Finding Diverse Solutions Parameterized by Cliquewidth

Karolina Drabik, Tomáš Masařík
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Abstract

Finding a few solutions for a given problem that are diverse, as opposed to finding a single best solution to solve the problem, has recently become a notable topic in theoretical computer science. Recently, Baste, Fellows, Jaffke, Masa\v{r}\'ik, Oliveira, Philip, and Rosamond showed that under a standard structural parameterization by treewidth, one can find a set of diverse solutions for many problems with only a very small additional cost [Artificial Intelligence 2022]. In this paper, we investigate a much stronger graph parameter, the cliquewidth, which can additionally describe some dense graph classes. Broadly speaking, it describes graphs that can be recursively constructed by a few operations defined on graphs whose vertices are divided into a bounded number of groups while each such group behaves uniformly with respect to any operation. We show that for any vertex problem, if we are given a dynamic program solving that problem on cliquewidth decomposition, we can modify it to produce a few solutions that are as diverse as possible with as little overhead as in the above-mentioned treewidth paper. As a consequence, we prove that a diverse version of any MSO$_1$ expressible problem can be solved in FPT time parameterized by cliquewidth, the number of sought solutions, and the number of quantifiers in the formula. That was an important missing piece in the complexity landscape of structural graph parameters and logic. We prove our results allowing for a more general natural collection of diversity functions compared to only two mostly studied diversity functions previously. That might be of independent interest as a larger pool of different diversity functions can highlight various aspects of different solutions to a problem.
寻找以链宽为参数的多样化解决方案
相对于寻找单一最佳解来解决问题,为一个给定问题寻找几个不同的解最近成为了理论计算机科学中一个值得关注的话题。最近,Baste、Fellows、Jaffke、Masa\v{r}\'ik、Oliveira、Philip 和 Rosamond 发现,在以树宽为标准的结构参数化条件下,人们只需付出很小的额外代价,就能为许多问题找到一组多样化的解决方案[Artificial Intelligence 2022]。在本文中,我们研究了一个更强的图参数--剪枝带宽,它可以额外描述一些密集图类。广义上讲,它描述的图可以通过定义在图上的一些操作递归构造,这些图的顶点被分成一定数量的组,而每个这样的组相对于任何操作都表现一致。我们证明,对于任何顶点问题,如果给我们一个动态程序来解决该问题的剪辑宽度分解,我们可以修改它,以产生一些尽可能多样化的解,其开销与上述树宽论文中的开销一样小。因此,我们证明了任何 MSO$_1$ 可表达问题的多样化版本都能在 FPT 时间内求解,而 FPT 时间的参数则是剪辑宽度、所求解的数量以及公式中的量词数量。这是结构图参数和逻辑复杂性研究中缺失的重要一环。我们证明了我们的结果,这使得多样性函数的自然集合更加普遍,而之前主要研究的多样性函数只有两种。这可能具有独立的意义,因为更多不同的多样性函数可以突出问题不同解决方案的各个方面。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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