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A short review on the maximum clique problem algorithms with classical, AI, and quantum methods
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A short review on the maximum clique problem algorithms with classical, AI, and quantum methods
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This manuscript provides a comprehensive review of the Maximum Clique Problem, a computational problem that involves finding subsets of vertices in a graph that are all pairwise adjacent to each other. As such, this review is a continuation of the series of previous reviews from 1994, 1999 and 2014. The manuscript covers in a simple way classical algorithms and includes a review of recent developments in graph neural networks and quantum algorithms.
Forward citations
Cited by 2 Pith papers
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Rounding the Lov\'asz Theta Function with a Value Function Approximation
A new single-SDP rounding method for Lovász theta that provably recovers maximum weighted stable sets in generalized split graphs and other perfect graph subclasses via value function approximation and dynamic programming.
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Displaced Gaussian Boson Sampling for enhanced max-clique search
Displaced GBS enhances max-clique search success under loss or low squeezing and scales to large graphs with modest overhead.
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