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Influences of Rounding Errors in Solving Large Sparse Linear Systems

In: Developments in Reliable Computing

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  • Axel Facius

    (Universität Karlsruhe (TH), Institut für Angewandte Mathematik)

Abstract

In many research areas like structural mechanics, economics, meteorology, and fluid dynamics, problems are mapped to large sparse linear systems via discretization. The resulting matrices are often ill-conditioned with condition numbers of about 1016 and higher. Usually these systems are preconditioned before they are fed to an iterative solver. Especially for ill-conditioned systems, we show that we have to be careful with these three classical steps — discretization, preconditioning, and (iterative) solving. For Krylov subspace solvers we give some detailed analysis and show possible improvements based on a multiple precision arithmetic. This special arithmetic can be easily implemented using the exact scalar product — a technique for computing scalar products of floating point vectors exactly.

Suggested Citation

  • Axel Facius, 1999. "Influences of Rounding Errors in Solving Large Sparse Linear Systems," Springer Books, in: Tibor Csendes (ed.), Developments in Reliable Computing, pages 17-30, Springer.
  • Handle: RePEc:spr:sprchp:978-94-017-1247-7_2
    DOI: 10.1007/978-94-017-1247-7_2
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