TWO-LEVEL NYSTROM-SCHUR PRECONDITIONER FOR SPARSE SYMMETRIC POSITIVE DEFINITE MATRICES

Randomized methods are becoming increasingly popular in numerical linear algebra. However, few attempts have been made to use them in developing preconditioners. Our interest lies in solving large-scale sparse symmetric positive definite linear systems of equations, where the system matrix is preordered to doubly bordered block diagonal form (for example, using a nested dissection ordering). We investigate the use of randomized methods to construct high-quality preconditioners. In particular, we propose a new and efficient approach that employs Nystrom's method for computing low rank approximations to develop robust algebraic two-level preconditioners. Construction of the new preconditioners involves iteratively solving a smaller but denser symmetric positive definite Schur complement system with multiple right-hand sides. Numerical experiments on problems coming from a range of application areas demonstrate that this inner system can be solved cheaply using block conjugate gradients and that using a large convergence tolerance to limit the cost does not adversely affect the quality of the resulting Nystrom-Schur two-level preconditioner.

Automated summary

The study explores the use of randomized methods in constructing high-quality preconditioners for large-scale sparse symmetric positive definite linear systems. A new and efficient approach is proposed, utilizing Nystrom's method for low rank approximations to develop robust algebraic two-level preconditioners. Numerical experiments demonstrate that the inner system can be solved cheaply using block conjugate gradients and that a large convergence tolerance does not negatively impact the quality of the resulting Nystrom-Schur two-level preconditioner.