R
Robert Krauthgamer
Researcher at Weizmann Institute of Science
Publications - 240
Citations - 7395
Robert Krauthgamer is an academic researcher from Weizmann Institute of Science. The author has contributed to research in topics: Metric space & Approximation algorithm. The author has an hindex of 43, co-authored 220 publications receiving 6683 citations. Previous affiliations of Robert Krauthgamer include IBM & University of California, Berkeley.
Papers
More filters
Proceedings ArticleDOI
Bounded geometries, fractals, and low-distortion embeddings
TL;DR: This work considers both general doubling metrics, as well as more restricted families such as those arising from trees, from graphs excluding a fixed minor, and from snowflaked metrics, which contains many families of metrics that occur in applied settings.
Proceedings ArticleDOI
Navigating nets: simple algorithms for proximity search
Robert Krauthgamer,James R. Lee +1 more
TL;DR: This work presents a simple deterministic data structure for maintaining a set S of points in a general metric space, while supporting proximity search and updates to S (insertions and deletions) and is essentially optimal in a certain model of distance computation.
Proceedings ArticleDOI
Polylogarithmic inapproximability
Eran Halperin,Robert Krauthgamer +1 more
TL;DR: It is shown that for every fixed ε>0, the GROUP-STEINER-TREE problem admits no efficient log2-ε k approximation, where k denotes the number of groups (or, alternatively, the input size), unless NP has quasi polynomial Las-Vegas algorithms.
Journal ArticleDOI
On the hardness of approximating multicut and sparsest-cut
TL;DR: It is shown that the Multicut, Sparsest-Cut, and Min-2CNF problems are NP-hard to approximate within every constant factor, assuming the Unique Games Conjecture of Khot (2002).
Journal ArticleDOI
Finding and certifying a large hidden clique in a semirandom graph
Uriel Feige,Robert Krauthgamer +1 more
TL;DR: This work shows that a different algorithm, based on the Lovasz theta function, almost surely both finds the hidden clique and certifies its optimality and has an additional advantage of being more robust: it also works in a semirandomhidden clique model, in which an adversary can remove edges from the random portion of the graph.