Lectures related to Eigenvalues Reduction: Two Real Eigenvalues

Explores the diagonalization of symmetric matrices through orthogonal decomposition and the spectral theorem.

Explores the canonical basis in linear algebra, focusing on matrix representation, diagonalizability, and characteristic polynomials.

Explores basis transformation, eigenvalues, and linear operators in inner product spaces, emphasizing their significance in Quantum Mechanics.

Delves into the complexity of matrix computations, focusing on eigenvalues and eigenvectors, algorithms, errors, and numerical stability.

Explores eigenvalues, matrix trace, and similarity, highlighting their significance in matrix properties.

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

Explores eigenvalues and eigenvectors, demonstrating their importance in linear algebra and their application in solving systems of equations.