Statistical and Machine Learning Techniques Applied to Algorithm Selection for Solving Sparse Linear Systems

@inproceedings{Wang2007StatisticalAM,
  title={Statistical and Machine Learning Techniques Applied to Algorithm Selection for Solving Sparse Linear Systems},
  author={Jonathan L. Wang},
  year={2007}
}
There are many applications and problems in science and engineering that require large-scale numerical simulations and computations. The issue of choosing an appropriate method to solve these problems is very common, however it is not a trivial one, principally because this decision is most of the times too hard for humans to make, or certain degree of expertise and knowledge in the particular discipline, or in mathematics, are required. Thus, the development of a methodology that can… CONTINUE READING

References

Publications referenced by this paper.
Showing 1-10 of 50 references

An approach recommender for preconditioned iterative solvers

  • George, Sarin, T. 2007 George, V. Sarin
  • 2007

PETSc home

  • Balay et al, S. 2007 Balay, +4 authors B. Smith
  • 2007

H

  • Y. Fukui, Hasegawa
  • (2005). Test of Iterative Solvers on ITBL. High…
  • 2005
1 Excerpt

A data mining approach to matrix preconditioning problem

  • Xu, Zhang, S. 2004 Xu, J. Zhang
  • Proceedings of the Eighth Workshop on Mining…
  • 2004

Domain Decomposition: Parallel Multilevel Methods for Elliptic Partial Differential Equations

  • Smith et al, B. 2004 Smith, P. Bjorstad, W. Gropp
  • 2004

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