Lectures related to Matrix Decompositions: LU, Cholesky, QR, Eigendecomposition

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

Explores matrix equivalence theorems for systems of equations and least squares solutions.

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

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

Covers the decomposition of matrices into triangular blocks and spectral decomposition.