Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Darren Strash"'
Publikováno v:
Proceedings of the VLDB Endowment. 16:127-139
The k -plex model is a relaxation of the clique model by allowing every vertex to miss up to k neighbors. Designing exact and efficient algorithms for computing a maximum k -plex in a graph has been receiving increasing interest recently. However, th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d1a95f819fd028d5dccc2a49b4846455
https://doi.org/10.14778/3565816.3565817
https://doi.org/10.14778/3565816.3565817
Autor:
Faisal N. Abu-Khzam, Sebastian Lamm, Matthias Mnich, Alexander Noe, Christian Schulz, Darren Strash
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031215339
Over the last two decades, significant advances have been made in the design and analysis of fixed-parameter algorithms for a wide variety of graph-theoretic problems. This has resulted in an algorithmic toolbox that is by now well-established. Howev
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::784674cae6b8aba8b52c51fc578e170d
https://doi.org/10.1007/978-3-031-21534-6_6
https://doi.org/10.1007/978-3-031-21534-6_6
Publikováno v:
Scopus-Elsevier
Given a vertex-weighted graph, the maximum weight independent set problem asks for a pair-wise non-adjacent set of vertices such that the sum of their weights is maximum. The branch-and-reduce paradigm is the de facto standard approach to solve the p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aac135362fe8352ceb7beb8e46ddf51f
https://doi.org/10.1137/1.9781611976472.10
https://doi.org/10.1137/1.9781611976472.10
Publikováno v:
Scopus-Elsevier
Many applications rely on time-intensive matrix operations, such as factorization, which can be sped up significantly for large sparse matrices by interpreting the matrix as a sparse graph and computing a node ordering that minimizes the so-called fi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7828f02644990ea6b33551726f8aa9f6
https://doi.org/10.1137/1.9781611976472.9
https://doi.org/10.1137/1.9781611976472.9
Autor:
Sebastian Lamm, Demian Hespe, Damir Ferizovic, Christian Schulz, Darren Strash, Matthias Mnich
Publikováno v:
ALENEX
Kernelization is a general theoretical framework for preprocessing instances of NP-hard problems into (generally smaller) instances with bounded size, via the repeated application of data reduction rules. For the fundamental Max Cut problem, kerneliz
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::eaaeac5ce7a130e55297c944dcd5f24f
https://doi.org/10.1137/1.9781611976007.3
https://doi.org/10.1137/1.9781611976007.3
Publikováno v:
Computers & Operations Research. 92:37-46
The maximal clique enumeration (MCE) problem has numerous applications in biology, chemistry, sociology, and graph modeling. Though this problem is well studied, most current research focuses on finding solutions in large sparse graphs or very dense
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f44c505b61039fccf911ab863a86f424
https://doi.org/10.1016/j.cor.2017.12.006
https://doi.org/10.1016/j.cor.2017.12.006
Autor:
Darren Strash, Nodari Sitchinava
Publikováno v:
Journal of Graph Algorithms and Applications. 22:431-459
Visibility graph reconstruction, which asks us to construct a polygon that has a given visibility graph, is a fundamental problem with unknown complexity (although visibility graph recognition is known to be in PSPACE). We show that two classes of un
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c8dde5a7aff0e2499449ae4919e91039
https://doi.org/10.7155/jgaa.00469
https://doi.org/10.7155/jgaa.00469
Autor:
Darren Strash, Christian Schulz
Publikováno v:
Encyclopedia of Big Data Technologies ISBN: 9783319639628
Encyclopedia of Big Data Technologies
Encyclopedia of Big Data Technologies
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1a076dde0bda954971c0f1270a1fdb2d
https://doi.org/10.1007/978-3-319-77525-8_312
https://doi.org/10.1007/978-3-319-77525-8_312