BILUTM: A Domain-Based Multilevel Block ILUT Preconditioner for General Sparse Matrices

نویسندگان

  • Yousef Saad
  • Jun Zhang
چکیده

This paper describes a domain-based multilevel block ILU preconditioner (BILUTM) for solving general sparse linear systems. This preconditioner combines a high accuracy incomplete LU factorization with an algebraic multilevel recursive reduction. Thus, in the first level the matrix is permuted into a block form using (block) independent set ordering and an ILUT factorization for the reordered matrix is performed. The reduced system is the approximate Schur complement associated with the partitioning, and it is obtained implicitly as a by-product of the partial ILUT factorization with respect to the complement of the independent set. The incomplete factorization process is repeated with the reduced systems recursively. The last reduced system is factored approximately using ILUT again. The successive reduced systems are not stored. This implementation is efficient in controlling the fill-in elements during the multilevel block ILU factorization, especially when large size blocks are used in domain decomposition-type implementations. Numerical experiments are used to show the robustness and efficiency of the proposed technique for solving some difficult problems.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

A grid-based multilevel incomplete LU factorization preconditioning technique for general sparse matrices

We design a grid based multilevel incomplete LU preconditioner (GILUM) for solving general sparse matrices. This preconditioner combines a high accuracy ILU factorization with an algebraic multilevel recursive reduction. The GILUM precondi-tioner is a compliment to the domain based multilevel block ILUT preconditioner. A major diierence between these two preconditioners is the way that the coar...

متن کامل

A fully parallel block independent set algorithm for distributed sparse matrices

We present a fully parallel algorithm for constructing block independent set for general sparse matrices in a distributed environment. The block independent set is used in the construction of parallel multilevel preconditioners in solving large sparse matrices on distributed memory parallel computers. We compare a few implementations of the parallel multilevel ILU preconditioners with diierent ...

متن کامل

Preconditioners based on Strong Components

This paper proposes an approach for obtaining block diagonal and block triangular preconditioners that can be used for solving a linear system Ax = b, where A is a large, nonsingular, real, n×n sparse matrix. The proposed approach uses Tarjan’s algorithm for hierarchically decomposing a digraph into its strong subgraphs [22, 23]. To the best of our knowledge, this is the first work that uses th...

متن کامل

BILUM: Block Versions of Multielimination and Multilevel ILU Preconditioner for General Sparse Linear Systems

We introduce block versions of the multielimination incomplete LU (ILUM) factorization preconditioning technique for solving general sparse unstructured linear systems. These preconditioners have a multilevel structure and, for certain types of problems, may exhibit properties that are typically enjoyed by multigrid methods. Several heuristic strategies for forming blocks of independent sets ar...

متن کامل

Efficient Preconditioning Strategies for the Multilevel Fast Multipole Algorithm

For the iterative solutions of the integral equation methods employing the multilevel fast multipole algorithm (MLFMA), effective preconditioning techniques should be developed for robustness and efficiency. Preconditioning techniques for such problems can be broadly classified as fixed preconditioners that are generated from the sparse near-field matrix and variable ones that can make use of M...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:
  • SIAM J. Matrix Analysis Applications

دوره 21  شماره 

صفحات  -

تاریخ انتشار 1999