Linear Algebra and Matrix Theory - Tapa dura

1. REAL COORDINATE SPACES. The Vector Spaces Rn. Linear Independence. Subspaces of Rn. Spanning Sets. Geometric Interpretations of R^2 and R^3. Bases and Dimension. 2. ELEMENTARY OPERATIONS ON VECTORS. Elementary Operations and Their Inverses. Elementary Operations and Linear Independence. Standard Bases for Subspaces. 3. MATRIX MULTIPLICATION. Matrices of Transition. Properties of Matrix Multiplication. Invertible Matrices. Column Operations and Column-Echelon Forms. Row Operations and Row-Echelon Forms. Row and Column Equivalence. Rank and Equivalence. LU Decompositions. 4. VECTOR SPACES, MATRICES, AND LINEAR EQUATIONS. Vector Spaces. Subspaces and Related Concepts. Isomorphisms of Vector Spaces. Standard Bases for Subspaces. Matrices over an Arbitrary Field. Systems of Linear Equations. More on Systems of Linear Equations. 5. LINEAR TRANSFORMATIONS. Linear Transformations. Linear Transformations and Matrices. Change of Basis. Composition of Linear Transformations. 6. DETERMINANTS. Permutations and Indices. The Definition of a Determinant. Cofactor Expansions. Elementary Operations and Cramer's Rule. Determinants and Matrix Multiplication. 7. EIGENVALUES AND EIGENVECTORS. Eigenvalues and Eigenvectors. Eigenspaces and Similarity. Representation by a Diagonal Matrix. 8. FUNCTIONS OF VECTORS. Linear Functionals. Real Quadratic Forms. Orthogonal Matrices. Reduction of Real Quadratic Forms. Classification of Real Quadratic Forms. Binlinear Forms. Symmetric Bilinear Forms. Hermitian Forms. 9. INNER PRODUCT SPACES. Inner Products. Norms and Distances. Orthonormal Bases. Orthogonal Complements. Isometrics. Normal Matrices. Normal Linear Operators. 10. SPECTRAL DECOMPOSITIONS. Projections and Direct Sums. Spectral Decompositions. Minimal Polynomials and Spectral Decompositions. Nilpotent Transformations. The Jordan Canonical Form. 11. NUMERICAL METHODS. Sequences and Series of Vectors. Sequences and Series of Matrices. The Standard Method of Iteration. Cimmino's Method. An Iterative Method for Determining Eigenvalues.

Master linear algebra with LINEAR ALGEBRA AND MATRIX THEORY! With coverage of the classification of bilinear forms, normal matrices, spectral decompositions, the Jordan form, and sequences and series of matrices, this mathematics text prepares you to succeed in this course and in math courses in your future. Numerous examples and exercises illustrate the theories and provide you with the practice you need to master difficult concepts.