一种适用于所有不同约束的按位GAC算法

Z. Li, Yao-Ming Wang, Zhanshan Li
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引用次数: 0

摘要

广义弧一致性(GAC)算法是求解各种约束问题的主流算法。针对所有不同约束条件的GAC的核心部分是挖掘和枚举图模型的所有强连接组件(scc)。这将导致大量复杂的数据结构来维护节点信息,从而导致大量的时间和内存空间开销。更为关键的是,数据结构的复杂性进一步阻碍了GAC不同优化方案的协调。为了解决这个问题,本文的关键观察是GAC算法只关心图模型的一个节点是否在一个SCC中,而不关心它属于哪个SCC。基于这种观察,我们提出了AllDiffbit,它采用位数据结构和操作来有效地确定节点是否在SCC中。这大大减少了相应的开销,并增强了合并现有优化以协同方式工作的能力。我们的实验表明,AllDiffbit优于最先进的GAC算法60%以上。
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A Bitwise GAC Algorithm for Alldifferent Constraints
The generalized arc consistency (GAC) algorithm is the prevailing solution for alldifferent constraint problems. The core part of GAC for alldifferent constraints is excavating and enumerating all the strongly connected components (SCCs) of the graph model. This causes a large amount of complex data structures to maintain the node information, leading to a large overhead both in time and memory space. More critically, the complexity of the data structures further precludes the coordination of different optimization schemes for GAC. To solve this problem, the key observation of this paper is that the GAC algorithm only cares whether a node of the graph model is in an SCC or not, rather than which SCCs it belongs to. Based on this observation, we propose AllDiffbit, which employs bitwise data structures and operations to efficiently determine if a node is in an SCC. This greatly reduces the corresponding overhead, and enhances the ability to incorporate existing optimizations to work in a synergistic way. Our experiments show that AllDiffbit outperforms the state-of-the-art GAC algorithms over 60%.
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