Matrix Computations: Eigenvalues and Eigenvectors

Covers the process of diagonalizing matrices, focusing on symmetric matrices and the spectral theorem.

Explores characteristic polynomials, similarity of matrices, and eigenvalues in linear transformations.

Explores the diagonalization of symmetric matrices through orthogonal decomposition and the spectral theorem.

Delves into the complexity of matrix computations, focusing on eigenvalues and eigenvectors, algorithms, errors, and numerical stability.

Covers the decomposition of a matrix into its eigenvalues and eigenvectors, the orthogonality of eigenvectors, and the normalization of vectors.