추가 적립 안내

Ch. 1. Vectors. 1.
1.1. Vectors and Matrices in Engineering and Mathematics; n-Space. 1.
1.2. Dot Product and Orthogonality. 15.
1.3. Vector Equations of Lines and Planes. 29.
Ch. 2. Systems of Linear Equations. 39.
2.1. Introduction to Systems of Linear Equations. 39.
2.2. Solving Linear Systems by Row Reduction. 48.
2.3. Applications of Linear Systems. 63.
Ch. 3. Matrices and Matrix Algebra. 79.
3.1. Operations on Matrices. 79.
3.2. Inverses; Algebraic Properties of Matrices. 94.
3.3. Elementary Matrices; A Method for Finding A[superscript -1]. 109.
3.4. Subspaces and Linear Independence. 123.
3.5. The Geometry of Linear Systems. 135.
3.6. Matrices with Special Forms. 143.
3.7. Matrix Factorizations; LU-Decomposition. 154.
3.8. Partitioned Matrices and Parallel Processing. 166.
Ch. 4. Determinants. 175.
4.1. Determinants; Cofactor Expansion. 175.
4.2. Properties of Determinants. 184.
4.3. Cramer's Rule; Formula for A[superscript -1]: Applications of Determinants. 196.
4.4. A First Look at Eigenvalues and Eigenvectors. 210.
Ch. 5. Matrix Models. 225.
5.1. Dynamical Systems and Markov Chains. 225.
5.2. Leontief Input-Output Models. 235.
5.3. Gauss-Seidel and Jacobi Iteration; Sparse Linear Systems. 241.
5.4. The Power Method; Application to Internet Search Engines. 249.
Ch. 6. Linear Transformations. 265.
6.1. Matrices as Transformations. 265.
6.2. Geometry of Linear Operators. 280.
6.3. Kernel and Range. 296.
6.4. Composition and Invertibility of Linear Transformations. 305.
6.5. Computer Graphics. 318.
Ch. 7. Dimension and Structure. 329.
7.1. Basis and Dimension. 329.
7.2. Properties of Bases. 335.
7.3. The Fundamental Spaces of a Matrix. 342.
7.4. The Dimension Theorem and Its Implications. 352.
7.5. The Rank Theorem and Its Implications. 360.
7.6. The Pivot Theorem and Its Implications. 370.
7.7. The Projection Theorem and Its Implications. 379.
7.8. Best Approximation and Least Squares. 393.
7.9. Orthonormal Bases and the Gram-Schmidt Process. 406.
7.10. QR-Decomposition; Householder Transformations. 417.
7.11. Coordinates with Respect to a Basis. 426.
Ch. 8. Diagonalization. 443.
8.1. Matrix Representations of Linear Transformations. 443.
8.2. Similarity and Diagonalizability. 456.
8.3. Orthogonal Diagonalizability; Functions of a Matrix. 468.
8.4. Quadratic Forms. 481.
8.5. Application of Quadratic Forms to Optimization. 495.
8.6. Singular Value Decomposition. 502.
8.7. The Pseudoinverse. 518.
8.8. Complex Eigenvalues and Eigenvectors. 525.
8.9. Hermitian, Unitary, and Normal Matrices. 535.
8.10. Systems of Differential Equations. 542.
Ch. 9. General Vector Spaces. 555.
9.1. Vector Space Axioms. 555.
9.2. Inner Product Spaces; Fourier Series. 569.
9.3. General Linear Transformations; Isomorphism. 582.
App. A. How to Read Theorems
App. B. Complex Numbers
Answers to Odd-Numbered Exercises
Photo Credits
Index

From one of the premier authors in higher education comes a new linear algebra textbook that fosters mathematical thinking, problem-solving abilities, and exposure to real-world applications. Without sacrificing mathematical precision, Anton and Busby focus on the aspects of linear algebra that are most likely to have practical value to the student while not compromising the intrinsic mathematical form of the subject. Throughout Contemporary Linear Algebra, students are encouraged to look at ideas and problems from multiple points of view.