Irina-Beatrice Haas will talk about; 'Sharp error bounds for approximate eigenvalues and singular values from subspace methods'
Subspace methods are commonly used for finding approximate eigenvalues and singular values of large-scale matrices. Once a subspace is found, the Rayleigh-Ritz method (for symmetric eigenvalue problems) and Petrov-Galerkin projection (for singular values) are the de facto method for extraction of eigenvalues and singular values. In this work we derive error bounds for approximate eigenvalues obtained via the Rayleigh-Ritz process. Our bounds are quadratic in the residual corresponding to each Ritz value while also being robust to clustered Ritz values, which is a key improvement over existing results. We apply these bounds to several methods for computing eigenvalues and singular values, including Krylov methods and randomized algorithms.