Concept

Eigenvalue algorithm

Related lectures (32)

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

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

Covers quantum eigenfunctions and the importance of A and B commuting for the same set of eigenfunctions.

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

Explores the diagonalization of symmetric matrices using eigenvectors and eigenvalues, emphasizing orthogonality and real eigenvalues.

Explores the complexity of matrix computations, focusing on eigenvalues and eigenvectors of symmetric matrices and the challenges in computing them.

Covers eigenvalues, eigenvectors, and the Fibonacci sequence, exploring their mathematical properties and practical applications.

Covers the decomposition of a matrix into its eigenvalues and eigenvectors, the orthogonality of eigenvectors, and the normalization of vectors.

Explores the diagonalization of matrices through eigenvalues and eigenvectors, emphasizing the importance of bases and subspaces.

Explains the postulates of Quantum Mechanics, focusing on self-adjoint operators and mathematical notation.

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

Covers tips on matrix construction and function manipulation using MATLAB.

Discusses the sharp transition in mixing time of a random walk.

Explores small signal stability in power systems and the impact of excitation control.

Explores eigenvalues and modes in multivariable control systems, focusing on their analysis and behavior.

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

Delves into iterative solvers for eigenvalue problems, addressing user criteria, convergence metrics, and method choices.

Covers matrix eigenvalues, eigenvectors, and their linear independence.

Explores the spectral gap in Markov chains and its impact on convergence speed.

Explores stability analysis in linear systems, emphasizing eigenvalues, eigenvectors, and stable manifolds.