000			03119nam a2200349 i 4500
003			CSPC
005			20230810091638.0
008			230804s2021 nju b 001 0 eng
010			_a 2020057459
020			_a9789811229343 _q(paperback)
040			_aDLC _beng _erda _cDLC _dCSPC
042			_apcc
050	0	0	_aQA297 _b.L336 2021
082	0	0	_a518.02855133 _bL171e
100	1		_aLambers, James V., _eauthor.
245	1	0	_aExplorations in numerical analysis : _bpython edition / _cJames V. Lambers, Amber Sumner Mooney, and Vivian A. Montiforte.
264		1	_aNew Jersey : _bWorld Scientific Publishing Co., _c2021.
300			_axv, 674 pages ; _c26 cm.
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
504			_aIncludes bibliographical references (pages 657-666) and index.
505	0		_aPart I Preliminaries -- 1. What is numerical analysis? -- 2. Understanding error -- Part II Numerical linear algebra -- 3. Direct methods for linear systems -- 4. Least squares problems -- 5. Iterative methods for linear systems -- 6. Eigenvalue problems -- Part III Data fitting and function approximation -- 7. Polynomial interpolation -- 8. Approximation of functions -- Part IV Nonlinear equations and optimization -- 10. Zeros of nonlinear functions -- 11. Optimization -- Part V Differential equations -- 12. Initial value problems -- Two-point boundary value problems -- 14. Partial differential equations.
520			_a"This textbook is intended to introduce advanced undergraduate and early-career graduate students to the field of numerical analysis. This field pertains to the design, analysis, and implementation of algorithms for the approximate solution of mathematical problems that arise in applications spanning science and engineering, and are not practical to solve using analytical techniques such as those taught in courses in calculus, linear algebra or differential equations. Topics covered include computer arithmetic, error analysis, solution of systems of linear equations, least squares problems, eigenvalue problems, nonlinear equations, optimization, polynomial interpolation and approximation, numerical differentiation and integration, ordinary differential equations, and partial differential equations. For each problem considered, the presentation includes the derivation of solution techniques, analysis of their efficiency, accuracy and robustness, and details of their implementation, illustrated through the Python programming language. This text is suitable for a year-long sequence in numerical analysis, and can also be used for a one-semester course in numerical linear algebra"-- _cProvided by publisher.
650		0	_aNumerical analysis.
650		0	_aNumerical analysis _vProblems, exercises, etc.
650		0	_aPython (Computer program language)
700	1		_aMooney, Amber Sumner, _eauthor.
700	1		_aMontiforte, Vivian A., _eauthor.
942			_2ddc _cBK _h518.02855133 _iL171e _kCIR _m2021 _n0
999			_c25842 _d25842