Low-rank matrices, tournaments, and symmetric designs

被引:0
|
作者
Balachandran, Niranjan [1 ]
Sankarnarayanan, Brahadeesh [1 ]
机构
[1] Indian Inst Technol, Dept Math, Mumbai 400076, Maharashtra, India
关键词
Rank; Tournament; Symmetric design; Bipartite graph; Multiplicity;
D O I
10.1016/j.laa.2024.04.006
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
Let a=(a(i))(i >= 1) be a sequence in a field F, and f:F x F -> F be a function such that f(a(i),a(i))not equal 0 for all i >= 1. For any tournament T over [n], consider the nxn symmetric matrix MT with zero diagonal whose (i,j)th entry (for i<j) is f(a(i),a(j)) if i -> j in T, and f(a(j),a(i)) if j -> i in T. It is known (cf. Balachandran et al., Linear Algebra Appl. 658 (2023), 310-318) that if T is a uniformly random tournament over [n], then rank(M-T)>=((1)/(2)-o(1))n with high probability when char(F)not equal 2 and f is a linear function. In this paper, we investigate the other extremal question: how low can the ranks of such matrices be? We work with sequences a that take only two distinct values, so the rank of any such n x n matrix is at least n/2. First, we show that the rank of any such matrix depends on whether an associated bipartite graph has certain eigenvalues of high multiplicity. Using this, we show that if f is linear, then there are real matrices M-T(f;a) of rank at most (n)/(2)+O(1). For rational matrices, we show that for each epsilon > 0 we can find a sequence a(epsilon) for which there are matrices M-T(f;a) of rank at most (12+epsilon)n+O(1). These matrices are constructed from symmetric designs, and we also use them to produce bisection-closed families of size greater than left perpendicular3n/2right perpendicular-2 for n <= 15, which improves the previously best known bound given in [5]. (c) 2024 Elsevier Inc. All rights reserved.
引用
收藏
页码:136 / 147
页数:12
相关论文
共 50 条
  • [31] MATRIX COMPLETION FOR MATRICES WITH LOW-RANK DISPLACEMENT
    Lazzaro, Damiana
    Morigi, Serena
    ELECTRONIC TRANSACTIONS ON NUMERICAL ANALYSIS, 2020, 53 : 481 - 499
  • [32] Fast low-rank approximation for covariance matrices
    Belabbas, Mohamed-Ali
    Wolfe, Patrick J.
    2007 2ND IEEE INTERNATIONAL WORKSHOP ON COMPUTATIONAL ADVANCES IN MULTI-SENSOR ADAPTIVE PROCESSING, 2007, : 181 - 184
  • [33] On the eigenvalues of specially low-rank perturbed matrices
    Zhou, Yunkai
    APPLIED MATHEMATICS AND COMPUTATION, 2011, 217 (24) : 10267 - 10270
  • [34] LOW-RANK FACTORIZATIONS IN DATA SPARSE HIERARCHICAL ALGORITHMS FOR PRECONDITIONING SYMMETRIC POSITIVE DEFINITE MATRICES
    Agullo, Emmanuel
    Darve, Eric
    Giraud, Luc
    Harness, Yuval
    SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS, 2018, 39 (04) : 1701 - 1725
  • [35] SYMMETRIC GENERALIZED LOW RANK APPROXIMATIONS OF MATRICES
    Inoue, Kohei
    Hara, Kenji
    Urahama, Kiichi
    2012 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2012, : 949 - 952
  • [36] Fast randomized numerical rank estimation for numerically low-rank matrices
    Meier, Maike
    Nakatsukasa, Yuji
    Linear Algebra and Its Applications, 2024, 686 : 1 - 32
  • [37] Fast randomized numerical rank estimation for numerically low-rank matrices
    Meier, Maike
    Nakatsukasa, Yuji
    LINEAR ALGEBRA AND ITS APPLICATIONS, 2024, 686 : 1 - 32
  • [38] Low-rank seismic denoising with optimal rank selection for hankel matrices
    Wang, Chong
    Zhu, Zhihui
    Gu, Hanming
    GEOPHYSICAL PROSPECTING, 2020, 68 (03) : 892 - 909
  • [39] Recovery of low-rank matrices based on the rank null space properties
    Gao, Yi
    Han, Xuanli
    Ma, Mingde
    INTERNATIONAL JOURNAL OF WAVELETS MULTIRESOLUTION AND INFORMATION PROCESSING, 2017, 15 (04)
  • [40] Optimizing low-rank adaptation with decomposed matrices and adaptive rank allocation
    Zhang, Dacao
    Yang, Fan
    Zhang, Kun
    Li, Xin
    Wei, Si
    Hong, Richang
    Wang, Meng
    FRONTIERS OF COMPUTER SCIENCE, 2025, 19 (05)