A New Regularity Lemma and Faster Approximation Algorithms for Low Threshold Rank Graphs

TL;DR

This paper introduces a new regularity lemma for low threshold-rank graphs, enabling faster approximation algorithms for problems like Max Cut, surpassing previous methods in efficiency and simplicity.

Contribution

The paper presents a novel regularity lemma for graphs with bounded threshold-rank, leading to improved approximation algorithms that are faster and easier to analyze.

Findings

Developed a weak Szemeredi regularity lemma for low threshold-rank graphs.

Designed a faster Max Cut approximation algorithm based on the new regularity lemma.

Achieved simpler analysis and implementation compared to previous algorithms.

Abstract

Kolla and Tulsiani [KT07,Kolla11} and Arora, Barak and Steurer [ABS10] introduced the technique of subspace enumeration, which gives approximation algorithms for graph problems such as unique games and small set expansion; the running time of such algorithms is exponential in the threshold-rank of the graph. Guruswami and Sinop [GS11,GS12], and Barak, Raghavendra, and Steurer [BRS11] developed an alternative approach to the design of approximation algorithms for graphs of bounded threshold-rank, based on semidefinite programming relaxations in the Lassere hierarchy and on novel rounding techniques. These algorithms are faster than the ones based on subspace enumeration and work on a broad class of problems. In this paper we develop a third approach to the design of such algorithms. We show, constructively, that graphs of bounded threshold-rank satisfy a weak Szemeredi regularity lemma analogous to the one proved by Frieze and Kannan [FK99] for dense graphs. The existence of efficient approximation algorithms is then a consequence of the regularity lemma, as shown by Frieze and Kannan. Applying our method to the Max Cut problem, we devise an algorithm that is faster than all previous algorithms, and is easier to describe and analyze.