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Vinayaka Pandit

Researcher at IBM

Publications -  76
Citations -  2667

Vinayaka Pandit is an academic researcher from IBM. The author has contributed to research in topics: Approximation algorithm & Resource allocation. The author has an hindex of 20, co-authored 76 publications receiving 2491 citations. Previous affiliations of Vinayaka Pandit include Indian Institutes of Technology.

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Journal ArticleDOI

Local Search Heuristics for k -Median and Facility Location Problems

TL;DR: This work analyzes local search heuristics for the metric k-median and facility location problems and shows that local search with swaps has a locality gap of 5 and introduces a new local search operation which opens one or more copies of a facility and drops zero or more facilities.
Proceedings ArticleDOI

An Overview of the BlueGene/L Supercomputer

N. R. Adiga, +114 more
TL;DR: An overview of the BlueGene/L Supercomputer, a massively parallel system of 65,536 nodes based on a new architecture that exploits system-on-a-chip technology to deliver target peak processing power of 360 teraFLOPS (trillion floating-point operations per second).
Proceedings ArticleDOI

Local search heuristic for k-median and facility location problems

TL;DR: This paper analyzes local search heuristics for the k-median and facility location problems and proves that without this stretch, the problem becomes NP-Hard to approximate.
Proceedings ArticleDOI

Robust fingerprint authentication using local structural similarity

TL;DR: This work proposes a novel, efficient, accurate and distortion-tolerant fingerprint authentication technique based on graph representation that has been tested with excellent results on a large private livescan database obtained with optical scanners.
Proceedings ArticleDOI

Decision trees for entity identification: approximation algorithms and hardness results

TL;DR: A natural greedy algorithm is considered and an approximation guarantee of O(rK • log N) is proved, where N is the number of entities and K is the maximum number of distinct values of an attribute, which shows that it is NP-hard to approximate the problem within a factor of Ω(log N).