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@ARTICLE{Berljafa:185482,
      author       = {Berljafa, Mario and Wortmann, Daniel and Di Napoli,
                      Edoardo},
      title        = {{A}n optimized and scalable eigensolver for sequences of
                      eigenvalue problems},
      journal      = {Concurrency and computation},
      volume       = {27},
      number       = {4},
      issn         = {1532-0626},
      address      = {Chichester},
      publisher    = {Wiley},
      reportid     = {FZJ-2014-06909},
      pages        = {905–922},
      year         = {2015},
      abstract     = {In many scientific applications, the solution of nonlinear
                      differential equations are obtained through the setup and
                      solution of a number of successive eigenproblems. These
                      eigenproblems can be regarded as a sequence whenever the
                      solution of one problem fosters the initialization of the
                      next. In addition, in some eigenproblem sequences, there is
                      a connection between the solutions of adjacent
                      eigenproblems. Whenever it is possible to unravel the
                      existence of such a connection, the eigenproblem sequence is
                      said to be correlated. When facing with a sequence of
                      correlated eigenproblems, the current strategy amounts to
                      solving each eigenproblem in isolation. We propose an
                      alternative approach that exploits such correlation through
                      the use of an eigensolver based on subspace iteration and
                      accelerated with Chebyshev polynomials (Chebyshev filtered
                      subspace iteration (ChFSI)). The resulting eigensolver is
                      optimized by minimizing the number of matrix–vector
                      multiplications and parallelized using the Elemental library
                      framework. Numerical results show that ChFSI achieves
                      excellent scalability and is competitive with current dense
                      linear algebra parallel eigensolvers.},
      cin          = {JSC / JARA-HPC},
      ddc          = {004},
      cid          = {I:(DE-Juel1)JSC-20090406 / $I:(DE-82)080012_20140620$},
      pnm          = {511 - Computational Science and Mathematical Methods
                      (POF3-511) / Simulation and Data Laboratory Quantum
                      Materials (SDLQM) (SDLQM)},
      pid          = {G:(DE-HGF)POF3-511 / G:(DE-Juel1)SDLQM},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000350293900010},
      doi          = {10.1002/cpe.3394},
      url          = {https://juser.fz-juelich.de/record/185482},
}