Skip to main content
Log in

Simplicial Faces of the Set of Correlation Matrices

  • Published:
Discrete & Computational Geometry Aims and scope Submit manuscript

Abstract

This paper concerns the facial geometry of the set of \(n \times n\) correlation matrices. The main result states that almost every set of r vertices generates a simplicial face, provided that \(r \le \sqrt{\mathrm {c} n}\), where \(\mathrm {c}\) is an absolute constant. This bound is qualitatively sharp because the set of correlation matrices has no simplicial face generated by more than \(\sqrt{2n}\) vertices.

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.

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. In our parameter regime, it is unlikely that any vertex of \(\mathscr {E}_n\) is chosen more than once, so this model is not substantially different from drawing vertices without replacement.

References

  1. Ahlswede, R., Winter, A.: Strong converse for identification via quantum channels. IEEE Trans. Inform. Theory 48(3), 569–579 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  2. Ben-Tal, A., Nemirovski, A.: Lectures on Modern Convex Optimization: Analysis, Algorithms, and Engineering Applications. MPS/SIAM Series on Optimization. SIAM, Philadelphia, PA (2001)

    Book  MATH  Google Scholar 

  3. Bhatia, R.: Matrix Analysis. Graduate Texts in Mathematics, vol. 169. Springer, New York (1997)

    MATH  Google Scholar 

  4. Delorme, C., Poljak, S.: The performance of an eigenvalue bound on the max-cut problem in some classes of graphs. Discrete Math. 111(1–3), 145–156 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  5. Goemans, M.X., Williamson, D.P.: Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming. J. Assoc. Comput. Mach. 42(6), 1115–1145 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  6. Grothendieck, A.: Résumé de la théorie métrique des produits tensoriels topologiques. Bol. Soc. Mat. São Paulo 8, 1–79 (1953)

    MathSciNet  MATH  Google Scholar 

  7. Hiriart-Urruty, J.-B., Lemaréchal, C.: Fundamentals of Convex Analysis. Grundlehren Text Editions. Springer, Berlin (2001)

    Book  MATH  Google Scholar 

  8. Karp, R.M.: Reducibility among combinatorial problems. In: Miller, R.E., Thatcher, J.W., Bohlinger, J.D. (eds.) Complexity of Computer Computations, pp. 85–103. Plenum, New York (1972)

    Chapter  Google Scholar 

  9. Khot, S., Naor, A.: Grothendieck-type inequalities in combinatorial optimization. Commun. Pure Appl. Math. 65(7), 992–1035 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  10. Koltchinskii, V., Mendelson, S.: Bounding the smallest singular value of a random matrix without concentration. Int. Math. Res. Not. IMRN 2015(23), 12991–13008 (2015)

    MathSciNet  MATH  Google Scholar 

  11. Laurent, M., Poljak, S.: On a positive semidefinite relaxation of the cut polytope. Linear Algebra Appl. 223(224), 439–461 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  12. Laurent, M., Poljak, S.: On the facial structure of the set of correlation matrices. SIAM J. Matrix Anal. Appl. 17(3), 530–547 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  13. McCoy, M., Tropp, J.A.: Two proposals for robust PCA using semidefinite programming. Electron. J. Stat. 5, 1123–1160 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  14. Nesterov, Yu.: Semidefinite relaxation and nonconvex quadratic optimization. Optim. Methods Softw. 9(1–3), 141–160 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  15. O’Donnell, R.: Analysis of Boolean Functions. Cambridge University Press, New York (2014)

    Book  MATH  Google Scholar 

  16. Oliveira, R.I.: The lower tail of random quadratic forms with applications to ordinary least squares. Probab. Theory Relat. Fields 166(3–4), 1175–1194 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  17. Pisier, G.: Grothendieck’s theorem, past and present. Bull. Am. Math. Soc. 49(2), 237–323 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  18. Poljak, S., Rendl, F.: Solving the max-cut problem using eigenvalues. Discrete Appl. Math. 62(1–3), 249–278 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  19. Rockafellar, R.T.: Convex Analysis. Princeton Mathematical Series, vol. 28. Princeton University Press, Princeton, NJ (1970)

    Book  MATH  Google Scholar 

  20. Tropp, J.A.: User-friendly tail bounds for sums of random matrices. Found. Comput. Math. 12(4), 389–434 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  21. Tropp, J.A.: Convex recovery of a structured signal from independent random linear measurements. In: Pfander, G.E. (ed.) Sampling Theory, a Renaissance. Applied and Numerical Harmonic Analysis, pp. 67–101. Birkhäuser, Cham (2015)

    Chapter  Google Scholar 

  22. Tropp, J.A.: An introduction to matrix concentration inequalities. Found. Trends Mach. Learn. 8(1–2), 1–230 (2015)

    Article  MATH  Google Scholar 

Download references

Acknowledgements

The author thanks Richard Küng and Benjamin Recht for helpful conversations related to this work. This research was partially supported by ONR award N00014-11-1002 and the Gordon & Betty Moore Foundation.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Joel A. Tropp.

Additional information

Editor in Charge: János Pach

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tropp, J.A. Simplicial Faces of the Set of Correlation Matrices. Discrete Comput Geom 60, 512–529 (2018). https://doi.org/10.1007/s00454-017-9961-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00454-017-9961-0

Keywords

Mathematics Subject Classification

Navigation