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Concise Introduction to Linear Algebra / Qingwen Hu.

By: Hu, Qingwen [author.].
Material type: materialTypeLabelBookPublisher: Boca Raton, FL : CRC Press, 2017Edition: First edition.Description: 1 online resource : text file, PDF.Content type: text Media type: computer Carrier type: online resourceISBN: 9781315172309; 1315172305; 9781351697460; 1351697463; 9781351697446; 1351697447.Subject(s): MATHEMATICS -- Applied | Algebras, LinearDDC classification: 512.5 Online resources: Taylor & Francis Distributed by publisher. Purchase or institutional license may be required for access. | Taylor & Francis Click here to view. | OCLC metadata license agreement
Contents:
Chapter 1 Vectors and linear systems -- chapter 2 Solving linear systems -- chapter 3 Vector spaces -- chapter 4 Orthogonality -- chapter 5 Determinants -- chapter 6 Eigenvalues and eigenvectors -- chapter 7 Singular value decomposition -- chapter 8 Linear transformations -- chapter 9 Linear programming.
Scope and content: "Concise Introduction to Linear Algebra deals with the subject of linear algebra, covering vectors and linear systems, vector spaces, orthogonality, determinants, eigenvalues and eigenvectors, singular value decomposition. It adopts an efficient approach to lead students from vectors, matrices quickly into more advanced topics including, LU decomposition, orthogonal decomposition, Least squares solutions, Gram-Schmidt process, eigenvalues and eigenvectors, diagonalizability, spectral decomposition, positive definite matrix, quadratic forms, singular value decompositions and principal component analysis. This book is designed for onesemester teaching to undergraduate students."--Provided by publisher.
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"Concise Introduction to Linear Algebra deals with the subject of linear algebra, covering vectors and linear systems, vector spaces, orthogonality, determinants, eigenvalues and eigenvectors, singular value decomposition. It adopts an efficient approach to lead students from vectors, matrices quickly into more advanced topics including, LU decomposition, orthogonal decomposition, Least squares solutions, Gram-Schmidt process, eigenvalues and eigenvectors, diagonalizability, spectral decomposition, positive definite matrix, quadratic forms, singular value decompositions and principal component analysis. This book is designed for onesemester teaching to undergraduate students."--Provided by publisher.

Chapter 1 Vectors and linear systems -- chapter 2 Solving linear systems -- chapter 3 Vector spaces -- chapter 4 Orthogonality -- chapter 5 Determinants -- chapter 6 Eigenvalues and eigenvectors -- chapter 7 Singular value decomposition -- chapter 8 Linear transformations -- chapter 9 Linear programming.

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