Results 1  10
of
50
Preconditioning techniques for large linear systems: A survey
 J. COMPUT. PHYS
, 2002
"... This article surveys preconditioning techniques for the iterative solution of large linear systems, with a focus on algebraic methods suitable for general sparse matrices. Covered topics include progress in incomplete factorization methods, sparse approximate inverses, reorderings, parallelization i ..."
Abstract

Cited by 189 (5 self)
 Add to MetaCart
This article surveys preconditioning techniques for the iterative solution of large linear systems, with a focus on algebraic methods suitable for general sparse matrices. Covered topics include progress in incomplete factorization methods, sparse approximate inverses, reorderings, parallelization issues, and block and multilevel extensions. Some of the challenges ahead are also discussed. An extensive bibliography completes the paper.
pARMS: A parallel version of the algebraic recursive multilevel solver
 Numer. Linear Algebra Appl
"... ..."
Multilevel preconditioners constructed from inversebased ILUs
, 2004
"... This paper analyzes dropping strategies in a multilevel incomplete LU decomposition context and presents a few of strategies for obtaining related ILUs with enhanced robustness. The analysis shows that the Incomplete LU factorization resulting from dropping small entries in Gaussian elimination prod ..."
Abstract

Cited by 32 (9 self)
 Add to MetaCart
(Show Context)
This paper analyzes dropping strategies in a multilevel incomplete LU decomposition context and presents a few of strategies for obtaining related ILUs with enhanced robustness. The analysis shows that the Incomplete LU factorization resulting from dropping small entries in Gaussian elimination produces a good preconditioner when the inverses of these factors have norms that are not too large. As a consequence a few strategies are developed whose goal is to achieve this feature. A number of “templates” for enabling implementations of these factorizations are presented. Numerical experiments show that the resulting ILUs offer a good compromise between robustness and efficiency.
Preconditioning Helmholtz linear systems
, 2009
"... Linear systems which originate from the simulation of wave propagation phenomena can be very difficult to solve by iterative methods. These systems are typically complex valued and they tend to be highly indefinite, which renders the standard ILUbased preconditioners ineffective. This paper present ..."
Abstract

Cited by 22 (1 self)
 Add to MetaCart
Linear systems which originate from the simulation of wave propagation phenomena can be very difficult to solve by iterative methods. These systems are typically complex valued and they tend to be highly indefinite, which renders the standard ILUbased preconditioners ineffective. This paper presents a study of ways to enhance standard preconditioners by altering the diagonal by imaginary shifts. Prior work indicates that modifying the diagonal entries during the incomplete factorization process, by adding to it purely imaginary values can improve the quality of the preconditioner in a substantial way. Here we propose simple algebraic heuristics to perform the shifting and test these techniques with the ARMS and ILUT preconditioners. Comparisons are made with applications stemming from the diffraction of an acoustic wave incident on a bounded obstacle (governed by the Helmholtz Wave Equation).
Multilevel ILU with reorderings for diagonal dominance
 SIAM J. Sci. Comput
, 2005
"... This paper presents a preconditioning method based on combining twosided permutations with a multilevel approach. The nonsymmetric permutation exploits a greedy strategy to put large entries of the matrix in the diagonal of the upper leading submatrix. The method can be regarded as a complete pivot ..."
Abstract

Cited by 17 (7 self)
 Add to MetaCart
(Show Context)
This paper presents a preconditioning method based on combining twosided permutations with a multilevel approach. The nonsymmetric permutation exploits a greedy strategy to put large entries of the matrix in the diagonal of the upper leading submatrix. The method can be regarded as a complete pivoting version of the incomplete LU factorization. This leads to an effective incomplete factorization preconditioner for general nonsymmetric, irregularly structured, sparse linear systems.
Robust parameterfree algebraic multilevel preconditioning
"... To precondition large sparse linear systems resulting from the discretization of secondorder elliptic partial di erential equations, many recent works focus on the socalled algebraic multilevel methods. These are based on a block incomplete factorization process applied to the system matrix partit ..."
Abstract

