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Combinatorial problems in solving linear systems
, 2009
"... Numerical linear algebra and combinatorial optimization are vast subjects; as is their interaction. In virtually all cases there should be a notion of sparsity for a combinatorial problem to arise. Sparse matrices therefore form the basis of the interaction of these two seemingly disparate subjects ..."
Abstract

Cited by 5 (3 self)
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Numerical linear algebra and combinatorial optimization are vast subjects; as is their interaction. In virtually all cases there should be a notion of sparsity for a combinatorial problem to arise. Sparse matrices therefore form the basis of the interaction of these two seemingly disparate subjects. As the core of many of today’s numerical linear algebra computations consists of the solution of sparse linear system by direct or iterative methods, we survey some combinatorial problems, ideas, and algorithms relating to these computations. On the direct methods side, we discuss issues such as matrix ordering; bipartite matching and matrix scaling for better pivoting; task assignment and scheduling for parallel multifrontal solvers. On the iterative method side, we discuss preconditioning techniques including incomplete factorization preconditioners, support graph preconditioners, and algebraic multigrid. In a separate part, we discuss the block triangular form of sparse matrices.
HYPERGRAPHBASED COMBINATORIAL OPTIMIZATION OF MATRIXVECTOR MULTIPLICATION
, 2009
"... Combinatorial scientific computing plays an important enabling role in computational science, particularly in high performance scientific computing. In this thesis, we will describe our work on optimizing matrixvector multiplication using combinatorial techniques. Our research has focused on two di ..."
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Cited by 1 (0 self)
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Combinatorial scientific computing plays an important enabling role in computational science, particularly in high performance scientific computing. In this thesis, we will describe our work on optimizing matrixvector multiplication using combinatorial techniques. Our research has focused on two different problems in combinatorial scientific computing, both involving matrixvector multiplication, and both are solved using hypergraph models. For both of these problems, the cost of the combinatorial optimization process can be effectively amortized over many matrixvector products. The first problem we address is optimization of serial matrixvector multiplication for relatively small, dense matrices that arise in finite element assembly. Previous work showed that combinatorial optimization of matrixvector multiplication can lead to faster assembly of finite element stiffness matrices by eliminating redundant operations. Based on a graph model characterizing row relationships, a more efficient set of operations can be generated to perform matrixvector multiplication. We improved this graph model by extending the
Experiments on Instance Preconditioning for Combinatorial Solvers
"... Abstract. Preconditioning of matrices, with the objective to solve large systems of linear equations more efficiently, is an active area of research. In contrast, there is no comparable systematic effort to precondition graphbased instances before solving them with a combinatorial solver. This pape ..."
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Abstract. Preconditioning of matrices, with the objective to solve large systems of linear equations more efficiently, is an active area of research. In contrast, there is no comparable systematic effort to precondition graphbased instances before solving them with a combinatorial solver. This paper asks the question: Does an existing preconditioning technique with known merits in solving systems of linear equations also improves the efficiency and effectiveness for a class of solvers on instances of combinatorial problems? We propose an experimental approach to evaluate merits of the Fiedler permutation when solving instances of the maximal independent set (MaxIS) problem with several solvers. Preliminary results are not only encouraging, they also demonstrate the value of Fiedler permutation when characterizing fundamental structural properties of graph instances themselves. Version: 2008TR Preconditioning v1Brglez, 28Jan2008 1
Combinatorial problems in . . .
, 2009
"... Numerical linear algebra and combinatorial optimization are vast subjects; as is their interaction. In virtually all cases there should be a notion of sparsity for a combinatorial problem to arise. Sparse matrices therefore form the basis of the interaction of these two seemingly disparate subject ..."
Abstract
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Numerical linear algebra and combinatorial optimization are vast subjects; as is their interaction. In virtually all cases there should be a notion of sparsity for a combinatorial problem to arise. Sparse matrices therefore form the basis of the interaction of these two seemingly disparate subjects. As the core of many of today’s numerical linear algebra computations consists of the solution of sparse linear system by direct or iterative methods, we survey some combinatorial problems, ideas, and algorithms relating to these computations. On the direct methods side, we discuss issues such as matrix ordering; bipartite matching and matrix scaling for better pivoting; task assignment and scheduling for parallel multifrontal solvers. On the iterative method side, we discuss preconditioning techniques including incomplete factorization preconditioners, support graph preconditioners, and algebraic multigrid. In a separate part, we discuss the block triangular form of sparse matrices.
Atlas Centre, RAL,
, 2009
"... Abstract. Numerical linear algebra and combinatorial optimization are vast subjects; as is their interaction. In virtually all cases there should be a notion of sparsity for a combinatorial problem to arise. Sparse matrices therefore form the basis of the interaction of these two seemingly disparate ..."
Abstract
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Abstract. Numerical linear algebra and combinatorial optimization are vast subjects; as is their interaction. In virtually all cases there should be a notion of sparsity for a combinatorial problem to arise. Sparse matrices therefore form the basis of the interaction of these two seemingly disparate subjects. As the core of many of today’s numerical linear algebra computations consists of the solution of sparse linear system by direct or iterative methods, we survey some combinatorial problems, ideas, and algorithms relating to these computations. On the direct methods side, we discuss issues such as matrix ordering; bipartite matching and matrix scaling for better pivoting; task assignment and scheduling for parallel multifrontal solvers. On the iterative method side, we discuss preconditioning techniques including incomplete factorization preconditioners, support graph preconditioners, and algebraic multigrid. In a separate part, we discuss the block triangular form of sparse matrices.
unknown title
"... On domain decomposition with space filling curves for the parallel solution of the coupled Maxwell/Vlasov equations ..."
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On domain decomposition with space filling curves for the parallel solution of the coupled Maxwell/Vlasov equations
unknown title
"... Contributions au partitionnement de graphes parallèle multiniveaux (Contributions to parallel multilevel graph partitioning) Soutenue et présentée publiquement le: 3 décembre 2009 Après avis des rapporteurs: ..."
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Contributions au partitionnement de graphes parallèle multiniveaux (Contributions to parallel multilevel graph partitioning) Soutenue et présentée publiquement le: 3 décembre 2009 Après avis des rapporteurs: