Matrix Analysis and Applied Linear Algebra Book and Solutions Manualtxt,chm,pdf,epub,mobi下载 作者:Carl D. Meyer 出版社: SIAM: Society for Industrial and Applied Mathematics 出版年: 2001-02-15 页数: 700 定价: USD 97.00 装帧: Textbook Binding ISBN: 9780898714548 内容简介 · · · · · ·Matrix Analysis and Applied Linear Algebra is an honest math text that circumvents the traditional definition-theorem-proof format that has bored students in the past. Meyer uses a fresh approach to introduce a variety of problems and examples ranging from the elementary to the challenging and from simple applications to discovery problems. The focus on applications is a big di... 目录 · · · · · ·Chapter 1: Linear Equations.Introduction; Gaussian Elimination and Matrices; Gauss-Jordan Method; Two-Point Boundary-Value Problems; Making Gaussian Elimination Work; Ill-Conditioned Systems Chapter 2: Rectangular Systems and Echelon Forms. Row Echelon Form and Rank; The Reduced Row Echelon Form; Consistency of Linear Systems; Homogeneous Systems; Nonhomogeneous Systems; Electrical Circuits Chapter 3: Matrix Algebra. From Ancient China to Arthur Cayley; Addition, Scalar Multiplication, and Transposition; Linearity; Why Do It This Way?; Matrix Multiplication; Properties of Matrix Multiplication; Matrix Inversion; Inverses of Sums and Sensitivity; Elementary Matrices and Equivalence; The LU Factorization · · · · · ·() Chapter 1: Linear Equations. Introduction; Gaussian Elimination and Matrices; Gauss-Jordan Method; Two-Point Boundary-Value Problems; Making Gaussian Elimination Work; Ill-Conditioned Systems Chapter 2: Rectangular Systems and Echelon Forms. Row Echelon Form and Rank; The Reduced Row Echelon Form; Consistency of Linear Systems; Homogeneous Systems; Nonhomogeneous Systems; Electrical Circuits Chapter 3: Matrix Algebra. From Ancient China to Arthur Cayley; Addition, Scalar Multiplication, and Transposition; Linearity; Why Do It This Way?; Matrix Multiplication; Properties of Matrix Multiplication; Matrix Inversion; Inverses of Sums and Sensitivity; Elementary Matrices and Equivalence; The LU Factorization Chapter 4: Vector Spaces. Spaces and Subspaces; Four Fundamental Subspaces; Linear Independence; Basis and Dimension; More About Rank; Classical Least Squares; Linear Transformations; Change of Basis and Similarity; Invariant Subspaces Chapter 5: Norms, Inner Products, and Orthogonality. Vector Norms; Matrix Norms; Inner Product Spaces; Orthogonal Vectors; Gram-Schmidt Procedure; Unitary and Orthogonal Matrices; Orthogonal Reduction; The Discrete Fourier Transform; Complementary Subspaces; Range-Nullspace Decomposition; Orthogonal Decomposition; Singular Value Decomposition; Orthogonal Projection; Why Least Squares?; Angles Between Subspaces Chapter 6: Determinants. Determinants; Additional Properties of Determinants Chapter 7: Eigenvalues and Eigenvectors. Elementary Properties of Eigensystems; Diagonalization by Similarity Transformations; Functions of Diagonalizable Matrices; Systems of Differential Equations; Normal Matrices; Positive Definite Matrices; Nilpotent Matrices and Jordan Structure; The Jordan Form; Functions of Non-diagonalizable Matrices; Difference Equations, Limits, and Summability; Minimum Polynomials and Krylov Methods Chapter 8: Perron-Frobenius Theory of Nonnegative Matrices. Introduction; Positive Matrices; Nonnegative Matrices; Stochastic Matrices and Markov Chains. · · · · · · () |
推荐给了朋友
这本书让我生气了,知道了。
内容的话,谈到了很多方面
生动有趣的诠释了