Nothing Special   »   [go: up one dir, main page]

Skip to main content
Log in

Random Triangle Theory with Geometry and Applications

  • Published:
Foundations of Computational Mathematics Aims and scope Submit manuscript

Abstract

What is the probability that a random triangle is acute? We explore this old question from a modern viewpoint, taking into account linear algebra, shape theory, numerical analysis, random matrix theory, the Hopf fibration, and much more. One of the best distributions of random triangles takes all six vertex coordinates as independent standard Gaussians. Six can be reduced to four by translation of the center to \((0,0)\) or reformulation as a \(2\times 2\) random matrix problem. In this note, we develop shape theory in its historical context for a wide audience. We hope to encourage others to look again (and differently) at triangles. We provide a new constructive proof, using the geometry of parallelians, of a central result of shape theory: triangle shapes naturally fall on a hemisphere. We give several proofs of the key random result: that triangles are uniformly distributed when the normal distribution is transferred to the hemisphere. A new proof connects to the distribution of random condition numbers. Generalizing to higher dimensions, we obtain the “square root ellipticity statistic” of random matrix theory. Another proof connects the Hopf map to the SVD of \(2\times 2\) matrices. A new theorem describes three similar triangles hidden in the hemisphere. Many triangle properties are reformulated as matrix theorems, providing insight into both. This paper argues for a shift of viewpoint to the modern approaches of random matrix theory. As one example, we propose that the smallest singular value is an effective test for uniformity. New software is developed, and applications are proposed.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  1. Marcus Baker. A collection of formulae for the area of a plane triangle. Annals of Mathematics, 1:134–138, 1885.

    Article  MathSciNet  Google Scholar 

  2. Yasuko Chikuse and Peter E. Jupp. A test of uniformity on shape spaces. Journal of Multivariate Analysis, 88(1):163–176, 2004.

    Article  MATH  MathSciNet  Google Scholar 

  3. Alan Edelman. Eigenvalues and condition numbers of random matrices. SIAM J. on Matrix Analysis and Applications, 9:543–560, 1988.

    Article  MATH  MathSciNet  Google Scholar 

  4. Alan Edelman. Eigenvalues and Condition Numbers of Random Matrices. PhD thesis, Massachusetts Institute of Technology, 1989.

  5. Bennett Eisenberg and Rosemary Sullivan. Random triangles in n dimensions. American Mathematical Monthly, 103(4):308–318, 1996.

    Article  MATH  MathSciNet  Google Scholar 

  6. Gerald S. Goodman. The problem of the broken stick reconsidered (Original problem reprinted from University of Cambridge Senate-House Examinations, Macmillan, 1854.). The Mathematical Intelligencer, 30(3):43–49, 2008.

    Article  MATH  MathSciNet  Google Scholar 

  7. William Kahan. Mathematics written in sand. In Proceedings of the Joint Statistical Meeting held in Toronto August 15-18, 1983, pages 12–26. American Statistical Association, 1983.

  8. William Kahan. Miscalculating area and angles of a needle-like triangle ( from lecture notes for introductory numerical analysis classes ), March 4 2000.

  9. David G. Kendall. (with comments by other authors) A survey of the statistical theory of shape. Statistical Science, 4(2):87–120, 1989.

    Article  MATH  MathSciNet  Google Scholar 

  10. Wilfrid Kendall and Hui-Lin Le. Statistical shape theory. In New Perspectives in Stochastic Geometry, pages 348–373. Oxford University Press, 2010.

  11. Plamen Koev and Alan Edelman. The efficient evaluation of the hypergeometric function of a matrix argument. Mathematics of Computation, 75:833–846, 2006.

    Article  MATH  MathSciNet  Google Scholar 

  12. David W. Lyons. An elementary introduction to the Hopf fibration. Mathematics Magazine, 76(2):87, 2003.

    Article  MathSciNet  Google Scholar 

  13. Robb J. Muirhead. Aspects of Multivariate Statistical Theory. John Wiley, New York, 1982.

    Book  MATH  Google Scholar 

  14. Stephen Portnoy. A Lewis Carroll pillow problem: Probability of an obtuse triangle. Statistical Science, 9(2):279–284, 1994.

    MATH  MathSciNet  Google Scholar 

  15. Rolf Schneider and Wolfgang Weil. Stochastic and Integral Geometry. Probability and Its Applications. Springer-Verlag, 2008.

    Book  Google Scholar 

Download references

Acknowledgments

We would like to thank Wilfrid Kendall, Mike Todd, and Eric Kostlan for their insights. The first author acknowledges NSF support under DMS 1035400 and DMS 1016125. The second author acknowledges NSF support under EFRI 1023152.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Alan Edelman.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Edelman, A., Strang, G. Random Triangle Theory with Geometry and Applications. Found Comput Math 15, 681–713 (2015). https://doi.org/10.1007/s10208-015-9250-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10208-015-9250-3

Keywords

Mathematics Subject Classification

Navigation