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Parameterized Complexity of Deletion to Scattered Graph Classes

机译:删除分散图形类的参数化复杂性

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Graph-modification problems, where we add/delete a small number of vertices/edges to make the given graph to belong to a simpler graph class, is a well-studied optimization problem in all algorithmic paradigms including classical, approximation and parameterized complexity. Specifically, graph-deletion problems, where one needs to delete at most k vertices to place it in a given non-trivial hereditary (closed under induced subgraphs) graph class, captures several well-studied problems including Vertex Cover, Feedback Vertex Set, Odd Cycle Transveral, Cluster Vertex Deletion, and Perfect Deletion. Investigation into these problems in parameterized complexity has given rise to powerful tools and techniques. While a precise characterization of the graph classes for which the problem is fixed-parameter tractable (FPT) is elusive, it has long been known that if the graph class is characterized by a finite set of forbidden graphs, then the problem is FPT. In this paper, we initiate a study of a natural variation of the problem of deletion to scattered graph classes where we need to delete at most k vertices so that in the resulting graph, each connected component belongs to one of a constant number of graph classes. A simple hitting set based approach is no longer feasible even if each of the graph classes is characterized by finite forbidden sets. As our main result, we show that this problem (in the case where each graph class has a finite forbidden set) is fixed-parameter tractable by a O^*(2^(k^O(1))) algorithm, using a combination of the well-known techniques in parameterized complexity - iterative compression and important separators. Our approach follows closely that of a related problem in the context of satisfiability [Ganian, Ramanujan, Szeider, TAlg 2017], where one wants to find a small backdoor set so that the resulting CSP (constraint satisfaction problem) instance belongs to one of several easy instances of satisfiability. While we follow the main idea from this work, there are some challenges for our problem which we needed to overcome. When there are two graph classes with finite forbidden sets to get to, and if one of the forbidden sets has a path, then we show that the problem has a (better) singly exponential algorithm and a polynomial sized kernel. We also design an efficient FPT algorithm for a special case when one of the graph classes has an infinite forbidden set. Specifically, we give a O^*(4^k) algorithm to determine whether k vertices can be deleted from a given graph so that in the resulting graph, each connected component is a tree (the sparsest connected graph) or a clique (the densest connected graph).
机译:图形修改问题,其中我们添加/删除少量顶点/边,以使给定图属于更简单的图形类,是所有算法范例中的良好优化问题,包括经典,近似和参数化复杂性。具体而言,图形删除问题,其中需要在大多数k顶点删除,将其放置在给定的非琐碎遗传(关闭在诱导的子图下)图表类中,捕获了几个研究的问题,包括顶点封面,反馈顶点集,奇数循环传输,群集顶点删除和完美删除。参数化复杂性的这些问题调查已经引起了强大的工具和技术。虽然问题是固定参数的图表类的图表类是难以捉摸的,但是已经难以知道,如果图表类的特征在于有限的禁止图,则问题是FPT。在本文中,我们开始研究删除问题的删除问题的自然变化,我们需要在最多k顶点上删除,以便在得到的图形中,每个连接的组件都属于恒定数量的图形类之一。即使每个图形类别以有限的禁止集为特征,基于基于组的基于组的方法也不再可行。作为我们的主要结果,我们展示了这个问题(在每个图形类具有有限禁止集的情况下)是由o ^ *(2 ^(k ^ o(1)))算法的固定参数,使用a参数化复杂性 - 迭代压缩和重要分离器中的众所周知技术的组合。我们的方法在满足性的背景下的相关问题紧密地遵循了相关问题[Ganian,Ramanujan,Szeider,Talg 2017],其中一个人想要找到一个小的后门集,以便产生的CSP(约束满足问题)实例属于几个简单的可靠性实例。虽然我们遵循这项工作的主要观点,但我们需要克服的问题存在一些挑战。当有两个图形类具有有限的禁止集时到达,如果其中一个禁止集有路径,那么我们表明问题有一个(更好)单指数算法和多项式大小的内核。我们还为特殊情况设计了一个高效的FPT算法,其中一个图表类具有无限禁止集。具体地,我们提供O ^ *(4 ^ k)算法来确定是否可以从给定图删除k顶点,使得在得到的图表中,每个连接的组件是树(稀疏连接图)或clique(最浓度的连接图形)。

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