Lectures related to Richardson Convergence

Explores characteristic polynomials, similarity of matrices, and eigenvalues in linear transformations.

Explores the link between linear systems and optimization through elimination and LU decomposition.

Covers non-negative definite matrices, covariance matrices, and Principal Component Analysis for optimal dimension reduction.

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

Explores the Jacobi method and diagonalization techniques, including similarity transformation, power methods, and QR decomposition.

Explores matrix inversion, conditions for invertibility, uniqueness of the inverse, and elementary matrices for inversion.