Low-Degree Testing over Grids

Authors Prashanth Amireddy , Srikanth Srinivasan , Madhu Sudan

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Prashanth Amireddy
  • School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
Srikanth Srinivasan
  • Department of Computer Science, Aarhus University, Denmark
Madhu Sudan
  • School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA

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Prashanth Amireddy, Srikanth Srinivasan, and Madhu Sudan. Low-Degree Testing over Grids. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 275, pp. 41:1-41:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)


We study the question of local testability of low (constant) degree functions from a product domain 𝒮_1 × … × 𝒮_n to a field 𝔽, where 𝒮_i ⊆ 𝔽 can be arbitrary constant sized sets. We show that this family is locally testable when the grid is "symmetric". That is, if 𝒮_i = 𝒮 for all i, there is a probabilistic algorithm using constantly many queries that distinguishes whether f has a polynomial representation of degree at most d or is Ω(1)-far from having this property. In contrast, we show that there exist asymmetric grids with |𝒮_1| = ⋯ = |𝒮_n| = 3 for which testing requires ω_n(1) queries, thereby establishing that even in the context of polynomials, local testing depends on the structure of the domain and not just the distance of the underlying code. The low-degree testing problem has been studied extensively over the years and a wide variety of tools have been applied to propose and analyze tests. Our work introduces yet another new connection in this rich field, by building low-degree tests out of tests for "junta-degrees". A function f:𝒮_1 × ⋯ × 𝒮_n → 𝒢, for an abelian group 𝒢 is said to be a junta-degree-d function if it is a sum of d-juntas. We derive our low-degree test by giving a new local test for junta-degree-d functions. For the analysis of our tests, we deduce a small-set expansion theorem for spherical/hamming noise over large grids, which may be of independent interest.

Subject Classification

ACM Subject Classification
  • Theory of computation → Probabilistic computation
  • Property testing
  • Low-degree testing
  • Small-set expansion
  • Local testing


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