In the field of parameterized complexity theory, the study of graph width measures has been intimately connected with the development of width-based model checking algorithms for combinatorial properties on graphs. In this work, we introduce a general framework to convert a large class of width-based model-checking algorithms into algorithms that can be used to test the validity of graph-theoretic conjectures on classes of graphs of bounded width. Our framework is modular and can be applied with respect to several well-studied width measures for graphs, including treewidth and cliquewidth. As a quantitative application of our framework, we show that for several long-standing graph-theoretic conjectures, there exists an algorithm that takes a number $k$ as input and correctly determines in time double-exponential in $k^{O(1)}$ whether the conjecture is valid on all graphs of treewidth at most $k$. This improves significantly on upper bounds obtained using previously available techniques.
翻译:在参数化复杂度理论领域,图形宽度测量的研究与图表组合属性宽度模型检查算法的开发密切相关。 在这项工作中,我们引入了一个总框架,将大量基于宽度模型检查算法转换成算法,用于测试相宽度图类图的图形理论预测的有效性。我们的框架是模块化的,可以应用于一些经过仔细研究的图表宽度测量法,包括树宽度和细微度。作为我们框架的量化应用,我们显示,对于一些长期存在的图形理论预测,我们有一个总框架,将大量基于宽度的模型检查算法作为输入,并在时间上正确确定以$k ⁇ (1)美元计算的图形双扩展值值值。我们的框架是模块化的,可以适用于若干图表的宽度测算法,包括树宽度和细微宽度。对于使用以前掌握的技术获得的上层线,这大大改善了。