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Title: Simultaneous Max-Cut Is Harder to Approximate Than Max-Cut
A systematic study of simultaneous optimization of constraint satisfaction problems was initiated by Bhangale et al. [ICALP, 2015]. The simplest such problem is the simultaneous Max-Cut. Bhangale et al. [SODA, 2018] gave a .878-minimum approximation algorithm for simultaneous Max-Cut which is almost optimal assuming the Unique Games Conjecture (UGC). For single instance Max-Cut, Goemans-Williamson [JACM, 1995] gave an α_GW-approximation algorithm where α_GW ≈ .87856720... which is optimal assuming the UGC. It was left open whether one can achieve an α_GW-minimum approximation algorithm for simultaneous Max-Cut. We answer the question by showing that there exists an absolute constant ε₀ ≥ 10^{-5} such that it is NP-hard to get an (α_GW- ε₀)-minimum approximation for simultaneous Max-Cut assuming the Unique Games Conjecture.  more » « less
Award ID(s):
1813438
NSF-PAR ID:
10181527
Author(s) / Creator(s):
;
Date Published:
Journal Name:
35th Computational Complexity Conference (CCC 2020)
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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