| Why walking the dog takes time: Frechet distance has no strongly subquadratic algorithms unless SETH fails K Bringmann 2014 IEEE 55th Annual Symposium on Foundations of Computer Science, 661-670, 2014 | 206 | 2014 |
| Quadratic conditional lower bounds for string problems and dynamic time warping K Bringmann, M Künnemann 2015 IEEE 56th Annual Symposium on Foundations of Computer Science, 79-97, 2015 | 181 | 2015 |
| Approximating the volume of unions and intersections of high-dimensional geometric objects K Bringmann, T Friedrich Computational Geometry 43 (6-7), 601-610, 2010 | 173 | 2010 |
| Approximating the least hypervolume contributor: NP-hard in general, but fast in practice K Bringmann, T Friedrich International Conference on Evolutionary Multi-Criterion Optimization, 6-20, 2009 | 143 | 2009 |
| Approximation-guided evolutionary multi-objective optimization K Bringmann, T Friedrich, F Neumann, M Wagner Twenty-Second International Joint Conference on Artificial Intelligence, 2011 | 89 | 2011 |
| Approximating the least hypervolume contributor: NP-hard in general, but fast in practice K Bringmann, T Friedrich International Conference on Evolutionary Multi-Criterion Optimization, 6-20, 2009 | 89 | 2009 |
| An efficient algorithm for computing hypervolume contributions K Bringmann, T Friedrich Evolutionary Computation 18 (3), 383-402, 2010 | 78 | 2010 |
| Geometric inhomogeneous random graphs K Bringmann, R Keusch, J Lengler Theoretical Computer Science 760, 35-54, 2019 | 62 | 2019 |
| Two-dimensional subset selection for hypervolume and epsilon-indicator K Bringmann, T Friedrich, P Klitzke Proceedings of the 2014 Annual Conference on Genetic and Evolutionary …, 2014 | 58 | 2014 |
| Approximability of the discrete Fréchet distance K Bringmann, W Mulzer Journal of Computational Geometry 7 (2), 46-76, 2016 | 55 | 2016 |
| Approximation quality of the hypervolume indicator K Bringmann, T Friedrich Artificial Intelligence 195, 265-290, 2013 | 55 | 2013 |
| Don't be greedy when calculating hypervolume contributions K Bringmann, T Friedrich Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic …, 2009 | 49 | 2009 |
| A near-linear pseudopolynomial time algorithm for subset sum K Bringmann Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete …, 2017 | 48 | 2017 |
| Efficient sampling methods for discrete distributions K Bringmann, K Panagiotou International Colloquium on Automata, Languages, and Programming, 133-144, 2012 | 44* | 2012 |
| SETH-based lower bounds for subset sum and bicriteria path A Abboud, K Bringmann, D Hermelin, D Shabtay Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete …, 2019 | 43 | 2019 |
| A dichotomy for regular expression membership testing K Bringmann, A Grønlund, KG Larsen 2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS …, 2017 | 36 | 2017 |
| Truly subcubic algorithms for language edit distance and RNA folding via fast bounded-difference min-plus product K Bringmann, F Grandoni, B Saha, VV Williams SIAM Journal on Computing 48 (2), 481-512, 2019 | 34 | 2019 |
| Sampling geometric inhomogeneous random graphs in linear time K Bringmann, R Keusch, J Lengler arXiv preprint arXiv:1511.00576, 2015 | 33 | 2015 |
| Speeding up many-objective optimization by Monte Carlo approximations K Bringmann, T Friedrich, C Igel, T Voß Artificial Intelligence 204, 22-29, 2013 | 33 | 2013 |
| The maximum hypervolume set yields near-optimal approximation K Bringmann, T Friedrich Proceedings of the 12th annual conference on Genetic and evolutionary …, 2010 | 32 | 2010 |
