Experiments on a parallel nonlinear Jacobi-Davidson algorithm

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The Jacobi-Davidson (JD) algorithm is very well suited for the computation of a few eigen-pairs of large sparse complex symmetric nonlinear eigenvalue problems. The performance of JD crucially depends on the treatment of the so-called correction equation, in particular the preconditioner, and the initial vector. Depending on the choice of the spectral shift and the accuracy of the solution, the convergence of JD can vary from linear to cubic. We investigate parallel preconditioners for the Krylov space method used to solve the correction equation. We apply our nonlinear Jacobi-Davidson (NLJD) method to quadratic eigenvalue problems that originate from the time-harmonic Maxwell equation for the modeling and simulation of resonating electromagnetic structures. © The Authors. Published by Elsevier B.V.




Matsuo, Y., Guo, H., & Arbenz, P. (2014). Experiments on a parallel nonlinear Jacobi-Davidson algorithm. In Procedia Computer Science (Vol. 29, pp. 565–575). Elsevier. https://doi.org/10.1016/j.procs.2014.05.051

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