Making the long code shorter, with applications to the Unique Games Conjecture

TL;DR

This paper introduces a new, more efficient long code construction that enhances the analysis of the Unique Games Conjecture, leading to exponential improvements in graph expansion, reduction gadgets, and integrality gaps.

Contribution

The authors develop an exponentially more efficient long code, enabling significant advancements in hardness of approximation results related to the Unique Games Conjecture.

Findings

Constructed a graph with high eigenvalues and small set expansion.

Created a gadget reducing K-ary unique games with polynomial blowup.

Established an improved integrality gap for Unique Games that withstands many SDP rounds.

Abstract

The long code is a central tool in hardness of approximation, especially in questions related to the unique games conjecture. We construct a new code that is exponentially more efficient, but can still be used in many of these applications. Using the new code we obtain exponential improvements over several known results, including the following: 1. For any eps > 0, we show the existence of an n vertex graph G where every set of o(n) vertices has expansion 1 - eps, but G's adjacency matrix has more than exp(log^delta n) eigenvalues larger than 1 - eps, where delta depends only on eps. This answers an open question of Arora, Barak and Steurer (FOCS 2010) who asked whether one can improve over the noise graph on the Boolean hypercube that has poly(log n) such eigenvalues. 2. A gadget that reduces unique games instances with linear constraints modulo K into instances with alphabet k with a blowup of K^polylog(K), improving over the previously known gadget with blowup of 2^K. 3. An n variable integrality gap for Unique Games that that survives exp(poly(log log n)) rounds of the SDP + Sherali Adams hierarchy, improving on the previously known bound of poly(log log n). We show a connection between the local testability of linear codes and small set expansion in certain related Cayley graphs, and use this connection to derandomize the noise graph on the Boolean hypercube.