perfect for students in the applied sciences. The author provides a clear introduction to the classical methods, how they work and why they sometimes fail. Crucially, he also demonstrates how these simple and classical techniques can be combined to address difficult problems. Many worked examples and sample programs are provided to help the reader make practical use of the subject material. Further mathematical background, if required, is summarized in an appendix. Topics covered include classical methods for linear systems, eigenvalues, interpolation and integration, ODEs and data fitting, and also more modern ideas like adaptivity and stochastic differential equations.
650		0	_aNumerical analysis.
776	0	8	_iPrint version: _z9781107021082
856	4	0	_uhttps://doi.org/10.1017/CBO9781139108188
907			_a.b16846187 _b2020-12-22 _c2020-12-22
942			_n0
998			_a1 _b2020-12-22 _cm _da _feng _genk _y0 _z.b16846187
999			_c651961 _d651961