Iterative ILU factorizations are constructed,analyzed and applied as preconditioners to solve both linear systems and eigenproblems.The computational kernels of these novel Iterative ILU factorizations are sparse matr...Iterative ILU factorizations are constructed,analyzed and applied as preconditioners to solve both linear systems and eigenproblems.The computational kernels of these novel Iterative ILU factorizations are sparse matrix-matrix multiplications,which are easy and efficient to implement on both serial and parallel computer architectures and can take full advantage of existing matrix-matrix multiplication codes.We also introduce level-based and threshold-based algorithms in order to enhance the accuracy of the proposed Iterative ILU factorizations.The results of several numerical experiments illustrate the efficiency of the proposed preconditioners to solve both linear systems and eigenvalue problems.展开更多
基金The authors are members of the INdAM Research group GNCS and their research is partially supported by IMATI/CNR,by PRIN/MIUR and the Dipartimenti di Eccellenza Program 2018-22-Dept,of Mathematics,University of Pavia.
文摘Iterative ILU factorizations are constructed,analyzed and applied as preconditioners to solve both linear systems and eigenproblems.The computational kernels of these novel Iterative ILU factorizations are sparse matrix-matrix multiplications,which are easy and efficient to implement on both serial and parallel computer architectures and can take full advantage of existing matrix-matrix multiplication codes.We also introduce level-based and threshold-based algorithms in order to enhance the accuracy of the proposed Iterative ILU factorizations.The results of several numerical experiments illustrate the efficiency of the proposed preconditioners to solve both linear systems and eigenvalue problems.
