Partial Optimality in Cubic Correlation Clustering

• URL: http://arxiv.org/abs/2302.04694v2
• Date: Fri, 31 Mar 2023 14:50:55 GMT
• Title: Partial Optimality in Cubic Correlation Clustering
• Authors: David Stein, Silvia Di Gregorio, Bjoern Andres
• Abstract summary: Certifying optimality is NP-hard and practically hampered already by the complexity of the problem statement. Here, we focus on establishing partial optimality conditions for the special case of complete graphs and cubic objective functions. In addition, we define and implement algorithms for testing these conditions and examine their effect numerically, on two datasets.
• Score: 6.372499127940625
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The higher-order correlation clustering problem is an expressive model, and recently, local search heuristics have been proposed for several applications. Certifying optimality, however, is NP-hard and practically hampered already by the complexity of the problem statement. Here, we focus on establishing partial optimality conditions for the special case of complete graphs and cubic objective functions. In addition, we define and implement algorithms for testing these conditions and examine their effect numerically, on two datasets.

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