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          <dc:title>Combinatorial problems in solving linear systems</dc:title>
          <dc:creator>Duff, Iain S.</dc:creator>
          <dc:creator>Ucar, Bora</dc:creator>
          <dc:subject>Combinatorial scientific computing</dc:subject>
          <dc:subject>graph theory</dc:subject>
          <dc:subject>combinatorial optimization</dc:subject>
          <dc:subject>sparse matrices</dc:subject>
          <dc:subject>linear system solution</dc:subject>
          <dc:description>Numerical linear algebra and combinatorial optimization are vast&#13;
subjects; as is their interaction. In virtually all cases there should&#13;
be a notion of sparsity for a combinatorial problem to arise. Sparse&#13;
matrices, therefore, form the basis of the interaction of these two&#13;
seemingly disparate subjects. As the core of many of today's numerical linear&#13;
algebra computations consists of sparse linear system solutions, we&#13;
will cover combinatorial problems, notions, and algorithms relating to&#13;
those computations.&#13;
&#13;
This talk is thus concerned with direct and iterative methods for sparse &#13;
linear systems and their intercation with combinatorial optimization.&#13;
On the direct methods side, we discuss matrix ordering; bipartite matching &#13;
and matrix scaling for better pivoting; task assignment and scheduling &#13;
for parallel multifrontal solvers. On the iterative method side, we discuss&#13;
preconditioning techniques including incomplete factor preconditioners&#13;
(notion of level of fill-in), support graph preconditioners (graph&#13;
embedding concepts), and algebraic multigrids (independent sets in&#13;
undirected graphs).&#13;
&#13;
In a separate part of the talk, we discuss methods that aim to exploit&#13;
sparsity during linear system solution. These methods include block&#13;
diagonalization of the matrix; efficient triangular system solutions&#13;
for right-hand side vectors of single nonzero entries. Towards the&#13;
end, we mention, quite briefly as they are topics of other invited&#13;
talks, some other areas whose interactions with combinatorial&#13;
optimization are of great benefit to numerical linear algebra.  These&#13;
include graph and hypergraph partitioning for load balancing problems,&#13;
and colouring problems in numerical optimization. On closing, we&#13;
compile and list a set of open problems.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Iain S. Duff and Bora Ucar</dc:contributor>
          <dc:date>2009</dc:date>
          <dc:relation>Is Part Of Dagstuhl Seminar Proceedings, Volume 9061, Combinatorial Scientific Computing (2009)</dc:relation>
          <dc:type>InProceedings</dc:type>
          <dc:type>Text</dc:type>
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          <dc:format>application/pdf</dc:format>
          <dc:identifier>doi:10.4230/DagSemProc.09061.8</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-20779</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.09061.8</dc:identifier>
          <dc:language>eng</dc:language>
          <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
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