An Optimal Tester for $k$-Linear

Abstract: A Boolean function $f:{0,1}^n\to {0,1}$ is $k$-linear if it returns the sum (over the binary field $F_2$) of $k$ coordinates of the input. In this paper, we study property testing of the classes $k$-Linear, the class of all $k$-linear functions, and $k$-Linear$<sup>*$, the class $\cup_{j=0}<sup>kj$-Linear. We give a non-adaptive distribution-free two-sided $\epsilon$-tester for $k$-Linear that makes $O\left(k\log k+\frac{1}{\epsilon}\right)$ queries. This matches the lower bound known from the literature. We then give a non-adaptive distribution-free one-sided $\epsilon$-tester for $k$-Linear$<sup>*$ that makes the same number of queries and show that any non-adaptive uniform-distribution one-sided $\epsilon$-tester for $k$-Linear must make at least $\tilde\Omega(k)\log n+\Omega(1/\epsilon)$ queries. The latter bound, almost matches the upper bound $O(k\log n+1/\epsilon)$ known from the literature. We then show that any adaptive uniform-distribution one-sided $\epsilon$-tester for $k$-Linear must make at least $\tilde\Omega(\sqrt{k})\log n+\Omega(1/\epsilon)$ queries.