We initiate the study of *unate distributions* over $\{\pm1\}^n$ -- a natural analogue of unate Boolean functions -- by considering two basic testing problems that parallel well-studied questions for monotone distributions: - Uniformity Testing of Unate Distributions: We show that $\widetilde{\Theta}(n^{3/2})$ samples are sufficient and necessary, in contrast to the $\widetilde{\Theta}(n)$ sample complexity of the analogous problem for monotone distributions (Rubinfeld and Servedio, STOC 2005; Adamaszek, Czumaj, and Sohler, SODA 2010). - Unateness Testing of Arbitrary Distributions: We give a tester that uses $\widetilde{O}(n^{3/2})$ conditional samples in the subcube conditional model. On the other hand, every tester that draws conditional samples in a similar fashion, namely from $O(1)$-dimensional subcubes, must have an $\widetilde{\Omega}(n^{2/3})$ complexity. In the same model, the complexity of monotonicity testing was recently shown to be $\widetilde{\Theta}(n)$ (Chakrabarty et al., STOC 2025). Our algorithms for both problems significantly outperform the naive approach of reducing to the monotone case, which would incur $\Omega(n^2)$ sample complexity. Our uniformity tester relies on a subroutine that"weakly"learns the hidden orientations of a unate distribution, together with a new correlation bound for these estimates. Both tools may be of independent interest in studying monotonicity and unateness over $\{\pm1\}^n$.
Daeho Lee, Shivam Nadimpalli, Mingda Qiao et al.· 0 citations
Graph $k$-coloring is one of the classic NP-complete problems. Previous work has studied its average time complexity, defined to be the average runtime of computing a $k$-coloring over the set of all $k$-colorable graphs on $n$ vertices. A highly influential result of Dyer-Frieze from 1989 gave an algorithm with $O(n^2)$ average runtime for constant $k$. This quadratic runtime appeared natural (and possibly even optimal) since almost all $k$-colorable graphs have $\Theta(n^2)$ edges, so one needs at least this time in order to read the (entire) input. However, this was later improved by Ku\v{c}era in 1995 to average runtime $O(n^2/k)$ for every $k \leq n^{c}$ where $c \in (0, 1)$. Nevertheless, in the most interesting case of $k = O(1)$, the best-known bound remained quadratic in $n$. The true average complexity of the $k$-coloring problem has remained elusive for the last three decades. We break the longstanding quadratic barrier. Our main result in this paper shows that the exact average-case complexity of this fundamental problem is $\Theta(nk)$ for every $k \leq n^{c'}$ and some $c'\in (0, 1)$. For $k = O(1)$, this reveals the average sublinear nature of $k$-colorability: the average-case complexity is linear in $n$, and thus sublinear in the size of the input. We further show that our $\Theta(nk)$ average runtime is optimal, since a simple bound proves that every algorithm that correctly $k$-colors all $k$-colorable graphs requires $\Omega(n k)$ average runtime. Our proofs draw on ideas from sublinear and local algorithms and also yield a local computation algorithm (LCA) for $k$-coloring with average-case probe complexity $\text{poly}(k)$. A key new ingredient in our algorithm is a method for certifying the unique colorability of random subgraphs, using tools from the theory of graph regularity.
Cassandra Marcussen, Edward Pyne, R. Rubinfeld et al.· 0 citations