Matrix Decomposition: Triangular and Spectral

Related lectures (25)

Page 2 of 3

Covers the basics of linear algebra, including matrix operations and singular value decomposition.

Explains the construction of U, verification of results, and interpretation of SVD in matrix decomposition.

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

Covers the Singular Value Decomposition theorem and its applications in practice.

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

Explores the diagonalization of symmetric matrices and the importance of Singular Value Decomposition.

Explores the spectral decomposition of symmetric matrices, including diagonalization and orthogonal basis change matrices.

Covers the process of finding orthogonal bases and spectral decomposition of symmetric matrices.

Covers the diagonalization of symmetric matrices, the spectral theorem, and the use of spectral decomposition.

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