Search for: All records

Abstract Let$$\Sigma$$be an alphabet and$$\mu$$be a distribution on$$\Sigma ^k$$for some$$k \geqslant 2$$. Let$$\alpha \gt 0$$be the minimum probability of a tuple in the support of$$\mu$$(denoted$$\mathsf{supp}(\mu )$$). We treat the parameters$$\Sigma , k, \mu , \alpha$$as fixed and constant. We say that the distribution$$\mu$$has a linear embedding if there exist an Abelian group$$G$$(with the identity element$$0_G$$) and mappings$$\sigma _i : \Sigma \rightarrow G$$,$$1 \leqslant i \leqslant k$$, such that at least one of the mappings is non-constant and for every$$(a_1, a_2, \ldots , a_k)\in \mathsf{supp}(\mu )$$,$$\sum _{i=1}^k \sigma _i(a_i) = 0_G$$. In [Bhangale-Khot-Minzer, STOC 2022], the authors asked the following analytical question. Let$$f_i: \Sigma ^n\rightarrow [\!-1,1]$$be bounded functions, such that at least one of the functions$$f_i$$essentially has degree at least$$d$$, meaning that the Fourier mass of$$f_i$$on terms of degree less than$$d$$is at most$$\delta$$. If$$\mu$$has no linear embedding (over any Abelian group), then is it necessarily the case that\begin{equation*}\left | \mathop {\mathbb{E}}_{({\textbf {x}}_1, {\textbf {x}}_2, \ldots , {\textbf {x}}_k)\sim \mu ^{\otimes n}}[f_1({\textbf {x}}_1)f_2({\textbf {x}}_2)\cdots f_k({\textbf {x}}_k)] \right | = o_{d, \delta }(1),\end{equation*}where the right hand side$$\to 0$$as the degree$$d \to \infty$$and$$\delta \to 0$$? In this paper, we answer this analytical question fully and in the affirmative for$$k=3$$. We also show the following two applications of the result.1.The first application is related to hardness of approximation. Using the reduction from [5], we show that for every$$3$$-ary predicate$$P:\Sigma ^3 \to \{0,1\}$$such that$$P$$has no linear embedding, anSDP (semi-definite programming) integrality gap instanceof a$$P$$-Constraint Satisfaction Problem (CSP) instance with gap$$(1,s)$$can be translated into a dictatorship test with completeness$$1$$and soundness$$s+o(1)$$, under certain additional conditions on the instance.2.The second application is related to additive combinatorics. We show that if the distribution$$\mu$$on$$\Sigma ^3$$has no linear embedding, marginals of$$\mu$$are uniform on$$\Sigma$$, and$$(a,a,a)\in \texttt{supp}(\mu )$$for every$$a\in \Sigma$$, then every large enough subset of$$\Sigma ^n$$contains a triple$$({\textbf {x}}_1, {\textbf {x}}_2,{\textbf {x}}_3)$$from$$\mu ^{\otimes n}$$(and in fact a significant density of such triples). 

We study parallel repetition of k-player games where the constraints satisfy the projection property. We prove exponential decay in the value of a parallel repetition of projection games with a value less than 1. 

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.