Optimal gradient minimization scheme for finite element eigenproblems
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Solving the Bethe-Salpeter equation on massively parallel architectures
2021, Computer Physics CommunicationsCitation Excerpt :The advantage of subspace methods is that the search space for the eigenvectors can be treated as one contiguous block and further refined at each iteration of the eigensolver. This is the main philosophy behind the version of the Conjugate Gradient (CG) method [18,19] modified by Kalkreuter and Simma (KSCG) in their work [41]. Originally conceived for applications in Quantum Chromodynamics, the KSCG eigensolver parallel implementation is based on geometrical data decomposition where vectors are equally partitioned and stored on distinct processing nodes.
Computing the lowest eigenvalues of the Fermion matrix by subspace iterations
1997, Nuclear Physics B - Proceedings SupplementsGradient eigenanalysis on nested finite elements
1996, Advances in Engineering SoftwareNumerical analysis of the spectrum of the Dirac operator in four-dimensional SU(2) gauge fields
1996, Nuclear Physics B - Proceedings SupplementsAn accelerated conjugate gradient algorithm to compute low-lying eigenvalues - A study for the Dirac operator in SU(2) lattice QCD
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1995, Nuclear Physics B (Proceedings Supplements)
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