Inference algorithms based on evolving interactions between replicated solutions are introduced and analyzed on a prototypical NP-hard problem - the capacity of the binary Ising perceptron. The efficiency of the algorithm is examined numerically against that of the parallel tempering algorithm, showing improved performance in terms of the results obtained, computing requirements and simplicity of implementation.
@article{arxiv.1305.2754,
title = {Replication-based Inference Algorithms for Hard Computational Problems},
author = {Roberto C. Alamino and Juan P. Neirotti and David Saad},
journal= {arXiv preprint arXiv:1305.2754},
year = {2015}
}