000			01747nam a22003737a 4500
003			IIITD
005			20250529142851.0
008			250521b |||||||| |||| 00| 0 eng d
020			_a9780321982384
040			_aIIITD
082	0	0	_aREF 512.5 _bLAY-L
100	1		_aLay, David C.
245	1	0	_aLinear algebra and its applications _cby David C. Lay, Steven R. Lay and Judith McDonald
250			_a5th ed.
260			_aNew york : _bPearson, _c©2016
300			_axvi, 494 p. : _bill. ; _c26 cm.
500			_aIncludes index
505			_t1. Linear equations in linear algebra
505			_t2. Matrix algebra
505			_t3. Determinants
505			_t4. Vector spaces
505			_t5. Eigenvalues and eigenvectors
505			_t6. Orthogonality and least squares
505			_t7. Symmetric matrices and quadratic forms
505			_t8. The geometry of vector spaces
505			_t9. Optimization (online)
505			_t10. Finite-state markov chains (online)
520			_aFor courses in linear algebra. With traditional linear algebra texts, the course is relatively easy for students during the early stages as material is presented in a familiar, concrete setting. However, when abstract concepts are introduced, students often hit a wall. Instructors seem to agree that certain concepts (such as linear independence, spanning, subspace, vector space, and linear transformations) are not easily understood and require time to assimilate. These concepts are fundamental to the study of linear algebra, so students' understanding of them is vital to mastering the subject.
650		0	_aMathematics
650		0	_aLinear Algebra
700	1		_aLay, Steven R.
700	1		_aMcDonald, Judith J.
942			_2ddc _cBK
999			_c208652 _d208652