Lectures related to Inverse iteration

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

Explores diagonalizable matrices, eigenvectors, and power iteration methods for dominant eigenvectors.

Explores the inverse power method for ODEs and the significance of Lipschitz continuity.

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

Explores the application of matrices and eigendecompositions in networks.

Covers the HITS algorithm, computing hubs and authorities, convergence, and sample results.

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

Explores the Jacobi method for linear systems, emphasizing convergence conditions and matrix properties.

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

Explores consensus algorithms and connectivity properties in networked control systems.

Covers the Power Method for approximating the largest eigenvalue of a matrix.

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

Covers the concept of convex functions and their applications in optimization problems.

Explores eigenvalue problems, iterative methods, and their applications in quantum physics and network analysis.

Explores primitive matrices, dominant eigenvalues, and spectral properties in networked control systems.

Covers the analysis of local extrema and Hessians, focusing on the development of limit notations.

Covers the analysis of linear systems, focusing on methods such as Jacobi and Richardson for solving linear equations.