On the VNP-Hardness of Some Monomial Symmetric Polynomials

Authors Radu Curticapean , Nutan Limaye , Srikanth Srinivasan



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Author Details

Radu Curticapean
  • IT University of Copenhagen, Denmark
  • Basic Algorithms Research Copenhagen, Denmark
Nutan Limaye
  • IT University of Copenhagen, Denmark
  • Basic Algorithms Research Copenhagen, Denmark
Srikanth Srinivasan
  • Department of Computer Science, Aarhus University, Denmark
  • On leave from Department of Mathematics, IIT Bombay, India

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Radu Curticapean, Nutan Limaye, and Srikanth Srinivasan. On the VNP-Hardness of Some Monomial Symmetric Polynomials. In 42nd IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 250, pp. 16:1-16:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022) https://doi.org/10.4230/LIPIcs.FSTTCS.2022.16

Abstract

A polynomial P ∈ 𝔽[x_1,…,x_n] is said to be symmetric if it is invariant under any permutation of its input variables. The study of symmetric polynomials is a classical topic in mathematics, specifically in algebraic combinatorics and representation theory. More recently, they have been studied in several works in computer science, especially in algebraic complexity theory.
In this paper, we prove the computational hardness of one of the most basic kinds of symmetric polynomials: the monomial symmetric polynomials, which are obtained by summing all distinct permutations of a single monomial. This family of symmetric functions is a natural basis for the space of symmetric polynomials (over any field), and generalizes many well-studied families such as the elementary symmetric polynomials and the power-sum symmetric polynomials.
We show that certain families of monomial symmetric polynomials are VNP-complete with respect to oracle reductions. This stands in stark contrast to the case of elementary and power symmetric polynomials, both of which have constant-depth circuits of polynomial size.

Subject Classification

ACM Subject Classification
  • Theory of computation → Algebraic complexity theory
  • Computing methodologies → Representation of polynomials
Keywords
  • algebraic complexity
  • symmetric polynomial
  • permanent
  • Sidon set

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References

  1. Jayadev Acharya, Hirakendu Das, Alon Orlitsky, and Ananda Theertha Suresh. A unified maximum likelihood approach for estimating symmetric properties of discrete distributions. In Doina Precup and Yee Whye Teh, editors, Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017, volume 70 of Proceedings of Machine Learning Research, pages 11-21. PMLR, 2017. URL: http://proceedings.mlr.press/v70/acharya17a.html.
  2. Markus Bläser and Gorav Jindal. On the complexity of symmetric polynomials. In Avrim Blum, editor, 10th Innovations in Theoretical Computer Science Conference, ITCS 2019, January 10-12, 2019, San Diego, California, USA, volume 124 of LIPIcs, pages 47:1-47:14. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2019. URL: https://doi.org/10.4230/LIPIcs.ITCS.2019.47.
  3. Peter Bürgisser, Michael Clausen, and Mohammad Amin Shokrollahi. Algebraic complexity theory, volume 315 of Grundlehren der mathematischen Wissenschaften. Springer, 1997. Google Scholar
  4. Prasad Chaugule, Mrinal Kumar, Nutan Limaye, Chandra Kanta Mohapatra, Adrian She, and Srikanth Srinivasan. Schur polynomials do not have small formulas if the determinant doesn't. In Shubhangi Saraf, editor, 35th Computational Complexity Conference, CCC 2020, July 28-31, 2020, Saarbrücken, Germany (Virtual Conference), volume 169 of LIPIcs, pages 14:1-14:27. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2020. URL: https://doi.org/10.4230/LIPIcs.CCC.2020.14.
  5. Sergey Fomin, Dima Grigoriev, and Gleb A. Koshevoy. Subtraction-free complexity, cluster transformations, and spanning trees. Found. Comput. Math., 16(1):1-31, 2016. URL: https://doi.org/10.1007/s10208-014-9231-y.
  6. Hervé Fournier, Nutan Limaye, Meena Mahajan, and Srikanth Srinivasan. The shifted partial derivative complexity of elementary symmetric polynomials. Theory Comput., 13(1):1-34, 2017. URL: https://doi.org/10.4086/toc.2017.v013a009.
  7. Dima Grigoriev and Gleb A. Koshevoy. Complexity of tropical schur polynomials. J. Symb. Comput., 74:46-54, 2016. URL: https://doi.org/10.1016/j.jsc.2015.05.005.
  8. Pavel Hrubes and Amir Yehudayoff. Homogeneous formulas and symmetric polynomials. Comput. Complex., 20(3):559-578, 2011. URL: https://doi.org/10.1007/s00037-011-0007-3.
  9. Mrinal Kumar and Ben Lee Volk. Lower bounds for matrix factorization. In Shubhangi Saraf, editor, 35th Computational Complexity Conference, CCC 2020, July 28-31, 2020, Saarbrücken, Germany (Virtual Conference), volume 169 of LIPIcs, pages 5:1-5:20. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2020. URL: https://doi.org/10.4230/LIPIcs.CCC.2020.5.
  10. Nutan Limaye, Srikanth Srinivasan, and Sébastien Tavenas. Superpolynomial lower bounds against low-depth algebraic circuits. In 62nd IEEE Annual Symposium on Foundations of Computer Science, FOCS 2021, Denver, CO, USA, February 7-10, 2022, pages 804-814. IEEE, 2021. URL: https://doi.org/10.1109/FOCS52979.2021.00083.
  11. I. G. (Ian Grant) Macdonald. Symmetric functions and Hall polynomials. Oxford mathematical monographs. Clarendon Press ; Oxford University Press, Oxford : New York, 1979. Google Scholar
  12. Henryk Minc and Marvin Marcus. Permanents. Encyclopedia of Mathematics and its Applications. Cambridge University Press, 1984. URL: https://doi.org/10.1017/CBO9781107340688.
  13. Noam Nisan and Avi Wigderson. Lower bounds on arithmetic circuits via partial derivatives. Comput. Complexity, 6(3):217-234, 1996/97. URL: https://doi.org/10.1007/BF01294256.
  14. Amritanshu Prasad. Representation theory, volume 147 of Cambridge Studies in Advanced Mathematics. Cambridge University Press, Delhi, 2015. A combinatorial viewpoint. URL: https://doi.org/10.1017/CBO9781139976824.
  15. Amir Shpilka. Affine projections of symmetric polynomials. J. Comput. Syst. Sci., 65(4):639-659, 2002. URL: https://doi.org/10.1016/S0022-0000(02)00021-1.
  16. Amir Shpilka and Avi Wigderson. Depth-3 arithmetic circuits over fields of characteristic zero. Comput. Complex., 10(1):1-27, 2001. URL: https://doi.org/10.1007/PL00001609.
  17. Amir Shpilka and Amir Yehudayoff. Arithmetic circuits: A survey of recent results and open questions. Found. Trends Theor. Comput. Sci., 5(3-4):207-388, 2010. URL: https://doi.org/10.1561/0400000039.
  18. Richard P. Stanley. Enumerative combinatorics. Vol. 2, volume 62 of Cambridge Studies in Advanced Mathematics. Cambridge University Press, Cambridge, 1999. With a foreword by Gian-Carlo Rota and appendix 1 by Sergey Fomin. URL: https://doi.org/10.1017/CBO9780511609589.
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