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Optimal Pruning in Parametric Differential Equations

  • Micha Janssen
  • Pascal Van Hentenryck
  • Yves Deville
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2239)

Abstract

Initial value problems for parametric ordinary differential equations (ODEs) arise in many areas of science and engineering. Since some of the data is uncertain, traditional numerical methods do not apply. This paper considers a constraint satisfaction approach that enhances traditional interval methods with a pruning component which uses a relaxation of the ODE and Hermite interpolation polynomials. It solves the main theoretical and practical open issue left in this approach: the choice of an optimal evaluation time for the relaxation. As a consequence, the constraint satisfaction approach is shown to provide a quadratic (asymptotical) improvement in accuracy over the best interval methods, while improving their running times. Experimental results confirm the theoretical results.

Keywords

Local Error Interpolation Point Interval Method Predictor Process Interval Extension 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Micha Janssen
    • 1
  • Pascal Van Hentenryck
    • 2
  • Yves Deville
    • 1
  1. 1.UCLLouvain-La-NeuveBelgium
  2. 2.Brown UniversityProvidence

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