D
David Eppstein
Researcher at University of California, Irvine
Publications - 689
Citations - 21750
David Eppstein is an academic researcher from University of California, Irvine. The author has contributed to research in topics: Planar graph & Time complexity. The author has an hindex of 67, co-authored 672 publications receiving 20584 citations. Previous affiliations of David Eppstein include McGill University & University of Passau.
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Proceedings ArticleDOI
Polynomial-size nonobtuse triangulation of polygons
Marshall Bern,David Eppstein +1 more
TL;DR: The main result is that a polygon with n sides can be triangulated with O(n2) nonobtuse triangles, and it is shown that any triangulation (without Steiner points) of a simple polygon has a refinement with O('n4' nonobTuse triangles.
Journal ArticleDOI
Drawing Trees with Perfect Angular Resolution and Polynomial Area
Christian A. Duncan,David Eppstein,Michael T. Goodrich,Stephen G. Kobourov,Martin Nöllenburg +4 more
TL;DR: The results explore what is achievable with straight-line drawings and what more is achieving with Lombardi-style drawings, with respect to drawings of trees with perfect angular resolution.
Proceedings ArticleDOI
Dynamic half-space reporting, geometric optimization, and minimum spanning trees
TL;DR: Using dynamic data structures for half-space range reporting and for maintaining the minima of a decomposable function, the authors obtain efficient dynamic algorithms for a number of geometric problems, including closest/farthest neighbor searching, fixed dimension linear programming, bi-chromatic closest pair, diameter, and Euclidean minimum spanning tree.
Journal ArticleDOI
Separator Based Sparsification
TL;DR: In this article, the authors describe algorithms and data structures for maintaining a dynamic planar graph subject to edge insertions and edge deletions that preserve planarity but that can change the embedding.
Proceedings ArticleDOI
Deterministic sampling and range counting in geometric data streams
TL;DR: Deterministic techniques are used to approximate several robust statistics of geometric data streams, including Tukey depth, simplicial depth, regression depth, the Thiel-Sen estimator, and the least median of squares, using only a polylogarithmic amount of memory.