Dynamic greedy algorithms for the Edwards–Anderson model

Abstract

To provide a novel tool for the investigation of the energy landscape of the Edwards–Anderson spin-glass model we introduce an algorithm that allows an efficient execution of a greedy optimization based on data from a previously performed optimization for a similar configuration. As an application we show how the technique can be used to perform higher-order greedy optimizations and simulated annealing searches with improved performance.