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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.

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Assessing the Performance of Mixed-Precision ILU(0)-Preconditioned Multiple-Precision Real and Complex Krylov Subspace Methods

  • 会议论文

  • 发表日期：

    2026年

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  • Tomonori Kouya

摘要

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.