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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

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.
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Drawing Trees with Perfect Angular Resolution and Polynomial Area

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.
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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.
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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.