Explicit Optimal Hardness via Gaussian stability results
Anindya De, Elchanan Mossel

TL;DR
This paper derives explicit optimal approximation algorithms for certain constraint satisfaction problems using Gaussian stability results, confirming conjectured approximation factors and establishing new hardness bounds under the Unique Games Conjecture.
Contribution
It introduces a new approach leveraging Gaussian partition results to explicitly determine optimal approximation algorithms and hardness factors for MAX-3-EQUAL and MAX-k-CSP problems.
Findings
Achieved the first explicit optimal approximation algorithm for MAX-3-EQUAL.
Confirmed the conjectured approximation factor of approximately 0.796 for Zwick's algorithm.
Established a new UGC-based hardness factor for MAX-k-CSP as loor((k+1)/2)rac{1}{2^{k-1}}.
Abstract
The results of Raghavendra (2008) show that assuming Khot's Unique Games Conjecture (2002), for every constraint satisfaction problem there exists a generic semi-definite program that achieves the optimal approximation factor. This result is existential as it does not provide an explicit optimal rounding procedure nor does it allow to calculate exactly the Unique Games hardness of the problem. Obtaining an explicit optimal approximation scheme and the corresponding approximation factor is a difficult challenge for each specific approximation problem. An approach for determining the exact approximation factor and the corresponding optimal rounding was established in the analysis of MAX-CUT (KKMO 2004) and the use of the Invariance Principle (MOO 2005). However, this approach crucially relies on results explicitly proving optimal partitions in Gaussian space. Until recently, Borell's…
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Taxonomy
TopicsMachine Learning and Algorithms · Complexity and Algorithms in Graphs · Algorithms and Data Compression
