Characteristic Polynomial: Eigenvalues and VAPs

Related lectures (28)

Page 2 of 3

Covers eigenvalues and eigenvectors, explaining their importance in linear algebra.

Covers the theory and examples of diagonalizing matrices, focusing on eigenvalues, eigenvectors, and linear independence.

Showcases a non-diagonalizable matrix example and explores eigenvalues and eigenvectors.

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

Covers the reduction of a linear transformation in a 2-dimensional space to find a simpler matrix representation.

Provides an overview of symmetric matrices, quadratic forms, and their applications in linear algebra and analysis.

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

Explains how the inverse of a matrix shares its eigenvalue and how the transposed version has the same set of eigenvalues.

Explores the diagonalization of symmetric matrices using eigenvectors and eigenvalues, emphasizing orthogonality and real eigenvalues.

Explores spectral decomposition of symmetric matrices and Singular Value Decomposition (SVD) for matrix decomposition.