Iterative Solvers: Eigenvalue Problems

Related lectures (28)

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Explains the diagonalization of linear transformations using eigenvectors and eigenvalues to form a diagonal matrix.

Covers iterative methods for solving linear systems and discusses convergence criteria and spectral radius.

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

Covers matrix eigenvalues, eigenvectors, and their linear independence.

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

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

Covers the concept of diagonalizability of matrices and explores eigenvalues and eigenvectors.

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

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

Covers linear algebra operations and applications, including matrix multiplication and eigenvalues.