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The problem of testing monotonicity for Boolean functions on the hypergrid, $$f:[n]^d \to \{0,1\}$$ is a classic topic in property testing. When $n=2$, the domain is the hypercube. For the hypercube case, a breakthrough result of Khot-Minzer-Safra (FOCS 2015) gave a non-adaptive, one-sided tester making $$\otilde(\eps^{-2}\sqrt{d})$$ queries. Up to polylog $$d$$ and $$\eps$$ factors, this bound matches the $$\widetilde{\Omega}(\sqrt{d})$$-query non-adaptive lower bound (Chen-De-Servedio-Tan (STOC 2015), Chen-Waingarten-Xie (STOC 2017)). For any $n > 2$, the optimal non-adaptive complexity was unknown. A previous result of the authors achieves a $$\otilde(d^{5/6})$$-query upper bound (SODA 2020), quite far from the $$\sqrt{d}$$ bound for the hypercube. In this paper, we resolve the non-adaptive complexity of monotonicity testing for all constant $$n$$, up to $$\poly(\eps^{-1}\log d)$$ factors. Specifically, we give a non-adaptive, one-sided monotonicity tester making $$\otilde(\eps^{-2}n\sqrt{d})$$ queries. From a technical standpoint, we prove new directed isoperimetric theorems over the hypergrid $[n]^d$. These results generalize the celebrated directed Talagrand inequalities that were only known for the hypercube.
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Testing k-Monotonicity
A Boolean {\em $$k$$-monotone} function defined over a finite poset domain $${\cal D}$$ alternates between the values $$0$$ and $$1$$ at most $$k$$ times on any ascending chain in $${\cal D}$$. Therefore, $$k$$-monotone functions are natural generalizations of the classical {\em monotone} functions, which are the {\em $$1$$-monotone} functions. Motivated by the recent interest in $$k$$-monotone functions in the context of circuit complexity and learning theory, and by the central role that monotonicity testing plays in the context of property testing, we initiate a systematic study of $$k$$-monotone functions, in the property testing model. In this model, the goal is to distinguish functions that are $$k$$-monotone (or are close to being $$k$$-monotone) from functions that are far from being $$k$$-monotone. Our results include the following: \begin{enumerate} \item We demonstrate a separation between testing $$k$$-monotonicity and testing monotonicity, on the hypercube domain $$\{0,1\}^d$$, for $$k\geq 3$$; \item We demonstrate a separation between testing and learning on $$\{0,1\}^d$$, for $$k=\omega(\log d)$$: testing $$k$$-monotonicity can be performed with $$2^{O(\sqrt d \cdot \log d\cdot \log{1/\eps})}$$ queries, while learning $$k$$-monotone functions requires $$2^{\Omega(k\cdot \sqrt d\cdot{1/\eps})}$$ queries (Blais et al. (RANDOM 2015)). \item We present a tolerant test for functions $$f\colon[n]^d\to \{0,1\}$$ with complexity independent of $$n$$, which makes progress on a problem left open by Berman et al. (STOC 2014). \end{enumerate} Our techniques exploit the testing-by-learning paradigm, use novel applications of Fourier analysis on the grid $$[n]^d$, and draw connections to distribution testing techniques.
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- Award ID(s):
- 1649515
- PAR ID:
- 10033552
- Date Published:
- Journal Name:
- ITCS
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Monotonicity testing of Boolean functions on the hypergrid, $$f:[n]^d \to \{0,1\}$$, is a classic topic in property testing. Determining the non-adaptive complexity of this problem is an important open question. For arbitrary $$n$$, [Black-Chakrabarty-Seshadhri, SODA 2020] describe a tester with query complexity $$\widetilde{O}(\varepsilon^{-4/3}d^{5/6})$$. This complexity is independent of $$n$$, but has a suboptimal dependence on $$d$$. Recently, [Braverman-Khot-Kindler-Minzer, ITCS 2023] and [Black-Chakrabarty-Seshadhri, STOC 2023] describe $$\widetilde{O}(\varepsilon^{-2} n^3\sqrt{d})$$ and $$\widetilde{O}(\varepsilon^{-2} n\sqrt{d})$$-query testers, respectively. These testers have an almost optimal dependence on $$d$$, but a suboptimal polynomial dependence on $$n$$. \smallskip In this paper, we describe a non-adaptive, one-sided monotonicity tester with query complexity $$O(\varepsilon^{-2} d^{1/2 + o(1)})$$, \emph{independent} of $$n$$. Up to the $$d^{o(1)}$$-factors, our result resolves the non-adaptive complexity of monotonicity testing for Boolean functions on hypergrids. The independence of $$n$$ yields a non-adaptive, one-sided $$O(\varepsilon^{-2} d^{1/2 + o(1)})$$-query monotonicity tester for Boolean functions $$f:\mathbb{R}^d \to \{0,1\}$$ associated with an arbitrary product measure.more » « less
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Tauman_Kalai, Yael (Ed.)We show improved monotonicity testers for the Boolean hypercube under the p-biased measure, as well as over the hypergrid [m]ⁿ. Our results are: 1) For any p ∈ (0,1), for the p-biased hypercube we show a non-adaptive tester that makes Õ(√n/ε²) queries, accepts monotone functions with probability 1 and rejects functions that are ε-far from monotone with probability at least 2/3. 2) For all m ∈ ℕ, we show an Õ(√nm³/ε²) query monotonicity tester over [m]ⁿ. We also establish corresponding directed isoperimetric inequalities in these domains, analogous to the isoperimetric inequality in [Subhash Khot et al., 2018]. Previously, the best known tester due to Black, Chakrabarty and Seshadhri [Hadley Black et al., 2018] had Ω(n^{5/6}) query complexity. Our results are optimal up to poly-logarithmic factors and the dependency on m. Our proof uses a notion of monotone embeddings of measures into the Boolean hypercube that can be used to reduce the problem of monotonicity testing over an arbitrary product domains to the Boolean cube. The embedding maps a function over a product domain of dimension n into a function over a Boolean cube of a larger dimension n', while preserving its distance from being monotone; an embedding is considered efficient if n' is not much larger than n, and we show how to construct efficient embeddings in the above mentioned settings.more » « less
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