Linear Algebra and Its Applications

Gilbert Strang is Professor of Mathematics at the Massachusetts Institute of Technology and an Honorary Fellow of Balliol College. He was an undergraduate at MIT and a Rhodes Scholar at Oxford. His doctorate was from UCLA and since then he has taught at MIT. He has been a Sloan Fellow and a Fairchild Scholar and is a Fellow of the American Academy of Arts and Sciences. Professor Strang has published a monograph with George Fix, An Analysis of the Finite Element Method", and has authored six widely used textbooks. He served as President of SIAM during 1999 and 2000 and he is Chair of the U.S. National Committee on Mathematics for 2003-2004."

1. MATRICES AND GAUSSIAN ELIMINATION.
Introduction. The Geometry of Linear Equations. An Example of Gaussian Elimination. Matrix Notation and Matrix Multiplication. Triangular Factors and Row Exchanges. Inverses and Transposes. Special Matrices and Applications. Review Exercises.
2. VECTOR SPACES.
Vector Spaces and Subspaces. The Solution of m Equations in n Unknowns. Linear Independence, Basis, and Dimension. The Four Fundamental Subspaces. Networks and Incidence Matrices. Linear Transformations. Review Exercises.
3. ORTHOGONALITY.
Perpendicular Vectors and Orthogonal Subspaces. Inner Products and Projections onto Lines. Least Squares Approximations. Orthogonal Bases, Orthogonal Matrices, and Gram-Schmidt Orthogonalization. The Fast Fourier Transform. Review and Preview. Review Exercises.
4. DETERMINANTS.
Introduction. Properties of the Determinant. Formulas for the Determinant. Applications of Determinants. Review Exercises.
5. EIGENVALUES AND EIGENVECTORS.
Introduction. Diagonalization of a Matrix. Difference Equations and the Powers Ak. Differential Equations and the Exponential eAt. Complex Matrices: Symmetric vs. Hermitian. Similarity Transformations. Review Exercises.
6. POSITIVE DEFINITE MATRICES.
Minima, Maxima, and Saddle Points. Tests for Positive Definiteness. The Singular Value Decomposition. Minimum Principles. The Finite Element Method.
7. COMPUTATIONS WITH MATRICES.
Introduction. The Norm and Condition Number. The Computation of Eigenvalues. Iterative Methods for Ax = b.
8. LINEAR PROGRAMMING AND GAME THEORY.
Linear Inequalities. The Simplex Method. Primal and Dual Programs. Network Models. Game Theory.
Appendix A: Computer Graphics.
Appendix B: The Jordan Form.
References.
Solutions to Selected Exercises.
Index.