Lectures related to Diagonalization of Matrices

Covers the concept of singular values in linear algebra and their properties, including diagonalization and practical examples.

Explores the diagonalization of linear maps by finding a basis formed by eigenvectors.

Covers matrix eigenvalues, eigenvectors, and their linear independence.

Covers the method of diagonalization for determining if a non-square matrix A is diagonalizable.

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

Explores eigenvalue problems, iterative methods, convergence properties, and applications in computational physics.

Provides an overview of symmetric matrices, quadratic forms, and their applications in linear algebra and analysis.

Introduces Convex Optimization notation, convex functions, vector norms, and matrix properties.