Description

Book Synopsis

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.



Table of Contents

Vectors and linear systems. Solving linear systems. Vector spaces. Orthogonality. Determinants. Eigenvalues and Eigenvectors. Singular value decomposition.

Concise Introduction to Linear Algebra

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    A Hardback by Qingwen Hu

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      View other formats and editions of Concise Introduction to Linear Algebra by Qingwen Hu

      Publisher: CRC Press
      Publication Date: 9/21/2017 12:00:00 AM
      ISBN13: 9781138044494, 978-1138044494
      ISBN10: 1138044490

      Description

      Book Synopsis

      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.



      Table of Contents

      Vectors and linear systems. Solving linear systems. Vector spaces. Orthogonality. Determinants. Eigenvalues and Eigenvectors. Singular value decomposition.

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