A flexible and adaptive Simpler GMRES with deflated restarting for shifted linear systems
Provides faster solvers for shifted linear systems, which are common in scientific computing, but the improvement is incremental.
Proposed two efficient iterative algorithms for solving shifted linear systems, using deflated restarting and flexible preconditioning to reduce matrix-vector products and CPU time, validated by numerical experiments.
In this paper, two efficient iterative algorithms based on the simpler GMRES method are proposed for solving shifted linear systems. To make full use of the shifted structure, the proposed algorithms utilizing the deflated restarting strategy and flexible preconditioning can significantly reduce the number of matrix-vector products and the elapsed CPU time. Numerical experiments are reported to illustrate the performance and effectiveness of the proposed algorithms.