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<item>
  <id>06034154</id>
  <dt>a</dt>
  <an>06034154</an>
  <augroup>
    <au>Sexton, Alan P.</au>
    <au>Sorge, Volker</au>
    <au>Watt, Stephen M.</au>
  </augroup>
  <ti>Computing with abstract matrix structures.</ti>
  <so>May, John P. (ed.), ISSAC 2009. Proceedings of the 2009 International Symposium on Symbolic and Algebraic Computation, Seoul, July 28--31, 2009. New York, NY: Association for Computing Machinery (ACM) (ISBN 978-1-60558-609-0). 325-332 (2009).</so>
  <py>2009</py>
  <pu>New York, NY: Association for Computing Machinery (ACM)</pu>
  <lagroup>
    <la>EN</la>
  </lagroup>
  <ccgroup>
  </ccgroup>
  <utgroup>
  </utgroup>
  <cigroup>
  </cigroup>
  <ligroup>
    <li>doi:10.1145/1576702.1576747</li>
  </ligroup>
  <abgroup>
    <ab>Summary: Classes of matrices are often presented with symbolic dimensions using a mixture of terms and ellipsis symbols to describe their internal structure. While working with such classes of matrices is everyday mathematical practice, it has little automated support. We describe an algebraic encoding of such matrices in terms of support functions and define the corresponding addition and multiplication algorithms. It is, however, non-trivial to retrieve the structural description of the matrix resulting from these operations. We therefore define an abstract matrix as an encoding of support function combinations that enables simple recovery of the structural properties. This allows us to define arithmetic algorithms for abstract matrices as extensions of those for support function combinations using a normalising term rewrite system.</ab>
    <rv></rv>
  </abgroup>
</item>