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Efficient Algorithms for Certifying Lower Bounds on the Discrepancy of Random Matrices

Author Prayaag Venkat

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Author Details

Prayaag Venkat
  • School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA

Funding

  • Venkat, Prayaag: The author was supported by NSF grant CCF-1565264 and an NSF Graduate Fellowship under grant DGE1745303.

Acknowledgements

The author would like to thank Boaz Barak, Alex Wein and Ilias Zadik for helpful discussions.

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Prayaag Venkat. Efficient Algorithms for Certifying Lower Bounds on the Discrepancy of Random Matrices. In 14th Innovations in Theoretical Computer Science Conference (ITCS 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 251, pp. 98:1-98:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023) https://doi.org/10.4230/LIPIcs.ITCS.2023.98

Abstract

In this paper, we initiate the study of the algorithmic problem of certifying lower bounds on the discrepancy of random matrices: given an input matrix A ∈ ℝ^{m × n}, output a value that is a lower bound on disc(A) = min_{x ∈ {± 1}ⁿ} ‖Ax‖_∞ for every A, but is close to the typical value of disc(A) with high probability over the choice of a random A. This problem is important because of its connections to conjecturally-hard average-case problems such as negatively-spiked PCA [Afonso S. Bandeira et al., 2020], the number-balancing problem [Gamarnik and Kızıldağ, 2021] and refuting random constraint satisfaction problems [Prasad Raghavendra et al., 2017]. We give the first polynomial-time algorithms with non-trivial guarantees for two main settings. First, when the entries of A are i.i.d. standard Gaussians, it is known that disc(A) = Θ (√n2^{-n/m}) with high probability [Karthekeyan Chandrasekaran and Santosh S. Vempala, 2014; Aubin et al., 2019; Paxton Turner et al., 2020] and that super-constant levels of the Sum-of-Squares SDP hierarchy fail to certify anything better than disc(A) ≥ 0 when m < n - o(n) [Mrinalkanti Ghosh et al., 2020]. In contrast, our algorithm certifies that disc(A) ≥ exp(-O(n²/m)) with high probability. As an application, this formally refutes a conjecture of Bandeira, Kunisky, and Wein [Afonso S. Bandeira et al., 2020] on the computational hardness of the detection problem in the negatively-spiked Wishart model. Second, we consider the integer partitioning problem: given n uniformly random b-bit integers a₁, …, a_n, certify the non-existence of a perfect partition, i.e. certify that disc(A) ≥ 1 for A = (a₁, …, a_n). Under the scaling b = α n, it is known that the probability of the existence of a perfect partition undergoes a phase transition from 1 to 0 at α = 1 [Christian Borgs et al., 2001]; our algorithm certifies the non-existence of perfect partitions for some α = O(n). We also give efficient non-deterministic algorithms with significantly improved guarantees, raising the possibility that the landscape of these certification problems closely resembles that of e.g. the problem of refuting random 3SAT formulas in the unsatisfiable regime. Our algorithms involve a reduction to the Shortest Vector Problem and employ the Lenstra-Lenstra-Lovász algorithm.

Subject Classification

ACM Subject Classification
  • Theory of computation → Randomness, geometry and discrete structures
Keywords
  • Average-case discrepancy theory
  • lattices
  • shortest vector problem

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