Maximizing a Submodular Function with Viability Constraints
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/sqrt(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 + \epsilon)-approximation algorithm for our problem setting (even for additive functions) and that there is no approximation algorithm for a slight extension of this setting.
|Paper in Conference Proceedings or in Workshop Proceedings (Paper)|
|21st European Symposium on Algorithms (ESA 2013)|
|Theory and Applications of Algorithms|
|Sophia Antipolis, France|
|02-04 Sep 2013|
|Lecture Notes in Computer Science|