{"_id":"11657","oa":1,"keyword":["Theoretical Computer Science"],"main_file_link":[{"url":"https://arxiv.org/abs/1708.06127","open_access":"1"}],"publication_identifier":{"eissn":["1084-6654"],"issn":["1084-6654"]},"language":[{"iso":"eng"}],"title":"Practical minimum cut algorithms","article_type":"original","publication":"ACM Journal of Experimental Algorithmics","doi":"10.1145/3274662","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","external_id":{"arxiv":["1708.06127"]},"quality_controlled":"1","type":"journal_article","citation":{"ista":"Henzinger MH, Noe A, Schulz C, Strash D. 2018. Practical minimum cut algorithms. ACM Journal of Experimental Algorithmics. 23, 1–22.","chicago":"Henzinger, Monika H, Alexander Noe, Christian Schulz, and Darren Strash. “Practical Minimum Cut Algorithms.” <i>ACM Journal of Experimental Algorithmics</i>. Association for Computing Machinery, 2018. <a href=\"https://doi.org/10.1145/3274662\">https://doi.org/10.1145/3274662</a>.","apa":"Henzinger, M. H., Noe, A., Schulz, C., &#38; Strash, D. (2018). Practical minimum cut algorithms. <i>ACM Journal of Experimental Algorithmics</i>. Association for Computing Machinery. <a href=\"https://doi.org/10.1145/3274662\">https://doi.org/10.1145/3274662</a>","short":"M.H. Henzinger, A. Noe, C. Schulz, D. Strash, ACM Journal of Experimental Algorithmics 23 (2018) 1–22.","ama":"Henzinger MH, Noe A, Schulz C, Strash D. Practical minimum cut algorithms. <i>ACM Journal of Experimental Algorithmics</i>. 2018;23:1-22. doi:<a href=\"https://doi.org/10.1145/3274662\">10.1145/3274662</a>","ieee":"M. H. Henzinger, A. Noe, C. Schulz, and D. Strash, “Practical minimum cut algorithms,” <i>ACM Journal of Experimental Algorithmics</i>, vol. 23. Association for Computing Machinery, pp. 1–22, 2018.","mla":"Henzinger, Monika H., et al. “Practical Minimum Cut Algorithms.” <i>ACM Journal of Experimental Algorithmics</i>, vol. 23, Association for Computing Machinery, 2018, pp. 1–22, doi:<a href=\"https://doi.org/10.1145/3274662\">10.1145/3274662</a>."},"year":"2018","extern":"1","date_created":"2022-07-27T08:28:26Z","article_processing_charge":"No","status":"public","author":[{"last_name":"Henzinger","orcid":"0000-0002-5008-6530","full_name":"Henzinger, Monika H","id":"540c9bbd-f2de-11ec-812d-d04a5be85630","first_name":"Monika H"},{"last_name":"Noe","full_name":"Noe, Alexander","first_name":"Alexander"},{"first_name":"Christian","full_name":"Schulz, Christian","last_name":"Schulz"},{"first_name":"Darren","last_name":"Strash","full_name":"Strash, Darren"}],"month":"10","publication_status":"published","page":"1-22","date_published":"2018-10-01T00:00:00Z","abstract":[{"lang":"eng","text":"The minimum cut problem for an undirected edge-weighted graph asks us to divide its set of nodes into two blocks while minimizing the weight sum of the cut edges. Here, we introduce a linear-time algorithm to compute near-minimum cuts. Our algorithm is based on cluster contraction using label propagation and Padberg and Rinaldi’s contraction heuristics [SIAM Review, 1991]. We give both sequential and shared-memory parallel implementations of our algorithm. Extensive experiments on both real-world and generated instances show that our algorithm finds the optimal cut on nearly all instances significantly faster than other state-of-the-art exact algorithms, and our error rate is lower than that of other heuristic algorithms. In addition, our parallel algorithm runs a factor 7.5× faster on average when using 32 threads. To further speed up computations, we also give a version of our algorithm that performs random edge contractions as preprocessing. This version achieves a lower running time and better parallel scalability at the expense of a higher error rate."}],"scopus_import":"1","intvolume":"        23","day":"01","oa_version":"Preprint","volume":23,"publisher":"Association for Computing Machinery","date_updated":"2022-09-09T11:32:52Z"}