Improved Upper Bounds for the Rigidity of Kronecker Products

Author Bohdan Kivva



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Bohdan Kivva
  • University of Chicago, IL, USA

Acknowledgements

The author is grateful to his advisor László Babai for helpful discussions, his help in improving the organization of the paper, and for pointing out improvements to the results and simplifications of the proofs.

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Bohdan Kivva. Improved Upper Bounds for the Rigidity of Kronecker Products. In 46th International Symposium on Mathematical Foundations of Computer Science (MFCS 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 202, pp. 68:1-68:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021) https://doi.org/10.4230/LIPIcs.MFCS.2021.68

Abstract

The rigidity of a matrix A for target rank r is the minimum number of entries of A that need to be changed in order to obtain a matrix of rank at most r. At MFCS'77, Valiant introduced matrix rigidity as a tool to prove circuit lower bounds for linear functions and since then this notion received much attention and found applications in other areas of complexity theory. The problem of constructing an explicit family of matrices that are sufficiently rigid for Valiant’s reduction (Valiant-rigid) still remains open. Moreover, since 2017 most of the long-studied candidates have been shown not to be Valiant-rigid.
Some of those former candidates for rigidity are Kronecker products of small matrices. In a recent paper (STOC'21), Alman gave a general non-rigidity result for such matrices: he showed that if an n× n matrix A (over any field) is a Kronecker product of d× d matrices M₁,… ,M_k (so n = d^k) (d ≥ 2) then changing only n^{1+ε} entries of A one can reduce its rank to ≤ n^{1-γ}, where 1/γ is roughly 2^d/ε².
In this note we improve this result in two directions. First, we do not require the matrices M_i to have equal size. Second, we reduce 1/γ from exponential in d to roughly d^{3/2}/ε² (where d is the maximum size of the matrices M_i), and to nearly linear (roughly d/ε²) for matrices M_i of sizes within a constant factor of each other.
As an application of our results we significantly expand the class of Hadamard matrices that are known not to be Valiant-rigid; these now include the Kronecker products of Paley-Hadamard matrices and Hadamard matrices of bounded size.

Subject Classification

ACM Subject Classification
  • Theory of computation
  • Theory of computation → Complexity theory and logic
Keywords
  • Matrix rigidity
  • Kronecker product
  • Hadamard matrices

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References

  1. Josh Alman. Kronecker products, low-depth circuits, and matrix rigidity. In Proc. 53rd STOC, pages 772-785. ACM Press, 2021. https://arxiv.org/abs/2102.11992. URL: https://doi.org/10.1145/3406325.3451008.
  2. Josh Alman and Ryan Williams. Probabilistic rank and matrix rigidity. In Proc. 49th STOC, pages 17:1-17:23. ACM Press, 2017. URL: https://doi.org/10.1145/3055399.3055484.
  3. László Babai and Bohdan Kivva. Matrix rigidity depends on the target field. In 36th Computational Complexity Conf. (CCC'21), volume 200, pages 41:1-41:26. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik, 2021. URL: https://doi.org/10.4230/LIPIcs.CCC.2021.41.
  4. Bruno Codenotti, Pavel Pudlák, and Giovanni Resta. Some structural properties of low-rank matrices related to computational complexity. Theoretical Computer Science, 235(1):89-107, 2000. URL: https://doi.org/10.1016/S0304-3975(99)00185-1.
  5. Zeev Dvir and Benjamin L. Edelman. Matrix rigidity and the Croot-Lev-Pach lemma. Theory of Computing, 15(8):1-7, 2019. URL: https://doi.org/10.4086/toc.2019.v015a008.
  6. Zeev Dvir and Allen Liu. Fourier and circulant matrices are not rigid. Theory of Computing, 16(20):1-48, 2020. URL: https://doi.org/10.4086/toc.2020.v016a020.
  7. Oded Goldreich and Avi Wigderson. On the size of depth-three boolean circuits for computing multilinear functions. Computational Complexity and Property Testing, pages 41-86, 2020. URL: https://doi.org/10.1007/978-3-030-43662-9_6.
  8. A. Hedayat and Walter Dennis Wallis. Hadamard matrices and their applications. Annals of Statistics, 6(6):1184-1238, 1978. URL: https://doi.org/10.1214/aos/1176344370.
  9. Kathy J. Horadam. Hadamard matrices and their applications. Princeton university press, 2012. URL: https://doi.org/10.1515/9781400842902.
  10. Satyanarayana V. Lokam. Complexity lower bounds using linear algebra. Foundations and Trends in Theoretical Computer Science, 4(1-2):1-155, 2009. URL: https://doi.org/10.1561/0400000011.
  11. Pavel Pudlák and Petr Savický. Private communication, cited in [Raz89], 1988. Google Scholar
  12. Alexander Razborov. On Rigid Matrices. Technical report, Steklov Math. Inst., 1989. (In Russian, http://people.cs.uchicago.edu/~razborov/files/rigid.pdf).
  13. Leslie G. Valiant. Graph-theoretic arguments in low-level complexity. In Math. Found. Comp. Sci. (MFCS'77), pages 162-176. Springer, 1977. URL: https://doi.org/10.1007/3-540-08353-7_135.
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