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Congress of the Italian Association for Artificial Intelligence

AI*IA 1991: Trends in Artificial Intelligence pp 208–217Cite as

Knowledge compilation to speed up numerical optimization

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 549))

Abstract

Many important application problems can be formalized as constrained non-linear optimization tasks. However, numerical methods for solving such problems are brittle and do not scale well. This paper describes a method to speed up and increase the reliability of numerical optimization by (a) optimizing the computation of the objective function, and (b) splitting the objective function into special cases that possess differentiable closed forms. This allows us to replace a single inefficient non-gradient-based optimization by a set of efficient numerical gradient-directed optimizations that can be performed in parallel. In the domain of 2-dimensional structural design, this procedure yields a 95% speedup over traditional optimization methods and decreases the dependence of the numerical methods on having a good starting point.

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References

  1. Wesley Braudaway. Constraint incorporation using constrained reformulation. Tech.Rep. LCSR-TR-100 Computer Science Dept., Rutgers University, April 1988.

    Google Scholar 

  2. Giuseppe Cerbone and Thomas G. Dietterich. Inductive and numerical methods in knowledge compilation. In Proceedings of the Workshop on Change of Representation and Problem Reformulation, 1989.

    Google Scholar 

  3. Thomas Ellman. Explanation-based learning: A survey of programs and perspectives. ACM Computing Surveys, 21(2):163–222, 1989.

    Google Scholar 

  4. James E. Gordon. Structures: or, Why things don't fall down. Plenum Press, New York, 1978.

    Google Scholar 

  5. Steve Minton. Empirical results concerning the utility of explanation-based learning. In Proceedings AAAI, 1988.

    Google Scholar 

  6. A.C. Palmer and D.J. Sheppard. Optimizing the shape of pin-jointed structures. In Proc. of the Institution of Civil Engineers, pages 363–376, 1970.

    Google Scholar 

  7. Garret N. Vanderplaats. Numerical Optimization Techniques for engineering design with applications. New York: McGraw Hill, 1984.

    Google Scholar 

  8. Chu-Kia Wang and Charles G. Salmon. Introductory Structural Analysis. Prentice Hall, New Jersey, 1984.

    Google Scholar 

  9. Steven Wolfram. Mathematica. Wolfram Research, 1988.

    Google Scholar 

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Authors and Affiliations

  1. Computer Science Dept., Oregon State University, 97331-3202, Corvallis, OR, USA

    Giuseppe Cerbone & Thomas G. Dietterich

Authors
  1. Giuseppe Cerbone
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  2. Thomas G. Dietterich
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Edoardo Ardizzone Salvatore Gaglio Filippo Sorbello

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© 1991 Springer-Verlag Berlin Heidelberg

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Cerbone, G., Dietterich, T.G. (1991). Knowledge compilation to speed up numerical optimization. In: Ardizzone, E., Gaglio, S., Sorbello, F. (eds) Trends in Artificial Intelligence. AI*IA 1991. Lecture Notes in Computer Science, vol 549. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-54712-6_233

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  • DOI: https://doi.org/10.1007/3-540-54712-6_233

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-54712-9

  • Online ISBN: 978-3-540-46443-3

  • eBook Packages: Springer Book Archive

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