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Lower Bound Methods for Sign-Rank and Their Limitations

Authors Hamed Hatami, Pooya Hatami , William Pires, Ran Tao, Rosie Zhao

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Author Details

Hamed Hatami
  • School of Computer Science, McGill University, Montreal, Canada
Pooya Hatami
  • Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA
William Pires
  • School of Computer Science, McGill University, Montreal, Canada
Ran Tao
  • Department of Mathematics and Statistics, McGill University, Montreal, Canada
Rosie Zhao
  • School of Computer Science, McGill University, Montreal, Canada

Funding

  • Hatami, Hamed: Supported by an NSERC grant.
  • Hatami, Pooya: Supported by NSF grant CCF-1947546.

Acknowledgements

We wish to thank Shachar Lovett and Shay Moran for helpful discussions. We would like to thank Shay Moran for sharing the proof of Proposition B.3 with us.

Cite AsGet BibTex

Hamed Hatami, Pooya Hatami, William Pires, Ran Tao, and Rosie Zhao. Lower Bound Methods for Sign-Rank and Their Limitations. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 245, pp. 22:1-22:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
https://doi.org/10.4230/LIPIcs.APPROX/RANDOM.2022.22

Abstract

The sign-rank of a matrix A with ±1 entries is the smallest rank of a real matrix with the same sign pattern as A. To the best of our knowledge, there are only three known methods for proving lower bounds on the sign-rank of explicit matrices: (i) Sign-rank is at least the VC-dimension; (ii) Forster’s method, which states that sign-rank is at least the inverse of the largest possible average margin among the representations of the matrix by points and half-spaces; (iii) Sign-rank is at least a logarithmic function of the density of the largest monochromatic rectangle. We prove several results regarding the limitations of these methods. - We prove that, qualitatively, the monochromatic rectangle density is the strongest of these three lower bounds. If it fails to provide a super-constant lower bound for the sign-rank of a matrix, then the other two methods will fail as well. - We show that there exist n × n matrices with sign-rank n^Ω(1) for which none of these methods can provide a super-constant lower bound. - We show that sign-rank is at most an exponential function of the deterministic communication complexity with access to an equality oracle. We combine this result with Green and Sanders' quantitative version of Cohen’s idempotent theorem to show that for a large class of sign matrices (e.g., xor-lifts), sign-rank is at most an exponential function of the γ₂ norm of the matrix. We conjecture that this holds for all sign matrices. - Towards answering a question of Linial, Mendelson, Schechtman, and Shraibman regarding the relation between sign-rank and discrepancy, we conjecture that sign-ranks of the ±1 adjacency matrices of hypercube graphs can be arbitrarily large. We prove that none of the three lower bound techniques can resolve this conjecture in the affirmative.

Subject Classification

ACM Subject Classification
  • Theory of computation → Communication complexity
  • Theory of computation → Boolean function learning
Keywords
  • Average Margin
  • Communication complexity
  • margin complexity
  • monochromatic rectangle
  • Sign-rank
  • Unbounded-error communication complexity
  • VC-dimension

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