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• W2884961996 abstract "The MaxClique problem, finding the largest complete subgraph in an Erd{o}s-R{e}nyi $G(N,p)$ random graph in the large $N$ limit, is a well-known example of a simple problem for which finding any approximate solution within a factor of $2$ of the known, probabilistically determined limit, appears to require P$=$NP. This type of search has practical importance in very large graphs. Algorithmic approaches run into phase boundaries long before they reach the size of the largest likely solutions. And, most intriguing, there is an extensive literature of textit{challenges} posed for concrete methods of finding maximum naturally occurring as well as artificially hidden cliques, with computational costs that are at most polynomial in the size of the problem. We use the probabilistic approach in a novel way to provide a more insightful test of constructive algorithms for this problem. We show that extensions of existing methods of greedy local search will be able to meet the textit{challenges} for practical problems of size $N$ as large as $10^{10}$ and perhaps more. Experiments with spectral methods that treat a single large clique of size $alpha N^{1/2}$ textit{planted} in the graph as an impurity level in a tight binding energy band show that such a clique can be detected when $alpha geq approx1.0$. Belief propagation using a recent textit{approximate message passing} (textbf{AMP}) scheme of inference pushes this limit down to $alpha sim sqrt{1/e}$. Exhaustive local search (with early stopping when the planted clique is found) does even better on problems of practical size, and proves to be the fastest solution method for this problem." @default.
• W2884961996 created "2018-08-03" @default.
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• W2884961996 date "2018-07-24" @default.
• W2884961996 modified "2023-10-06" @default.
• W2884961996 title "Revisiting the Challenges of MaxClique." @default.
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