Lectures related to Eigenvalues and Symmetric Matrices

Covers the concept of balanced realization in the SISO case, focusing on system observability and controllability.

Explores symmetric matrices, their diagonalization, and properties like eigenvalues and eigenvectors.

Covers the calculation of eigenvalues and eigenvectors, emphasizing their significance and applications.

Explores the Jordan normal form and its applications in linear algebra, focusing on diagonalization and cyclic bases.

Covers linear systems, diagonal and triangular matrices, and LU factorization.

Explores the diagonalization of symmetric matrices and the orthogonality of eigenvectors.

Covers the diagonalization of symmetric matrices and the spectral theorem.

Covers the concept of symmetric matrices, orthogonal bases, and eigenvectors.