Skip to main content

Advertisement

Springer Nature Link
Account
Menu
Find a journal Publish with us Track your research
Search
Cart
  1. Home
  2. Mathematical Programming
  3. Article

Semidefinite optimization in discrepancy theory

  • Full Length Paper
  • Series B
  • Open access
  • Published: 17 May 2012
  • Volume 134, pages 5–22, (2012)
  • Cite this article

You have full access to this open access article

Download PDF
Mathematical Programming Submit manuscript
Semidefinite optimization in discrepancy theory
Download PDF
  • Nikhil Bansal1 
  • 900 Accesses

  • 2 Citations

  • Explore all metrics

Abstract

Recently, there have been several new developments in discrepancy theory based on connections to semidefinite programming. This connection has been useful in several ways. It gives efficient polynomial time algorithms for several problems for which only non-constructive results were previously known. It also leads to several new structural results in discrepancy itself, such as tightness of the so-called determinant lower bound, improved bounds on the discrepancy of the union of set systems and so on. We will give a brief survey of these results, focussing on the main ideas and the techniques involved.

Article PDF

Download to read the full article text

Similar content being viewed by others

An exact semidefinite programming approach for the max-mean dispersion problem

Article 26 August 2016

On the complexity of testing attainment of the optimal value in nonlinear optimization

Article 06 July 2019

Theoretical insights and algorithmic tools for decision diagram-based optimization

Article 03 March 2016

Explore related subjects

Discover the latest articles, books and news in related subjects, suggested using machine learning.
  • Convex and Discrete Geometry
  • Discrete Mathematics
  • Linear Algebra
  • Mathematics
  • Mathematics and Computing
  • Calculus of Variations and Optimization
Use our pre-submission checklist

Avoid common mistakes on your manuscript.

References

  1. Alon N., Spencer J.H.: The Probabilistic Method. 2nd edn. Wiley, New York (2000)

    Book  MATH  Google Scholar 

  2. Banaszczyk W.: Balancing vectors and gaussian measures of n-dimensional convex bodies. Random Struct. Algorithms 12(4), 351–360 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  3. Bansal, N.: Constructive algorithms for discrepancy minimization. In: Foundations of Computer Science (FOCS), pp. 3–10 (2010)

  4. Bansal, N., Spencer, J.: Deterministic discrepancy minimization. In: ESA, pp. 408–420 (2011)

  5. Beck J.: Roth’s estimate on the discrepancy of integer sequences is nearly sharp. Combinatorica 1, 319–325 (1981)

    Article  MathSciNet  MATH  Google Scholar 

  6. Beck J., Fiala T.: Integer-making theorems. Discret. Appl. Math. 3, 1–8 (1981)

    Article  MathSciNet  MATH  Google Scholar 

  7. Bednarchak D., Helm M.: A note on the beck-fiala theorem. Combinatorica 17, 147–149 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  8. Chandrasekaran, K., Vempala, S.: A discrepancy based approach to integer programming. Arxiv 1111.4649 (2011)

  9. Charikar, M., Newman, A., Nikolov, A.: Tight hardness results for minimizing discrepancy. In: Proceedings of the 22nd Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) (2011)

  10. Chazelle B.: The Discrepancy Method: Randomness and Complexity. Cambridge University Press, Cambridge (2001)

    Google Scholar 

  11. Doerr B.: Linear and hereditary discrepancy. Combin. Probab. Comput. 9(4), 349–354 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  12. Drmota M., Tichy R.F.: Sequences, Discrepancies and Applications. Springer, Berlin (1997)

    MATH  Google Scholar 

  13. Eisenbrand, F., Pálvölgyi, D., Rothvoss, T.: Bin packing via discrepancy of permutations. In: Symposium on Discrete Algorithms (SODA), pp. 476–481 (2011)

