Lectures related to Decomposition: Simple and Quadratic Factors

Explores error analysis and limitations in interpolation on evenly distributed nodes.

Explores eigenvalues and minimal polynomial, emphasizing their importance in linear algebra.

Covers Singular Value Decomposition, focusing on its application in image compression and data representation.

Covers matrix representations of linear applications in R³ and the invariance of rank.

Explains orthogonal families, bases, and projections in vector spaces.

Explores the Cholesky factorization method for symmetric positive definite matrices.

Explores the uniqueness of minimal polynomials and the division algorithm for polynomials.