Lectures related to Matrix Calculus: Matrices and Determinants

Covers the LU decomposition algorithm, transforming a matrix into L and U.

Explores matrix decompositions for solving linear systems and simulating dynamics.

Covers essential linear algebra concepts for convex optimization, including vector norms, eigenvalue decomposition, and matrix properties.

Covers the Singular Value Decomposition (SVD) of a matrix and its applications.

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

Covers the Jacobi method, Givens rotation, QR decomposition, and diagonalization techniques in computational physics.

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