Sketching approximability of all finite CSPs

Abstract

A constraint satisfaction problem (CSP), \(\mathsf {Max-CSP}(\mathcal {F}) \), is specified by a finite set of constraints \(\mathcal {F} \subseteq \lbrace [q]^k \rightarrow \lbrace 0,1\rbrace \rbrace \) for positive integers q and k. An instance of the problem on n variables is given by m applications of constraints from \(\mathcal {F} \) to subsequences of the n variables, and the goal is to find an assignment to the variables that satisfies the maximum number of constraints. In the (γ, β)-approximation version of the problem for parameters 0 ≤ β < γ ≤ 1, the goal is to distinguish instances where at least γ fraction of the constraints can be satisfied from instances where at most β fraction of the constraints can be satisfied.

In this work, we consider the approximability of this problem in the context of sketching algorithms and give a dichotomy result. Specifically, for every family \(\mathcal {F} \) and every β < γ, we show that either a linear sketching algorithm solves the problem in polylogarithmic space, or the problem is not solvable by any sketching algorithm in \(o(\sqrt {n}) \) space. In particular, we give non-trivial approximation algorithms using polylogarithmic space for infinitely many constraint satisfaction problems.

We also extend previously known lower bounds for general streaming algorithms to a wide variety of problems, and in particular the case of q = k = 2, where we get a dichotomy, and the case when the satisfying assignments of the constraints of \(\mathcal {F} \) support a distribution on [q]^k with uniform marginals.

Prior to this work, other than sporadic examples, the only systematic classes of CSPs that were analyzed considered the setting of Boolean variables q = 2, binary constraints k = 2, singleton families \(|\mathcal {F}|=1 \) and only considered the setting where constraints are placed on literals rather than variables.

Our positive results show wide applicability of bias-based algorithms used previously by [47] and [41], which we extend to include richer norm estimation algorithms, by giving a systematic way to discover biases. Our negative results combine the Fourier analytic methods of [56], which we extend to a wider class of CSPs, with a rich collection of reductions among communication complexity problems that lie at the heart of the negative results. In particular, previous works used Fourier analysis over the Boolean cube to initiate their results and the results seemed particularly tailored to functions on Boolean literals (i.e., with negations). Our techniques surprisingly allow us to get to general q-ary CSPs without negations by appealing to the same Fourier analytic starting point over Boolean hypercubes.