Introduction to Linear Algebra

1. Introduction to Vectors: 1.1 Vectors and linear combinations; 1.2 Lengths and dot products; 1.3 Matrices; 2. Solving Linear Equations: 2.1 Vectors and linear equations; 2.2 The idea of elimination; 2.3 Elimination using matrices; 2.4 Rules for matrix operations; 2.5 Inverse matrices; 2.6 Elimination = factorization: A = LU; 2.7 Transposes and permutations; 3. Vector Spaces and Subspaces: 3.1 Spaces of vectors; 3.2 The nullspace of A: solving Ax = 0; 3.3 The rank and the row reduced form; 3.4 The complete solution to Ax = b; 3.5 Independence, basis and dimension; 3.6 Dimensions of the four subspaces; 4. Orthogonality: 4.1 Orthogonality of the four subspaces; 4.2 Projections; 4.3 Least squares approximations; 4.4 Orthogonal bases and Gram-Schmidt; 5. Determinants: 5.1 The properties of determinants; 5.2 Permutations and cofactors; 5.3 Cramer's rule, inverses, and volumes; 6. Eigenvalues and Eigenvectors: 6.1 Introduction to eigenvalues; 6.2 Diagonalizing a matrix; 6.3 Applications to differential equations; 6.4 Symmetric matrices; 6.5 Positive definite matrices; 6.6 Similar matrices; 6.7 Singular value decomposition (SVD); 7. Linear Transformations: 7.1 The idea of a linear transformation; 7.2 The matrix of a linear transformation; 7.3 Diagonalization and the pseudoinverse; 8. Applications: 8.1 Matrices in engineering; 8.2 Graphs and networks; 8.3 Markov matrices, population, and economics; 8.4 Linear programming; 8.5 Fourier series: linear algebra for functions; 8.6 Linear algebra for statistics and probability; 8.7 Computer graphics; 9. Numerical Linear Algebra: 9.1 Gaussian elimination in practice; 9.2 Norms and condition numbers; 9.3 Iterative methods for linear algebra; 10. Complex Vectors and Matrices: 10.1 Complex numbers; 10.2 Hermitian and unitary matrices; 10.3 The fast Fourier transform; Solutions to selected exercises; Matrix factorizations; Conceptual questions for review; Glossary: a dictionary for linear algebra; Index; Teaching codes.

Gilbert Strang ist Mathematiker des MIT, der im Bereich des wissenschaftlichen Rechnens und anderer Gebiete der angewandten Mathematik forscht. Er ist Autor zahlreicher Lehrbücher und wurde bereits mit zahlreichen Preisen ausgezeichnet: von Neumann Prize, American Assoc. for Computational Mechanics; Henrici Prize (ETH-SIAM, Applied Analysis, Numerical Analysis); Su Buchin Prize (Intl Congress of Appl Math); Haimo Prize (Teaching: Math Assoc of America)