Student Posters Repository

We consider iterative techniques for the solution of large, sparse interior eigenvalue problems. Several spectral slicing approaches, including polynomial filtering and contour integration approaches have been incorporated into BEAST, a parallel, distributed memory eigensolver framework. This framework builds an approximate subspace for the desired eigenpairs, then uses a Rayleigh-Ritz approach to reduce the eigenproblem to a size which may be solved directly. The iterative nature of the framework may be utilized for various adaptive approaches. For example, the precision or the method used to construct the subspace may be changed between iterations for improvement of performance, robustness, or accuracy. Several such strategies with initial results are shown.