Maximum Quadratic Assignment Problem: Reduction from Maximum Label Cover and LP-based Approximation Algorithm

Konstantin Makarychev; Rajsekar Manokaran; Maxim Sviridenko

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arXiv · 1403.7721

Maximum Quadratic Assignment Problem: Reduction from Maximum Label Cover and LP-based Approximation Algorithm

Konstantin Makarychev·Rajsekar Manokaran·Maxim Sviridenko

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Abstract

We show that for every positive $ε> 0$, unless NP $\subset$ BPQP, it is impossible to approximate the maximum quadratic assignment problem within a factor better than $2^{\log^{1-ε} n}$ by a reduction from the maximum label cover problem. Our result also implies that Approximate Graph Isomorphism is not robust and is in fact, $1 - ε$ vs $ε$ hard assuming the Unique Games Conjecture. Then, we present an $O(\sqrt{n})$-approximation algorithm for the problem based on rounding of the linear programming relaxation often used in the state of the art exact algorithms.

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Konstantin Makarychev, Rajsekar Manokaran, Maxim Sviridenko. 2014-03-30. Maximum Quadratic Assignment Problem: Reduction from Maximum Label Cover and LP-based Approximation Algorithm. https://arxiv.org/abs/1403.7721

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