[{"arxiv":1,"oa_version":"Preprint","author":[{"last_name":"Dvořák","full_name":"Dvořák, Wolfgang","first_name":"Wolfgang"},{"full_name":"Henzinger, Monika H","first_name":"Monika H","last_name":"Henzinger","id":"540c9bbd-f2de-11ec-812d-d04a5be85630","orcid":"0000-0002-5008-6530"},{"last_name":"Williamson","full_name":"Williamson, David P.","first_name":"David P."}],"oa":1,"abstract":[{"text":"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 algorithms. We present the first constant factor approximation algorithm if the depth is constant. Its approximation ratio is (1−1e√). 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+ϵ)-approximation algorithm for our problem setting (even for additive functions) and that there is no approximation algorithm for a slight extension of this setting.","lang":"eng"}],"publication":"Algorithmica","language":[{"iso":"eng"}],"volume":77,"status":"public","month":"01","type":"journal_article","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_published":"2017-01-01T00:00:00Z","citation":{"ista":"Dvořák W, Henzinger M, Williamson DP. 2017. Maximizing a submodular function with viability constraints. Algorithmica. 77(1), 152–172.","ieee":"W. Dvořák, M. Henzinger, and D. P. Williamson, “Maximizing a submodular function with viability constraints,” <i>Algorithmica</i>, vol. 77, no. 1. Springer Nature, pp. 152–172, 2017.","mla":"Dvořák, Wolfgang, et al. “Maximizing a Submodular Function with Viability Constraints.” <i>Algorithmica</i>, vol. 77, no. 1, Springer Nature, 2017, pp. 152–72, doi:<a href=\"https://doi.org/10.1007/s00453-015-0066-y\">10.1007/s00453-015-0066-y</a>.","chicago":"Dvořák, Wolfgang, Monika Henzinger, and David P. Williamson. “Maximizing a Submodular Function with Viability Constraints.” <i>Algorithmica</i>. Springer Nature, 2017. <a href=\"https://doi.org/10.1007/s00453-015-0066-y\">https://doi.org/10.1007/s00453-015-0066-y</a>.","apa":"Dvořák, W., Henzinger, M., &#38; Williamson, D. P. (2017). Maximizing a submodular function with viability constraints. <i>Algorithmica</i>. Springer Nature. <a href=\"https://doi.org/10.1007/s00453-015-0066-y\">https://doi.org/10.1007/s00453-015-0066-y</a>","ama":"Dvořák W, Henzinger M, Williamson DP. Maximizing a submodular function with viability constraints. <i>Algorithmica</i>. 2017;77(1):152-172. doi:<a href=\"https://doi.org/10.1007/s00453-015-0066-y\">10.1007/s00453-015-0066-y</a>","short":"W. Dvořák, M. Henzinger, D.P. Williamson, Algorithmica 77 (2017) 152–172."},"publication_identifier":{"issn":["0178-4617"],"eissn":["1432-0541"]},"day":"01","article_processing_charge":"No","publisher":"Springer Nature","scopus_import":"1","acknowledgement":"The research leading to these results has received funding from the European Research\r\nCouncil under the European Union’s Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement No. 340506.","date_updated":"2024-11-06T12:07:55Z","page":"152-172","extern":"1","external_id":{"arxiv":["1611.05753"]},"quality_controlled":"1","intvolume":"        77","title":"Maximizing a submodular function with viability constraints","keyword":["Approximation algorithms","Submodular functions","Phylogenetic diversity","Viability constraints"],"doi":"10.1007/s00453-015-0066-y","year":"2017","article_type":"original","issue":"1","publication_status":"published","_id":"11676","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/1611.05753"}],"date_created":"2022-07-27T14:37:24Z"}]