English

Spectral Norm, Economical Sieve, and Linear Invariance Testing of Boolean Functions

Computational Complexity 2026-01-14 v2

Abstract

Given Boolean functions f,g:F2n{1,+1} f, g : \mathbb{F}_2^n \to \{-1,+1\} , we say they are {\em linearly isomorphic} if there exists AGLn(F2) A \in \mathrm{GL}_n(\mathbb{F}_2) such that f(x)=g(Ax) f(x)=g(Ax) for all x x . We study this problem in the tolerant property testing framework under the known--unknown model, where g g is given explicitly and f f is accessible only via oracle queries, meaning the algorithm may adaptively request the value of f(x) f(x) for inputs xF2n x \in \mathbb{F}_2^n of its choice. Given parameters ϵ0 \epsilon \ge 0 and ω>0 \omega>0 , the goal is to distinguish whether there exists AGLn(F2) A \in \mathrm{GL}_n(\mathbb{F}_{2}) such that the normalized Hamming distance between f f and g(Ax) g(Ax) is at most ϵ \epsilon , or whether for every AGLn(F2) A \in \mathrm{GL}_n(\mathbb{F}_2) the distance is at least ϵ+ω \epsilon+\omega . Our main result is a tolerant tester making O~((m/ω)4) \widetilde{O} \left( \left( m/\omega \right)^4 \right) queries to f f , where m m is an upper bound on the spectral norm of g g , improving the previous O~((m/ω)24) \widetilde{O} \left( \left( m/\omega \right)^{24} \right) bound of Wimmer and Yoshida. We complement this with a nearly matching lower bound of Ω(m2) \Omega(m^2) for constant ω \omega (for example, ω=1/4 \omega=1/4 ), improving the prior Ω(logm) \Omega(\log m) lower bound of Grigorescu, Wimmer and Xie. A key technical ingredient on the algorithmic side is a query-efficient local list corrector. For the lower bound, we give a reduction from communication complexity using a novel subclass of Maiorana--McFarland functions from symmetric-key cryptography.

Keywords

Cite

@article{arxiv.2308.02662,
  title  = {Spectral Norm, Economical Sieve, and Linear Invariance Testing of Boolean Functions},
  author = {Swarnalipa Datta and Arijit Ghosh and Chandrima Kayal and Manaswi Paraashar and Manmatha Roy},
  journal= {arXiv preprint arXiv:2308.02662},
  year   = {2026}
}