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Maximizing a Submodular Function with Viability Constraints

机译:最大化带有活力约束的子模具功能

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We study the problem of maximizing a monotone submodular function with viability constraints. This problem originates from computational biology, where we are given a phylogenetic tree over a set of species and a directed graph, the so-called food web, encoding viability constraints between these species. These food webs usually have constant depth. The goal is to select a subset of k species that satisfies the viability constraints and has maximal phylogenetic diversity. As this problem is known to be NP-hard, we investigate approximation algorithm. We present the first constant factor approximation algorithm if the depth is constant. Its approximation ratio is (1? 1/~(1/2)e). This algorithm not only applies to phylogenetic trees with viability constraints but for arbitrary monotone submodular set functions with viability constraints. Second, we show that there is no (1? 1/e + ?)-approximation algorithm for our problem setting (even for additive functions) and that there is no approximation algorithm for a slight extension of this setting.
机译:我们研究用最大化的约束活力单调子模函数的问题。此问题源自从计算生物学,我们都给出了进化树在一组物种,并有向图,所谓的食物网,编码这些物种之间的生存能力约束。这些食物网通常有一定的深度。我们的目标是选择ķ物种满足可行性约束的一个子集,具有极大的系统发育多样性。由于这个问题被称为是NP难的,我们研究了近似算法。我们提出的第一个常数因子近似算法,如果深度是恒定的。其近似比为(1→1 /〜(1/2)E)。该算法不仅适用于有活力的约束进化树,但与可行性约束单调任意模集功能。其次,我们表明,没有(1 / E +?) - 近似算法,我们的问题设定(甚至是添加剂的功能),并有此设置的一个轻微的分机号码近似算法。

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