000868382 001__ 868382 000868382 005__ 20221109161717.0 000868382 0247_ $$2Handle$$a2128/23783 000868382 037__ $$aFZJ-2019-06911 000868382 041__ $$aEnglish 000868382 1001_ $$0P:(DE-Juel1)144723$$aDi Napoli, Edoardo$$b0$$eCorresponding author$$ufzj 000868382 1112_ $$aSPPEXA Final Symposium 2019$$cDresden$$d2019-10-21 - 2019-10-23$$gSPPEXA$$wGermany 000868382 245__ $$aOptimizing rational filters for interior eigenvalue solvers 000868382 260__ $$c2019 000868382 3367_ $$033$$2EndNote$$aConference Paper 000868382 3367_ $$2DataCite$$aOther 000868382 3367_ $$2BibTeX$$aINPROCEEDINGS 000868382 3367_ $$2DRIVER$$aconferenceObject 000868382 3367_ $$2ORCID$$aLECTURE_SPEECH 000868382 3367_ $$0PUB:(DE-HGF)6$$2PUB:(DE-HGF)$$aConference Presentation$$bconf$$mconf$$s1578313781_24844$$xInvited 000868382 520__ $$aRational filter functions can be used to improve the convergence of the so-called contour-based eigensolvers, a popular family of algorithms for the solution of the interior eigenvalue problem. This talk provide an overview on how such filters can be optimized for performance and convergence based on a non-convex weighted Least-Square scheme.The net result is an almost parameter-free minimization framework with an enhanced usability and productivity which leads to rational functions outperforming state-of-art filters. 000868382 536__ $$0G:(DE-HGF)POF3-511$$a511 - Computational Science and Mathematical Methods (POF3-511)$$cPOF3-511$$fPOF III$$x0 000868382 536__ $$0G:(DE-Juel1)SDLQM$$aSimulation and Data Laboratory Quantum Materials (SDLQM) (SDLQM)$$cSDLQM$$fSimulation and Data Laboratory Quantum Materials (SDLQM)$$x2 000868382 7001_ $$0P:(DE-Juel1)167415$$aWinkelmann, Jan$$b1$$ufzj 000868382 7001_ $$0P:(DE-HGF)0$$aKollnig, Konrad$$b2 000868382 8564_ $$uhttps://juser.fz-juelich.de/record/868382/files/ESSEX-workshop-Di_Napoli.pdf$$yOpenAccess 000868382 8564_ $$uhttps://juser.fz-juelich.de/record/868382/files/ESSEX-workshop-Di_Napoli.pdf?subformat=pdfa$$xpdfa$$yOpenAccess 000868382 909CO $$ooai:juser.fz-juelich.de:868382$$pdriver$$pVDB$$popen_access$$popenaire 000868382 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144723$$aForschungszentrum Jülich$$b0$$kFZJ 000868382 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)167415$$aForschungszentrum Jülich$$b1$$kFZJ 000868382 9131_ $$0G:(DE-HGF)POF3-511$$1G:(DE-HGF)POF3-510$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lSupercomputing & Big Data$$vComputational Science and Mathematical Methods$$x0 000868382 9141_ $$y2019 000868382 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess 000868382 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0 000868382 9801_ $$aFullTexts 000868382 980__ $$aconf 000868382 980__ $$aVDB 000868382 980__ $$aUNRESTRICTED 000868382 980__ $$aI:(DE-Juel1)JSC-20090406