Almost-Linear Planted Cliques Elude the Metropolis Process

Abstract

A seminal work of Jerrum (1992) showed that large cliques elude the Metropolis process. More specifically, Jerrum showed that the Metropolis algorithm cannot find a clique of size k = Θ(nα) for α ∈ (0,1/2), which is planted in the Erdős-Rényi random graph G(n, 1/2), in polynomial time. Information theoretically it is possible to find such planted cliques as soon as k ≥ (2 + ε) log n. Since the work of Jerrum, the computational problem of finding a planted clique in G(n, 1/2) was studied extensively and many polynomial time algorithms were shown to find the planted clique if it is of size , while no polynomial-time algorithm is known to work when . The computational problem of finding a planted clique of size is now widely considered as a foundational problem in the study of computational-statistical gaps. Notably, the first evidence of the problem's algorithmic hardness is commonly attributed to the result of Jerrum from 1992.In this paper we revisit the original Metropolis algorithm suggested by Jerrum. Interestingly, we find that the Metropolis algorithm actually fails to recover a planted clique of size k = Θ(nα) for any constant α ∈ (0,1), unlike many other efficient algorithms that succeed when α > 1/2. Moreover, we strengthen Jerrum's results in a number of other ways including:• Like many results in the MCMC literature, the result of Jerrum shows that there exists a starting state (which may depend on the instance) for which the Metropolis algorithm fails to find the planted clique in polynomial time. For a wide range of temperatures, we show that the algorithm fails when started at the most natural initial state, which is the empty clique. This answers an open problem stated in Jerrum (1992). We highlight that it is rather rare to be able to show the failure of a Markov chain starting from a specific state, which is arguably an important missing piece in the Markov chain literature.• We show that the simulated tempering version of the Metropolis algorithm, a more sophisticated temperature-exchange variant of it, also fails at the same regime of parameters.Our results substantially extend Jerrum's result. Furthermore, they confirm recent predictions by Gamarnik and Zadik (2019) and Angelini, Fachin, de Feo (2021).* The full version of the paper can be accessed at https://arxiv.org/abs/2204.01911