Lectures related to Power Method and Applications

Explores eigenvalues, eigenvectors, and methods for solving linear systems with a focus on rounding errors and preconditioning matrices.

Covers stress components, tensor transformation, and invariants in continuum mechanics.

Discusses tensor transformations and diagonalization of symmetric tensors, focusing on stress analysis and the significance of principal stresses.

Covers the theory and examples of diagonalizing matrices, focusing on eigenvalues, eigenvectors, and linear independence.

Covers tensor decomposition and Jennrich's theorem, focusing on tensor rank and the uniqueness of tensor decomposition.

Covers the calculation of eigenvalues and eigenvectors, emphasizing their significance and applications.

Covers tensor algebras, symmetric and exterior algebras, and tensor products of modules.

Explains the diagonalization of matrices, criteria, and significance of distinct eigenvalues.

Covers the theory of scalar fields, stability of fixed points, eigenvalues, Lagrangian interactions, tensors, and beta functions.

Explores eigenvalues and eigenvectors in matrices through examples and calculations.

Explores eigenvalues and eigenvectors in 3D linear algebra, covering characteristic polynomials, stability under transformations, and real roots.

Explores characteristic polynomials, similarity of matrices, and eigenvalues in linear transformations.

Explores the effect of rounding errors in solving linear systems using the LU factorization method.

Explores chemical environment representations, symmetrized correlations, and machine learning applications at the atomic scale.

Explores the transformation of vectors and tensors, including rotations and representations in quantum physics.

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

Covers the transformation of vectors and tensors in quantum physics.

Explores the transformation of vectors and tensors in quantum physics, emphasizing Lie groups and algebras.

Covers the concept of diagonalization of matrices through the study of eigenvectors and eigenvalues.

Explores canonical basis, linear mapping, and eigenvalues in linear algebra.