Skip to main content

Volume Computation Using a Direct Monte Carlo Method

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4598))

Abstract

Volume computation is a traditional, extremely hard but highly demanding task. It has been widely studied and many interesting theoretical results are obtained in recent years. But very little attention is paid to put theory into use in practice. On the other hand, applications emerging in computer science and other fields require practically effective methods to compute/estimate volume. This paper presents a practical Monte Carlo sampling algorithm on volume computation/estimation and a corresponding prototype tool is implemented. Preliminary experimental results on lower dimensional instances show a good approximation of volume computation for both convex and non-convex cases. While there is no theoretical performance guarantee, the method itself even works for the case when there is only a membership oracle, which tells whether a point is inside the geometric body or not, and no description of the actual geometric body is given.

This work is partially supported by the National Natural Science Foundation (NSFC) under grant number 60673044 and 60633010, and by Montana EPSCOR Visiting Scholar’s Program.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Applegate, D., Kannan, R.: Sampling and integration of near log-concave functions. In: Proc. 23rd annual ACM symp. on Theory of Computing (STOC), pp. 156–163 (1991)

    Google Scholar 

  2. Bollobás, B.: Volume estimates and rapid mixing. Flavors of geometry. Math. Sci. Res. Inst. Publ. 31, 151–182 (1997)

    Google Scholar 

  3. Büeler, B., Enge, A., Fukuda, K.: Exact volume computation for polytopes: a practical study. Polytopes–combinatorics and computation (1998)

    Google Scholar 

  4. Dyer, M., Frieze, A.: On the complexity of computing the volume of a polyhedron. SIAM J. Comput. 17(5), 967–974 (1988)

    Article  MATH  MathSciNet  Google Scholar 

  5. Dyer, M., Frieze, A.: Computing the volume of convex bodies: A case where randomness provably helps. In: Proc. 44th Symp. in Applied Mathematics (PSAM) (1991)

    Google Scholar 

  6. Dyer, M., Frieze, A., Kannan, R.: A random polynomial-time algorithm for approximating the volume of convex bodies. J. ACM 38(1), 1–17 (1991)

    Article  MATH  MathSciNet  Google Scholar 

  7. Gritzmann, P., Klee, V.: On the complexity of some basic problems in computational convexity: II. volume and mixed volumes. Polytopes: abstract, convex and computational (Scarborough, ON, 1993), NATO Adv. Sci. Inst. Ser. C Math. Phys. Sci., pp. 373–466 (1994)

    Google Scholar 

  8. Kannan, R., Lovász, L., Simonovits, M.: Random walks and an O*(n5) volume algorithm for convex bodies. Random Struct. Algorithms 11(1), 1–50 (1997)

    Article  MATH  Google Scholar 

  9. ó Lovász, L.: How to compute the volume? Jber. d. Dt. Math.-Verein, Jubiläumstagung, B. G. Teubner, Stuttgart, pp. 138–151 (1990)

    Google Scholar 

  10. Lovász, L., Simonovits, M.: The mixing rate of markov chains, an isoperimetric inequality, and computing the volume. In: Proc. 31th IEEE Annual Symp. on Found. of Comp. Sci (FOCS), pp. 482–491 (1990)

    Google Scholar 

  11. Lovász, L., Simonovits, M.: Random walks in a convex body and an improved volume algorithm. Random Struct. Algorithms 4(4), 359–412 (1993)

    Article  MATH  Google Scholar 

  12. Lovász, L., Vempala, S.: Simulated annealing in convex bodies and an O*(n4) volume algorithm. In: ó Lovász, L. (ed.) Proc. 44th IEEE Annual Symp. on Found. of Comp. Sci (FOCS), pp. 650–659 (2003)

    Google Scholar 

  13. Rademacher, L., Vempala, S.: Dispersion of mass and the complexity of randomized geometric algorithms. In: Proc. 47th IEEE Annual Symp. on Found. of Comp. Sci (FOCS), pp. 729–738 (2006)

    Google Scholar 

  14. Simonovits, M.: How to compute the volume in high dimension? Mathematical Programming 97, 337–374 (2003)

    MATH  MathSciNet  Google Scholar 

  15. Weisstein, E.: Ball. From MathWorld – A Wolfram Web Resource (2003), available at http://mathworld.wolfram.com/Ball.html

Download references

Author information

Authors and Affiliations

Authors

Editor information

Guohui Lin

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Liu, S., Zhang, J., Zhu, B. (2007). Volume Computation Using a Direct Monte Carlo Method . In: Lin, G. (eds) Computing and Combinatorics. COCOON 2007. Lecture Notes in Computer Science, vol 4598. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73545-8_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-73545-8_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73544-1

  • Online ISBN: 978-3-540-73545-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics