
TL;DR
This paper introduces a robust adaptive sampling algorithm for combinatorial optimization that improves the discovery of good local optima, outperforming existing methods in tasks like clique detection and clustering.
Contribution
The paper presents a novel learning algorithm that adapts sampling distributions to find high-quality local optima across various combinatorial problems.
Findings
Adaptive sampler outperforms related sampling methods.
Approaches the performance of established clique algorithms.
Enhances greedy algorithms for k-medoid clustering.
Abstract
We study the task of finding good local optima in combinatorial optimization problems. Although combinatorial optimization is NP-hard in general, locally optimal solutions are frequently used in practice. Local search methods however typically converge to a limited set of optima that depend on their initialization. Sampling methods on the other hand can access any valid solution, and thus can be used either directly or alongside methods of the former type as a way for finding good local optima. Since the effectiveness of this strategy depends on the sampling distribution, we derive a robust learning algorithm that adapts sampling distributions towards good local optima of arbitrary objective functions. As a first use case, we empirically study the efficiency in which sampling methods can recover locally maximal cliques in undirected graphs. Not only do we show how our adaptive sampler…
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Taxonomy
TopicsAdvanced Clustering Algorithms Research · Bayesian Methods and Mixture Models · Data-Driven Disease Surveillance