Cited by 13 (0 self)
 Add to MetaCart
To precondition large sparse linear systems resulting from the discretization of secondorder elliptic partial di erential equations, many recent works focus on the socalled algebraic multilevel methods. These are based on a block incomplete factorization process applied to the system matrix partitioned in hierarchical form. They have been shown to be both robust and e cient in several circumstances, leading to iterative solution schemes of optimal order of computational complexity. Now, despite the procedure is essentially algebraic, previous works focus generally on a speci c context and consider schemes that use classical grid hierarchies with characteristic mesh sizes h; 2h; 4h, etc. Therefore, these methods require some extra information besides the matrix of the linear system and lack of robustness in some situations where semicoarsening would be desirable. In this paper, we develop a general method that can be applied in a black box fashion to a wide class of problems, ranging from 2D model Poisson problems to 3D singularly perturbed convection–di usion equations. It is based on an automatic coarsening process similar to the one used in the AMG method, and on coarse grid matrices computed according to a simple and cheap aggregation principle. Numerical experiments illustrate the e ciency and the robustness of the proposed approach. Copyright? 2002 John Wiley & Sons, Ltd. KEY WORDS: iterative methods; convergence; preconditioning
On Preconditioning Schur Complement And Schur Complement Preconditioning
, 2000
"... . We study two implementation strategies to utilize Schur complement technique in multilevel recursive incomplete LU preconditioning techniques (RILUM) for solving general sparse matrices. The first strategy constructs a RILUM to precondition the original matrix. The second strategy solves the first ..."
Abstract

Cited by 10 (3 self)
 Add to MetaCart
(Show Context)
. We study two implementation strategies to utilize Schur complement technique in multilevel recursive incomplete LU preconditioning techniques (RILUM) for solving general sparse matrices. The first strategy constructs a RILUM to precondition the original matrix. The second strategy solves the first Schur complement matrix using the lower level parts of the RILUM as the preconditioner. We discuss computational and memory costs of both strategies and the potential effect on grid independent convergence rate of RILUM with different implementation strategies. Key words. sparse matrices, Schur complement, RILUM, preconditioning techniques. AMS subject classifications. 65F10, 65N06. 1. Introduction. In this paper, we discuss the issue of implementing a class of multilevel recursive incomplete LU (RILUM) preconditioners. These preconditioners were first reported and implemented in [40]. RILUM is a general framework for constructing robust multilevel preconditioning techniques based on blo...
A greedy strategy for coarsegrid selection
, 2006
"... Efficient solution of the very large linear systems that arise in numerical modelling of realworld applications is often only possible through the use of multilevel techniques. While highly optimized algorithms may be developed using knowledge about the origins of the matrix problem to be considere ..."
Abstract

Cited by 10 (4 self)
 Add to MetaCart
(Show Context)
Efficient solution of the very large linear systems that arise in numerical modelling of realworld applications is often only possible through the use of multilevel techniques. While highly optimized algorithms may be developed using knowledge about the origins of the matrix problem to be considered, much recent interest has been in the development of purely algebraic approaches that may be applied in many situations, without problemspecific tuning. Here, we consider an algebraic approach to finding the fine/coarse partitions needed in multilevel approaches. The algorithm is motivated by recent theoretical analysis of the performance of two common multilevel algorithms, multilevel block factorization and algebraic multigrid. While no guarantee on the rate of coarsening is given, the splitting is shown to always yield an effective preconditioner in the twolevel sense. Numerical performance of twolevel and multilevel variants of this approach is demonstrated in combination with both algebraic multigrid and multilevel block factorizations, and the advantages of each of these two algorithmic settings are explored. 1
AGGREGATIONBASED ALGEBRAIC MULTILEVEL Preconditioning
, 2006
"... We propose a preconditioning technique that is applicable in a “black box” fashion to linear systems arising from second order scalar elliptic PDEs discretized by finite differences or finite elements with nodal basis functions. This technique is based on an algebraic multilevel scheme with coarse ..."
Abstract

Cited by 7 (1 self)
 Add to MetaCart
We propose a preconditioning technique that is applicable in a “black box” fashion to linear systems arising from second order scalar elliptic PDEs discretized by finite differences or finite elements with nodal basis functions. This technique is based on an algebraic multilevel scheme with coarsening by aggregation. We introduce a new aggregation method which, for the targeted class of applications, produces semicoarsening effects whenever desirable, while the number of nodes is decreased by a factor of about 4 at each level, regardless of the problem at hand. Moreover, the number of nonzero entries per row in the successive coarse grid matrices remains approximately constant, ensuring small set up cost and modest memory requirements. This aggregation technique can be used in an algebraic multigrid (AMG)like framework, but better results are obtained with an algebraic multilevel scheme based on a block approximate factorization of the matrix. In this scheme, the block pivot corresponding to fine grid nodes is approximated by a modified incomplete LU (MILU) factorization. To enhance robustness and avoid any potential breakdown, the coarsening process is refined by recasting as “coarse ” fine grid nodes for which the corresponding pivot in this MILU factorization would be negative or too small. Numerical results display the efficiency, the scalability, and the robustness of the resulting preconditioner on a wide set of discrete scalar PDE problems, ranging from the twodimensional Poisson equation to threedimensional convectiondiffusion problems with high Reynolds number and strongly varying convection.