Lectures related to Hessenberg matrix

Explores orthogonal projections and the Gram-Schmidt method for constructing bases.

Explores reducing a matrix with a double eigenvalue and its diagonalizability.

Explores the reduction of a matrix based on its eigenvalues, focusing on the case of two real eigenvalues.

Explores the complexity of matrix computations, focusing on eigenvalues and eigenvectors of symmetric matrices and the challenges in computing them.

Explores the complexity of matrix computations, focusing on solving least squares problems and the impact of noise on numerical stability.

Covers the QR factorization method applied to solving a system of linear equations in the least squares sense.