A Robust Iterative Scheme for Symmetric Indefinite Systems
For computational scientists solving large sparse symmetric indefinite systems, this work offers a robust iterative solver that leverages existing preconditioners, though it is an incremental improvement over known nested iteration techniques.
The authors propose a two-level nested preconditioned iterative scheme for solving symmetric indefinite linear systems with few negative eigenvalues, combining outer MINRES and inner CG iterations. The method is demonstrated on quadratic eigenvalue problems from disk brake models and other applications, showing robustness.
We propose a two-level nested preconditioned iterative scheme for solving sparse linear systems of equations in which the coefficient matrix is symmetric and indefinite with relatively small number of negative eigenvalues. The proposed scheme consists of an outer Minimum Residual (MINRES) iteration, preconditioned by an inner Conjugate Gradient (CG) iteration in which CG can be further preconditioned. The robustness of the proposed scheme is illustrated by solving indefinite linear systems that arise in the solution of quadratic eigenvalue problems in the context of model reduction methods for finite element models of disk brakes as well as on other problems that arise in a variety of applications.