Abstract

In the context of solving large distributed constraint optimization problems, belief-propagation and incomplete inference algorithms are candidates of choice. However, in general, when the problem structure is very cyclic, these solution methods suffer from bad performance, due to non-convergence and many exchanged messages. As to improve performances of the MaxSum inference algorithm when solving cyclic constraint optimization problems, we propose here to take inspiration from the belief-propagation-guided decimation used to solve sparse random graphs ($k$-satisfiability). We propose the novel DeciMaxSum method, which is parameterized in terms of policies to decide when to trigger decimation, which variables to decimate and which values to assign to decimated variables. Based on an empirical evaluation on a classical constraint optimization benchmarks (graph coloring, random graph and Ising model), some of these combinations of policies, using periodic decimation, cycle detection-based decimation, parallel and non parallel decimation, random or deterministic variable selection and deterministic or random sampling for value selection, outperform state-of-the-art competitors in many settings.

中文翻译：

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在不破坏银行的情况下解决高循环分布优化问题：一种基于抽取的方法

抽象的

在解决大型分布式约束优化问题的情况下，置信传播和不完整推理算法是首选。但是，通常，当问题结构非常循环时，由于不收敛和交换了很多消息，因此这些解决方法的性能很差。为了在解决循环约束优化问题时提高MaxSum推理算法的性能，我们在此建议从用于解决稀疏随机图（$ k $-可满足性）的置信传播引导抽取中获得启发。我们提出小说DeciMaxSum方法，可以根据政策确定参数，以决定何时触发抽取，抽取哪些变量以及将哪些值分配给抽取变量。基于对经典约束优化基准（图形着色，随机图和Ising模型）的经验评估，其中一些策略组合使用定期抽取，基于周期检测的抽取，并行和非并行抽取，随机或确定性变量选择以及用于价值选择的确定性或随机抽样，在许多情况下都胜过了最新的竞争对手。