  14. Gärtner B., Matoušek J.: Approximation Algorithms and Semidefinite Programming. Springer, Berlin (2012)

    Book  MATH  Google Scholar 

  15. Ghouila-Houri A.: Caracterisation des matrices totalement unimodularies. Comptes Rendus Hebdomadaires des Seances de l’Academie des Sciences 254, 1191–1194 (1962)

    Google Scholar 

  16. Haxell P.: A condition for matchability in hypergraphs. Graphs Combin. 11, 245–248 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  17. Kim J.H., Matoušek J., Vu V.: Discrepancy after adding a single set. Combinatorica 25(4), 499–501 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  18. Lovász L., Spencer J., Vesztergombi K.: Discrepancy of set-systems and matrices. Eur. J. Combin. 7, 151–160 (1986)

    MATH  Google Scholar 

  19. Matoušek, J.: The determinant bound for discrepancy is almost tight. Manuscript, Arxiv 1101:0767

  20. Matoušek J.: An lp version of the beck-fiala conjecture. Eur. J. Combin. 19, 175–182 (1998)

    Article  MATH  Google Scholar 

  21. Matoušek J.: Geometric Discrepancy: An Illustrated Guide. Springer, Berlin (2010)

    Book  MATH  Google Scholar 

  22. Moser, R.A.: A constructive proof of the lovász local lemma. In: Symposium on Theory of Computering (STOC), pp. 343–350 (2009)

  23. Newman, A., Nikolov, A.: A counterexample to beck’s conjecture on the discrepancy of three permutations. Arxiv 1104.2922 (2011)

  24. Pálvölgyi D.: Indecomposable coverings with concave polygons. Discret. Comput. Geom. 44, 577–588 (2010)

    Article  MATH  Google Scholar 

  25. Rothvoss, T.: The entropy rounding method in approximation algorithms. In: Symposium on Discrete Algorithms (SODA), pp. 356–372 (2012)

  26. Spencer J.: Six standard deviations suffice. Trans. Am. Math. Soc. 289(2), 679–706 (1985)

    Article  MATH  Google Scholar 

  27. Srinivasan, A.: Improving the discrepancy bound for sparse matrices: better approximations for sparse lattice approximation problems. In: Symposium on Discrete Algorithms (SODA), pp. 692–701 (1997)

  28. Vandenberghe L., Boyd S.: Semidefinite programming. SIAM Rev. 38, 49–95 (1996)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Open Access

This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

Author information

Authors and Affiliations

  1. Eindhoven University of Technology, Eindhoven, The Netherlands

    Nikhil Bansal

Authors
  1. Nikhil Bansal
    View author publications

    Search author on:PubMed Google Scholar

Corresponding author

Correspondence to Nikhil Bansal.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and permissions

About this article

Cite this article

Bansal, N. Semidefinite optimization in discrepancy theory. Math. Program. 134, 5–22 (2012). https://doi.org/10.1007/s10107-012-0546-7

Download citation

  • Received: 13 October 2011

  • Accepted: 16 April 2012

  • Published: 17 May 2012

  • Issue date: August 2012

  • DOI: https://doi.org/10.1007/s10107-012-0546-7

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Keywords

  • Semidefinite optimization
  • Discrepancy theory
  • Rounding error
  • Algorithms

Mathematics Subject Classification

  • 90
  • 05
Use our pre-submission checklist

Avoid common mistakes on your manuscript.

Advertisement

Search

Navigation

  • Find a journal
  • Publish with us
  • Track your research

Discover content

  • Journals A-Z
  • Books A-Z

Publish with us

  • Journal finder
  • Publish your research
  • Language editing
  • Open access publishing

Products and services

  • Our products
  • Librarians
  • Societies
  • Partners and advertisers

Our brands

  • Springer
  • Nature Portfolio
  • BMC
  • Palgrave Macmillan
  • Apress
  • Discover
  • Your US state privacy rights
  • Accessibility statement
  • Terms and conditions
  • Privacy policy
  • Help and support
  • Legal notice
  • Cancel contracts here

Not affiliated

Springer Nature

© 2025 Springer Nature