GMRES: a generalized minimal residual algorithm for solving nonsymmetric linear systems
SIAM Journal on Scientific and Statistical Computing
Implicit application of polynomial filters in a k-step Arnoldi method
SIAM Journal on Matrix Analysis and Applications
SIAM Journal on Numerical Analysis
Analysis and implementation of an implicitly restarted Arnoldi iteration
Analysis and implementation of an implicitly restarted Arnoldi iteration
Implicitly restarted Arnoldi with purification for the shift-invert transformation
Mathematics of Computation
Matrix computations (3rd ed.)
Efficient management of parallelism in object-oriented numerical software libraries
Modern software tools for scientific computing
Implicitly Restarted Arnoldi Methods and Subspace Iteration
SIAM Journal on Matrix Analysis and Applications
Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
Study of a non-overlapping domain decomposition method: poisson and stokes problems
Applied Numerical Mathematics
Multiple Explicitly Restarted Arnoldi Method for Solving Large Eigenproblems
SIAM Journal on Scientific Computing
On a parallel multilevel preconditioned Maxwell eigensolver
Parallel Computing - Parallel matrix algorithms and applications (PMAA'04)
Anasazi software for the numerical solution of large-scale eigenvalue problems
ACM Transactions on Mathematical Software (TOMS)
Advances in Engineering Software
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This work presents a parallel implementation of the implicitly restarted Arnoldi/Lanczos method for the solution of eigenproblems approximated by the finite element method. The implicitly restarted Arnoldi/Lanczos uses a restart scheme in order to improve the convergence of the desired portion of the spectrum, addressing issues such as memory requirements and computational costs related to the generation and storage of the Krylov basis. The presented implementation is suitable for distributed memory architectures, especially PC clusters. In the parallel solution, a subdomain by subdomain approach was implemented and overlapping and non-overlapping mesh partitions were tested. Compressed data structures in the formats CSRC and CSRC/CSR were used to store the coefficient matrices. The parallelization of numerical linear algebra operations present in both Krylov and implicitly restarted methods are discussed. Numerical examples are shown, in order to point out the efficiency and applicability of the proposed method.