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ITERATIVE ILU PRECONDITIONERS FOR LINEAR SYSTEMS AND EIGENPROBLEMS

Daniele Boffi1, Zhongjie Lu2, Luca F. Pavarino3   

  1. 1 Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia Dipartimento di Matematica "F. Casorati" University of Pavia Via Ferrata 5, 27100 Pavia, Italy;
    2 Dipartimento di Matematica, Università di Pavia, via Ferrata 5, 27100 Pavia, Italy School of Mathematical Sciences, University of Science and Technology of China, Hefei 230026, China;
    3 Dipartimento di Matematica, Università di Pavia, via Ferrata 5, 27100 Pavia, Italy
  • Received:2020-05-27 Revised:2020-08-17 Published:2021-08-06
  • Contact: Zhongjie Lu,Email:zhjlu@ustc.edu.cn
  • Supported by:
    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.

Daniele Boffi, Zhongjie Lu, Luca F. Pavarino. ITERATIVE ILU PRECONDITIONERS FOR LINEAR SYSTEMS AND EIGENPROBLEMS[J]. Journal of Computational Mathematics, 2021, 39(4): 633-654.

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.

CLC Number: 

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