Clustering phase of a general constraint satisfaction problem model d-k-CSP

Wei Xu, Fuzhou Gong and Guangyan Zhou

Physica A: Statistical Mechanics and its Applications, 2020, vol. 537, issue C

Abstract: Relation between problem hardness and solution space structure is an important research aspect. Model d-k-CSP is a random model of constraint satisfaction problem, which generates very hard instances when r=1 or r is near 1, where r represents normalized constraint density. We study the clustering phase of the model, and find that if r is below and close to 1, the solution space contains many widely distributed and well-separated small frozen clusters, which should be the reason why the generated instances are hard to solve.

Keywords: Constraint satisfaction problem; Solution space structure; Clustering phase transition; Problem hardness; Belief propagation (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:537:y:2020:i:c:s0378437119315420

DOI: 10.1016/j.physa.2019.122708

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