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20192023
most citedk-means++: few more steps yield constant approximation

5 citations · 20 across the 17 of their papers we have counts for

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Showing 2022 · cs.DSShow all

10 papers · 2 filters

cs.DS2022

Massively Parallel Algorithms for -Matching

Mohsen Ghaffari, Christoph Grunau, Slobodan Mitrović

This paper presents an round massively parallel algorithm for approximation of maximum weighted -matchings, using near-linear memory per machine. Her…

cs.DS2022★ 1 cited

Parallel Breadth-First Search and Exact Shortest Paths and Stronger Notions for Approximate Distances

Václav Rozhoň, Bernhard Haeupler, Anders Martinsson +2

We introduce stronger notions for approximate single-source shortest-path distances, show how to efficiently compute them from weaker standard notions, and demonstrate the algorith…

cs.DS2022

A Simple Deterministic Distributed Low-Diameter Clustering

Václav Rozhoň, Bernhard Haeupler, Christoph Grunau

We give a simple, local process for nodes in an undirected graph to form non-adjacent clusters that (1) have at most a polylogarithmic diameter and (2) contain at least half of all…

cs.DS2022★ 1 cited

Improved Distributed Network Decomposition, Hitting Sets, and Spanners, via Derandomization

Mohsen Ghaffari, Christoph Grunau, Bernhard Haeupler +2

This paper presents significantly improved deterministic algorithms for some of the key problems in the area of distributed graph algorithms, including network decomposition, hitti…

cs.DS2022★ 1 cited

Local Distributed Rounding: Generalized to MIS, Matching, Set Cover, and Beyond

Salwa Faour, Mohsen Ghaffari, Christoph Grunau +2

We develop a general deterministic distributed method for locally rounding fractional solutions of graph problems for which the analysis can be broken down into analyzing pairs of…

cs.DS2022

A Nearly Tight Analysis of Greedy k-means++

Christoph Grunau, Ahmet Alper Özüdoğru, Václav Rozhoň +1

The famous -means++ algorithm of Arthur and Vassilvitskii [SODA 2007] is the most popular way of solving the -means problem in practice. The algorithm is very simple: it samp…