Assessing the Performance of Mixed-Precision ILU(0)-Preconditioned Multiple-Precision Real and Complex Krylov Subspace Methods
arXiv:2504.14498 · doi:10.1007/978-3-031-97606-3_3
Abstract
Krylov subspace methods are linear solvers based on matrix-vector multiplications and vector operations. While easily parallelizable, they are sensitive to rounding errors and may experience convergence issues. ILU(0), an incomplete LU factorization with zero fill-in, is a well-known preconditioning technique that enhances convergence for sparse matrices. In this paper, we implement a double-precision and multiple-precision ILU(0) preconditioner, compatible with product-type Krylov subspace methods, and evaluate its performance.