NEAR-OPTIMAL LOWER BOUNDS ON THE THRESHOLD DEGREE AND SIGN-RANK OF AC0

被引:2
|
作者
Sherstov, Alexander A. [1 ]
Wu, Pei [1 ]
机构
[1] Univ Calif Los Angeles, Los Angeles, CA 90095 USA
关键词
constant-depth circuits; sign-rank; threshold degree; sign-representation by polynomials; communication complexity; unbounded-error communication; MULTIPARTY COMMUNICATION COMPLEXITY; 2; HALFSPACES; POWER; INTERSECTION; CIRCUITS; PRODUCT; SIZE;
D O I
10.1137/20M1312459
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
The threshold degree of a Boolean function f : {0, 1}(n) -> {0, 1} is the minimum degree of a real polynomial p that represents f in sign: sgn p(x) = (-1)(f(x)). A related notion is sign-rank, defined for a Boolean matrix F = [F-ij] as the minimum rank of a real matrix M with sgn Mij = (-1)(Fij). Determining the maximum threshold degree and sign-rank achievable by constant-depth circuits (AC(0)) is a well-known and extensively studied open problem with complexity-theoretic and algorithmic applications. We give an essentially optimal solution to this problem. For any epsilon > 0, we construct an AC(0) circuit in n variables that has threshold degree Omega(n(1-epsilon)) and sign-rank exp(Omega(n(1-epsilon))), improving on the previous best lower bounds of Omega(root n) and exp((Omega) over tilde(root n)), respectively. Our results subsume all previous lower bounds on the threshold degree and sign-rank of AC(0) circuits of any given depth, with a strict improvement starting at depth 4. As a corollary, we also obtain near-optimal bounds on the discrepancy, threshold weight, and threshold density of AC(0), strictly subsuming previous work on these quantities. Our work gives some of the strongest lower bounds to date on the communication complexity of AC(0).
引用
收藏
页码:1 / 86
页数:86
相关论文
共 25 条
  • [21] Near-optimal lower bounds on the multi-party communication complexity of set disjointness
    Chakrabarti, A
    Khot, S
    Sun, XD
    18TH IEEE ANNUAL CONFERENCE ON COMPUTATIONAL COMPLEXITY, PROCEEDINGS, 2003, : 107 - 117
  • [22] Lower bounds and a near-optimal shrinkage estimator for least squares using random projections
    Sridhar S.
    Pilanci M.
    Özgür A.
    Sridhar, Srivatsan (svatsan@stanford.edu), 1600, Institute of Electrical and Electronics Engineers Inc. (01): : 660 - 668
  • [23] Near-optimal solutions and tight lower bounds for the parallel machines scheduling problem with learning effect
    Hidri, Lotfi
    Jemmali, Mahdi
    RAIRO-OPERATIONS RESEARCH, 2020, 54 (02) : 507 - 527
  • [24] Near-Optimal Statistical Query Lower Bounds for Agnostically Learning Intersections of Halfspaces with Gaussian Marginals
    Hsu, Daniel
    Sanford, Clayton
    Servedio, Rocco A.
    Vlatakis-Gkaragkounis, Emmanouil-Vasileios
    CONFERENCE ON LEARNING THEORY, VOL 178, 2022, 178 : 283 - 312
  • [25] Near-Optimal SQ Lower Bounds for Agnostically Learning Halfspaces and ReLUs under Gaussian Marginals
    Diakonikolas, Ilias
    Kane, Daniel M.
    Zarifis, Nikos
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 33, NEURIPS 2020, 2020, 33