Near-Optimal UGC-hardness of Approximating Max k-CSP_R
Pasin Manurangsi, Preetum Nakkiran, Luca Trevisan

TL;DR
This paper establishes near-optimal hardness of approximating Max k-CSP_R under UGC, improving previous bounds and providing matching algorithms, thus advancing understanding of the problem's computational complexity.
Contribution
It proves a nearly tight UGC-based hardness result for Max k-CSP_R and extends approximation algorithms, significantly narrowing the gap between hardness and achievable solutions.
Findings
Hardness of approximation factor: 2^{O(k log k)}(log R)^{k/2}/R^{k-1}
Approximation algorithm with ratio: Ω(log R/R^{k-1} )
Hardness results hold under both UGC and Khot's d-to-1 Conjecture
Abstract
In this paper, we prove an almost-optimal hardness for Max -CSP based on Khot's Unique Games Conjecture (UGC). In Max -CSP, we are given a set of predicates each of which depends on exactly variables. Each variable can take any value from . The goal is to find an assignment to variables that maximizes the number of satisfied predicates. Assuming the Unique Games Conjecture, we show that it is NP-hard to approximate Max -CSP to within factor for any . To the best of our knowledge, this result improves on all the known hardness of approximation results when . In this case, the previous best hardness result was NP-hardness of approximating within a factor by Chan. When , our result matches the best known UGC-hardness result of Khot, Kindler,…
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Taxonomy
TopicsComplexity and Algorithms in Graphs · Cryptography and Data Security · Computational Geometry and Mesh Generation
