Enumerating Parametric Global Minimum Cuts By Random Interleaving
STOC(2016)
摘要
Recently, Aissi et al. gave new counting and algorithmic bounds for parametric minimum cuts in a graph, where each edge cost is a linear combination of multiple cost criteria and different cuts become minimum as the coefficients of the linear combination are varied. In this article, we derive better bounds using a mathematically simpler argument. We provide faster algorithms for enumerating these cuts. We give a lower bound showing our upper bounds have roughly the right form. Our results also immediately generalize to parametric versions of other problems solved by the Contraction Algorithm, including approximate min-cuts, multi-way cuts, and a matroid optimization problem. We also give a first generalization to nonlinear parametric minimum cuts.
更多查看译文
关键词
Minimum cuts,Randomization,Parametric optimization,Enumeration
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络