This is the peer reviewed version of the following article: Campos, C., and Roman, J. E. (2016) Parallel iterative refinement in polynomial eigenvalue problems. Numer. Linear Algebra Appl., 23: 730–745, which has been published in final form at http://dx.doi.org/10.1002/nla.2052. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-ArchivingMethods for the polynomial eigenvalue problem sometimes need to be followed by an iterative refinement process to improve the accuracy of the computed solutions. This can be accomplished 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 bordered coefficient 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.This work was partially supported by the Spanish Ministry of Economy and Competitiveness under grant TIN2013-41049-P. Carmen Campos was supported by the Spanish Ministry of Education, Culture and Sport through an FPU grant with reference AP2012-0608. The computational experiments of Section 5 were carried out on the supercomputer Tirant at Universitat de Valencia.Campos, C.; Román Moltó, JE. (2016). Parallel iterative refinement in polynomial eigenvalue problems. Numerical Linear Algebra with Applications. 23(4):730-745. https://doi.org/10.1002/nla.2052S73074523
