Recent progress on robust clustering led to constant-factor approximations for Robust Matroid Center. After a first combinatorial $7$-approximation that is based on a matroid intersection approach, two tight LP-based $3$-approximations were discovered, both relying on the Ellipsoid Method. In this paper, we show how a carefully designed, yet very simple, greedy selection algorithm gives a $5$-approximation. An important ingredient of our approach is a well-chosen use of Rado matroids. This enables us to capture with a single matroid a relaxed version of the original matroid, which, as we show, is amenable to straightforward greedy selections.
翻译:稳健的集群最近的进展导致强力机器人中心(Robust Materroid Center)的不变因素近似值。在第一次组合式的7美元接近率基于机器人交叉法之后,发现了两种紧凑的LP基价为3美元的近似值,这两种方法都依赖 Ellipsoby 方法。在本文中,我们展示了精心设计但非常简单、贪婪的选择算法是如何提供5美元接近率的。我们方法的一个重要部分是精心选择使用Rodo Maidroids。这使我们能够用一个单机类模型来捕捉一个轻松的原生型机器人,正如我们所显示的那样,它很容易被直截了当的贪婪选择。