Approximation algorithms for facility location problems

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Proportionally Fair Clustering (2019, methodology): “This is a classic context for clustering work [19, 33, 8, 2], and is perhaps best known as the setting for the celebrated k-means heuristic (independently discovered many times, see [22] for a brief history).”
The Design of Approximation Algorithms (2011, background): “5 is due to Chudak and Shmoys [77], building on earlier work of Shmoys, Tardos, and Aardal [264].”
Clustering Data Streams: Theory and Practice (2003, unknown): “There is abundant literature on these, books [45], [62], [55], provable algorithms [41], [49], [54], [53], [71], [31], [16], [15], [6], [52], [47], [14], [59], [7], [46], the running time of provable clustering heuristics [21], [10], [42], [34], [73], [60], and special metric spaces [6], [52], [43],”
A new greedy approach for facility location problems (2002, background): “In the last decade this problem was studied extensively from the perspective of approximation algorithms [2, 4, 6, 7, 8, 13, 18, 20, 28, 30].”
Greedy facility location algorithms analyzed using dual fitting with factor-revealing LP (2002, methodology): “The first constant factor approximation algorithm for this problem was given by Shmoys, Tardos, and Aardal [39].”