Eigenvalues and Eigenvectors

Related lectures (29)

Page 2 of 3

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

Introduces eigenvalues, eigenvectors, and similar matrices, emphasizing diagonalization and geometric interpretations.

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

Covers the decomposition of linear operators and properties of eigenspaces.

Explains the diagonalization of matrices, criteria, and significance of distinct eigenvalues.

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

Explores the diagonalization of linear maps by finding a basis formed by eigenvectors.

Explores algebraic and geometric multiplicities of eigenvalues in linear algebra.

Explores the canonical basis in linear algebra, focusing on matrix representation, diagonalizability, and characteristic polynomials.

Covers eigenvalues, eigenvectors, and matrix similarity for dynamic systems.