no code implementations • 28 Sep 2023 • Lorenzo Beretta, Vincent Cohen-Addad, Silvio Lattanzi, Nikos Parotsidis
The $k$-means++ algorithm of Arthur and Vassilvitskii (SODA 2007) is often the practitioners' choice algorithm for optimizing the popular $k$-means clustering objective and is known to give an $O(\log k)$-approximation in expectation.
1 code implementation • 26 Nov 2021 • Lorenzo Beretta, Franco Maria Nardini, Roberto Trani, Rossano Venturini
In this paper, we address the problem of finding a champion of the tournament, also known as Copeland winner, which is a player that wins the highest number of matches.