Spectral Norm, Economical Sieve, and Linear Invariance Testing of Boolean Functions
Abstract
Given Boolean functions , we say they are {\em linearly isomorphic} if there exists such that for all . We study this problem in the tolerant property testing framework under the known--unknown model, where is given explicitly and is accessible only via oracle queries, meaning the algorithm may adaptively request the value of for inputs of its choice. Given parameters and , the goal is to distinguish whether there exists such that the normalized Hamming distance between and is at most , or whether for every the distance is at least . Our main result is a tolerant tester making queries to , where is an upper bound on the spectral norm of , improving the previous bound of Wimmer and Yoshida. We complement this with a nearly matching lower bound of for constant (for example, ), improving the prior 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}
}