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Branch-Cut-and-Propagate for the Maximum k-Colorable Subgraph Problem with Symmetry

机译:具有对称性的最大k可着色子图问题的分支剪切传播

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Given an undirected graph and a positive integer k, the maximum k-colorable subgraph problem consists of selecting a k-colorable induced subgraph of maximum cardinality. The natural integer programming formulation for this problem exhibits two kinds of symmetry: arbitrarily permuting the color classes and/or applying a non-trivial graph automorphism gives equivalent solutions. It is well known that such symmetries have negative effects on the performance of constraint/integer programming solvers.We investigate the integration of a branch-and-cut algorithm for solving the maximum k-colorable subgraph problem with constraint propagation techniques to handle the symmetry arising from the graph. The latter symmetry is handled by (non-linear) lexicographic ordering constraints and linearizations thereof. In experiments, we evaluate the influence of several components of our algorithm on the performance, including the different symmetry handling methods. We show that several components are crucial for an efficient algorithm; in particular, the handling of graph symmetries yields a significant performance speed-up.
机译:给定无向图和正整数k,最大k可着色子图问题包括选择最大基数的k可着色诱导子图。用于此问题的自然整数编程公式具有两种对称性:任意排列颜色类别和/或应用非平凡的图自同构可得出等效的解决方案。众所周知,这种对称性对约束/整数规划求解器的性能有负面影响。我们研究了使用分支传播算法来解决最大k可着色子图问题与约束传播技术的集成,以处理出现的对称性从图中。后者的对称性通过(非线性)词典顺序约束及其线性化处理。在实验中,我们评估了算法的几个组件对性能的影响,包括不同的对称处理方法。我们展示了几个组件对于有效算法至关重要。特别是,图形对称性的处理可显着提高性能。

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