Parallel Iterative Refinement in Polynomial Eigenvalue Problems∗

Methods for the polynomial eigenvalue problem often require to be followed by an iterative refinement process to improve the accuracy of the computed solutions. This can be accom-plished by means of a Newton iteration tailored to matrix polynomials. The computational cost of this step is usually higher than the cost of computing the initial approximations, due to the need of solving multiple linear systems of equations with a modified system matrix. An effective parallelization is thus important, and we propose different approaches for the message-passing scenario. Some schemes use a subcommunicator strategy in order to improve the scalability whenever direct linear solvers are used. We show performance results for the various alternatives implemented in the context of SLEPc, the Scalable Library for Eigenvalue Problem Computations.