Strongly refuting all semi-random Boolean CSPs

TL;DR

This paper presents an efficient algorithm for strongly refuting semi-random Boolean CSP instances, matching the best bounds for fully random cases, and introduces novel spectral and SDP techniques for analysis.

Contribution

The paper introduces a new spectral refutation algorithm for semi-random Boolean CSPs, especially for the $k$-XOR problem, with improved analysis techniques.

Findings

Algorithm matches bounds for fully random instances

Spectral methods effectively analyze semi-random XOR instances

Shorter, more general proof techniques using matrix Bernstein inequality

Abstract

We give an efficient algorithm to strongly refute \emph{semi-random} instances of all Boolean constraint satisfaction problems. The number of constraints required by our algorithm matches (up to polylogarithmic factors) the best-known bounds for efficient refutation of fully random instances. Our main technical contribution is an algorithm to strongly refute semi-random instances of the Boolean $k$-XOR problem on $n$ variables that have $\widetilde{O}(n^{k/2})$ constraints. (In a semi-random $k$-XOR instance, the equations can be arbitrary and only the right-hand sides are random.) One of our key insights is to identify a simple combinatorial property of random XOR instances that makes spectral refutation work. Our approach involves taking an instance that does not satisfy this property (i.e., is \emph{not} pseudorandom) and reducing it to a partitioned collection of $2$-XOR instances. We analyze these subinstances using a carefully chosen quadratic form as a proxy, which in turn is bounded via a combination of spectral methods and semidefinite programming. The analysis of our spectral bounds relies only on an off-the-shelf matrix Bernstein inequality. Even for the purely random case, this leads to a shorter proof compared to the ones in the literature that rely on problem-specific trace-moment computations.