Vectors and Tensors: Mathematical Preliminaries and Spectral Decomposition

Related lectures (29)

Page 1 of 3

Covers the process of diagonalizing matrices, focusing on symmetric matrices and the spectral theorem.

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

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

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

Covers the decomposition of symmetric matrices into eigenvalues and eigenvectors.

Covers the diagonalization of symmetric matrices and the spectral theorem.

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

Covers examples of symmetric matrices and their properties, including eigenvectors and eigenvalues.

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

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