A Characterization of Approximability for Biased CSPs

TL;DR

This paper characterizes how the bias parameter affects the approximability of biased CSPs, providing tight bounds and new algorithms for problems like Densest-k-Subhypergraph, especially in the linear bias regime.

Contribution

It offers a tight characterization of biased CSP approximability via the bias-approximation curve of Densest-k-SubHypergraph and establishes new bounds and algorithms under the Small Set Expansion Hypothesis.

Findings

Tight approximation bounds for Densest-k-Subhypergraph in the linear bias regime.

NP-hardness of approximation for DkSH with bias parameter under SSEH.

A new approximation algorithm matching the hardness bounds for biased CSPs.

Abstract

A $\mu$-biased Max-CSP instance with predicate $\psi:\{0,1\}^r \to \{0,1\}$ is an instance of Constraint Satisfaction Problem (CSP) where the objective is to find a labeling of relative weight at most $\mu$ which satisfies the maximum fraction of constraints. Biased CSPs are versatile and express several well studied problems such as Densest-$k$-Sub(Hyper)graph and SmallSetExpansion. In this work, we explore the role played by the bias parameter $\mu$ on the approximability of biased CSPs. We show that the approximability of such CSPs can be characterized (up to loss of factors of arity $r$) using the bias-approximation curve of Densest-$k$-SubHypergraph (DkSH). In particular, this gives a tight characterization of predicates which admit approximation guarantees that are independent of the bias parameter $\mu$. Motivated by the above, we give new approximation and hardness results for DkSH. In particular, assuming the Small Set Expansion Hypothesis (SSEH), we show that DkSH with arity $r$ and $k = \mu n$ is NP-hard to approximate to a factor of $\Omega(r^3\mu^{r-1}\log(1/\mu))$ for every $r \geq 2$ and $\mu < 2^{-r}$. We also give a $O(\mu^{r-1}\log(1/\mu))$-approximation algorithm for the same setting. Our upper and lower bounds are tight up to constant factors, when the arity $r$ is a constant, and in particular, imply the first tight approximation bounds for the Densest-$k$-Subgraph problem in the linear bias regime. Furthermore, using the above characterization, our results also imply matching algorithms and hardness for every biased CSP of constant arity.