Dynamic Greedy Algorithms for the Edwards-Anderson Model
Stefan Schnabel, Wolfhard Janke
TL;DR
This paper introduces a dynamic greedy algorithm for the Edwards-Anderson spin-glass model that leverages previous optimization data to enhance the efficiency of higher-order greedy searches and simulated annealing.
Contribution
The paper presents a novel dynamic greedy algorithm that improves optimization efficiency by utilizing data from prior similar configurations in the Edwards-Anderson model.
Findings
Enhanced performance in higher-order greedy optimizations
Improved efficiency in simulated annealing searches
Effective use of previous optimization data
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.
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