Lectures related to Eigenvalue Double Case Reduction

Covers the reduction of a linear application and finding corresponding reduced forms and bases.

Covers the criteria for diagonalizing a matrix and provides illustrative examples.

Explores eigenvalues, eigenvectors, diagonalization, and spectral theorem in linear algebra.

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

Explores the reduction of a matrix based on its eigenvalues, focusing on the case of two real eigenvalues.

Covers the Singular Value Decomposition (SVD) in detail, including properties of matrices and system linearity.

Explores the properties and examples of diagonalizable matrices, emphasizing the relationship between eigenvectors and eigenvalues.

Explores the application of matrices and eigendecompositions in networks.