约束尺寸图问题的多参数复杂性分析：使用贪婪参数化
Multi-parameter complexity analysis for constrained size graph problems: using greediness for parameterization
摘要：
本文研究了一类被称为“局部图划分问题”的问题的参数复杂性，这类问题包括经典的固定基数问题，如最大k-顶点覆盖、k-可确定子图等。通过发展一种“参数化的贪婪”技术，我们得到了关于一对参数k（解的大小）的固定参数算法。（但不是它的值）和Δ，输入图的最大度。特别是，参数化的贪婪改进了随机分离（这是颜色编码的特殊情况）时这些问题的渐近运行时间，并且更直观和简单。然后，我们展示了这些结果如何可以很容易地扩展为获得标准参数化结果（即，参数为最优解的值）。
英文摘要：
We study the parameterized complexity of a broad class of problems called "local graph partitioning problems" that includes the classical fixed cardinality problems as max k-vertex cover, k-densest subgraph, etc. By developing a technique "greediness-for-parameterization", we obtain fixed parameter algorithms with respect to a pair of parameters k, the size of the solution (but not its value) and Δ, the maximum degree of the input graph. In particular, greediness-for-parameterization improves asymptotic running times for these problems upon random separation (that is a special case of color coding) and is more intuitive and simple. Then, we show how these results can be easily extended for getting standard-parameterization results (i.e., with parameter the value of the optimal solution) for a well known local graph partitioning problem.
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