- Ashkan Norouzi Fard
- Christian Sohler
- Ola Svensson
- Silvio Lattanzi
- Vincent Pierre Cohen-addad
NeurIPS 2020
We present the first distributed approximation algorithm for the Euclidean $k$-median problem with an optimal trade-off between memory usage and the number of parallel rounds. Our algorithm even works in the setting where each machine has very limited memory $s\in \Omega(\log n)$ and it is work efficient. In the future, it would be interesting to obtain similar results for other clustering problems and to improve the approximation factor of our algorithm.
We maintain a portfolio of research projects, providing individuals and teams the freedom to emphasize specific types of work