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Pankaj K. Agarwal
Duke University
480Publications
52H-index
11.7kCitations
Publications 480
Newest
Published on Feb 10, 2018
Pankaj K. Agarwal52
Estimated H-index: 52
(Courant Institute of Mathematical Sciences),
Alok Aggarwal34
Estimated H-index: 34
(IBM)
+ 3 AuthorsSubhash Suri55
Estimated H-index: 55
Abstract Let P be (the boundary of) a simple polygon with n vertices. For a vertex p of P , let ϕ( p ) be the set of points on P that are farthest from p , where the distance between two points is the length of the (Euclidean) shortest path that connects them without intersecting the interior of P . In this paper, we present an O( n log n ) algorithm to compute a member of ϕ( p ) for every vertex p of P . As a corollary, the external diameter of P can also be computed in the same time.
8 Citations
Published on Jun 11, 2018 in Symposium on Computational Geometry
Pankaj K. Agarwal52
Estimated H-index: 52
(Duke University),
Lars Arge36
Estimated H-index: 36
(Aarhus University),
Frank Staals6
Estimated H-index: 6
(Utrecht University)
We present an efficient dynamic data structure that supports geodesic nearest neighbor queries for a set SSS of point sites in a static simple polygon PPP. Our data structure allows us to insert a new site in SSS, delete a site from SSS, and ask for the site in SSS closest to an arbitrary query point q∈Pq∈Pq \in P. All distances are measured using the geodesic distance, that is, the length of the shortest path that is completely contained in PPP. Our data structure achieves polylogarithmic updat...
Published on May 27, 2018 in Symposium on Principles of Database Systems
Pankaj K. Agarwal52
Estimated H-index: 52
(Duke University),
Kyle Fox7
Estimated H-index: 7
(University of Texas at Dallas)
+ 3 AuthorsErin Taylor (Duke University)
We propose a model for subtrajectory clustering ---the clustering of subsequences of trajectories; each cluster of subtrajectories is represented as a pathlet, a sequence of points that is not necessarily a subsequence of an input trajectory. Given a set of trajectories, our clustering model attempts to capture the shared portions between them by assuming each trajectory is a concatenation of a small set of pathlets, with possible gaps in between. We present a single objective function for findi...
Source Cite
Published on Apr 16, 2018in ACM Transactions on Algorithms 0.65
Pankaj K. Agarwal52
Estimated H-index: 52
(Duke University),
Kyle Fox7
Estimated H-index: 7
(University of Texas at Dallas)
+ 2 AuthorsYusu Wang24
Estimated H-index: 24
(Ohio State University)
Source Cite
Published on Jan 1, 2018
Pankaj K. Agarwal52
Estimated H-index: 52
(Duke University),
Neeraj Kumar1
Estimated H-index: 1
+ 1 AuthorsSubhash Suri55
Estimated H-index: 55
(University of California, Santa Barbara)
1 Citations
Published on Jan 1, 2018
Pankaj K. Agarwal52
Estimated H-index: 52
(Duke University),
Haim Kaplan45
Estimated H-index: 45
(Tel Aviv University),
Micha Sharir67
Estimated H-index: 67
(Tel Aviv University)
Published on Aug 21, 2018in ACM Transactions on Algorithms 0.65
Pankaj K. Agarwal52
Estimated H-index: 52
(Duke University),
Kyle Fox7
Estimated H-index: 7
(University of Texas at Dallas),
Oren Salzman8
Estimated H-index: 8
(Carnegie Mellon University)
Source Cite
Published on Jan 1, 2018in IEEE Data(base) Engineering Bulletin 0.40
Jun Yang29
Estimated H-index: 29
,
Pankaj K. Agarwal52
Estimated H-index: 52
+ 4 AuthorsChengkai Li18
Estimated H-index: 18
Published on Sep 1, 2018 in Very Large Data Bases
Junyang Gao1
Estimated H-index: 1
(Duke University),
Pankaj K. Agarwal52
Estimated H-index: 52
(Duke University),
Jun Yang29
Estimated H-index: 29
(Duke University)
Source Cite
Published on Jan 1, 2018 in International Symposium on Algorithms and Computation
Pankaj K. Agarwal52
Estimated H-index: 52
(Duke University),
Haim Kaplan45
Estimated H-index: 45
(Tel Aviv University)
+ 4 AuthorsAllen Xiao1
Estimated H-index: 1
Our goal is to compare two planar point sets by finding subsets of a given size such that a minimum-weight matching between them has the smallest weight. This can be done by a translation of one set that minimizes the weight of the matching. We give efficient algorithms (a) for finding approximately optimal matchings, when the cost of a matching is the $L_p$-norm of the tuple of the Euclidean distances between the pairs of matched points, for any $p\in [1,\infty]$, and (b)~for constructing small...
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