Skip to main content
Log in

A Constraint Satisfaction Approach to a Circuit Design Problem

  • Published:
Journal of Global Optimization Aims and scope Submit manuscript

Abstract

A classical circuit-design problem from Ebers and Moll (1954) features a system of nine nonlinear equations in nine variables that is very challenging both for local and global methods. This system was solved globally using an interval method by Ratschek and Rokne (1993) in the box [0, 10]9. Their algorithm had enormous costs (i.e., over 14 months using a network of 30 Sun Sparc-1 workstations) but they state that ‘at this time, we know no other method which has been applied to this circuit design problem and which has led to the same guaranteed result of locating exactly one solution in this huge domain, completed with a reliable error estimate’. The present paper gives a novel branch-and-prune algorithm that obtains a unique safe box for the above system within reasonable computation times. The algorithm combines traditional interval techniques with an adaptation of discrete constraint-satisfaction techniques to continuous problems. Of particular interest is the simplicity of the approach.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. G. Alefeld and J. Herzberger (1983), Introduction to Interval Computations. New York: Academic Press.

    Google Scholar 

  2. F. Benhamou, D. McAllister, and P. Van Hentenryck (1994), CLP (Intervals) revisited, in Proceedings of the International Symposium on Logic Programming (ILPS-94), pages 124–138. Ithaca, NY.

  3. F. Benhamou and W. Older (1997), Applying interval arithmetic to real, integer and Boolean constraints, Journal of Logic Programming 32(1): 1–24.

    Article  Google Scholar 

  4. O. Caprani and K. Madsen (1980), Mean value forms in interval analysis, Computing 25: 147–154.

    Article  Google Scholar 

  5. J. G. Cleary (1987), Logical arithmetic, Future Generation Computing Systems 2(2): 125–149.

    Google Scholar 

  6. J. J. Ebers and J. L. Moll (1954), Large-scale behaviour of junction transistors, IEE Proc. 42: 1761–1772.

    Google Scholar 

  7. E. R. Hansen and R. I. Greenberg (1983), An interval Newton method, Appl. Math. Comput. 12: 89–98.

    Article  Google Scholar 

  8. E. R. Hansen and S. Sengupta (1981), Bounding solutions of systems of equations using interval analysis, BIT 21: 203–211.

    Article  Google Scholar 

  9. E. R. Hansen and R. R. Smith (1967), Interval arithmetic in matrix computation: Part II, SIAM Journal on Numerical Analysis 4: 1–9.

    Article  Google Scholar 

  10. H. Hong and V. Stahl (1994), Safe starting regions by fixed points and tightening, Computing, 53(3–4): 323–335.

    Article  Google Scholar 

  11. R. B. Kearfott (1990), Preconditioners for the interval Gauss-Seidel method, SIAM Journal of Numerical Analysis 27: 804–822.

    Article  Google Scholar 

  12. R. B. Kearfott (1991), A review of preconditioners for the interval Gauss-Seidel method, Interval Computations 1: 59–85.

    Google Scholar 

  13. R. Krawczyk (1969), Newton-Algorithmen zur Bestimmung von Nullstellen mit Fehlerschranken, Computing 4: 187–201.

    Article  Google Scholar 

  14. O. L'Homme (1993), Consistency techniques for numerical constraint satisfaction problems, in: Proceedings of the 1993 International Joint Conference on Artificial Intelligence. Chamberry, France.

    Google Scholar 

  15. A. K. Mackworth (1977), Consistency in networks of relations, Artificial Intelligence 8(1): 99–118.

    Article  Google Scholar 

  16. U. Montanari (1974), Networks of constraints: fundamental properties and applications to picture processing, Information Science 7(2): 95–132.

    Article  Google Scholar 

  17. R. E. Moore (1966), Interval Analysis. Englewood Cliffs, NJ: Prentice-Hall.

    Google Scholar 

  18. R. E. Moore (1979), Methods and Applications of Interval Analysis. SIAM Publ.

  19. R. E. Moore and S. T. Jones (1977), Safe starting regions for iterative methods, SIAM Journal on Numerical Analysis 14: 1051–1065.

    Article  Google Scholar 

  20. A. Neumaier (1990), Interval Methods for Systems of Equations. PHI Series in Computer Science. Cambridge: Cambridge University Press.

    Google Scholar 

  21. W. Older and A. Vellino (1993), Constraint arithmetic on real intervals, in Constraint Logic Programming: Selected Research. Cambridge, Mass.: The MIT Press.

    Google Scholar 

  22. H. Ratschek and J. Rokne (1988), New Computer Methods for Global Optimization. Chichester: Ellis Horwood Ltd.

    Google Scholar 

  23. H. Ratschek and J. Rokne (1993), Experiments using interval analysis for solving a circuit design problem, Journal of Global Optimization 3: 501–518.

    Article  Google Scholar 

  24. P. Van Hentenryck, D. McAllister, and D. Kapur (1997), Solving polynomial systems using a branch and prune approach, SIAM Journal on Numerical Analysis 34(2): 797–827.

    Article  Google Scholar 

  25. J. Verschelde, P. Verlinden, and R. Cools (1994), Homotopies exploiting Newton polytopes for solving sparse polynomial systems, SIAM Journal on Numerical Analysis 31(3): 915–930.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Puget, JF., van Hentenryck, P. A Constraint Satisfaction Approach to a Circuit Design Problem. Journal of Global Optimization 13, 75–93 (1998). https://doi.org/10.1023/A:1008236911603

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1008236911603

Navigation