Jordan decomposition

Related lectures (23)

Page 2 of 3

Covers the diagonalization of matrices using eigenvectors and eigenvalues.

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

Covers the diagonalization of linear transformations in R^3, exploring properties and examples.

Explores linear applications in R² and matrix representation, including basis, operations, and geometric interpretation of transformations.

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

Covers the Singular Value Decomposition theorem and its application in decomposing matrices.

Explores the Cholesky factorization method for symmetric positive definite matrices.

Explores the Jordan normal form and its applications in linear algebra, focusing on diagonalization and cyclic bases.

Covers the reduction of a linear application and finding corresponding reduced forms and bases.

Covers matrix operations, focusing on the product and inverse of matrices.