Halfspaces are hard to test with relative error.
X. Chen and A. De and Y. Huang and S. Nadimpalli and R. Servedio and T. Yang.
37th Symposium on Discrete Algorithms (SODA), 2026, to appear.


Abstract:

Several recent works \cite{CDHLNSY25,CPPS25conjunctionDL,CPPS25junta,rel-error-DNF} have studied a model of property testing of Boolean functions under a relative-error criterion. In this model, the distance from a target function $f: \{0,1\}^n \to \{0,1\}$ that is being tested to a function $g$ is defined relative to the number of inputs $x$ for which $f(x)=1$; moreover, testing algorithms in this model have access both to a black-box oracle for $f$ and to independent uniform satisfying assignments of $f$. The motivation for this model is that it provides a natural framework for testing sparse Boolean functions that have few satisfying assignments, analogous to well-studied models for property testing of sparse graphs.

The main result of this paper is a lower bound for testing \emph{halfspaces} (i.e.,~linear threshold functions) in the relative error model: we show that $\tilde{\Omega}(\log n)$ oracle calls are required for any relative-error halfspace testing algorithm over the Boolean hypercube $\{0,1\}^n$. This stands in sharp contrast both with the constant-query testability (independent of $n$) of halfspaces in the standard model \cite{MORS10}, and with the positive results for relative-error testing of many other classes given in \cite{CDHLNSY25,CPPS25conjunctionDL,CPPS25junta,rel-error-DNF}. Our lower bound for halfspaces gives the first example of a well-studied class of functions for which relative-error testing is provably more difficult than standard-model testing.

pdf of full version on arxiv


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