**Approximating the minimum weight Steiner triangulation**.

D. Eppstein.

Tech. Rep. 91-55, ICS, UCI, 1991.

*3rd ACM-SIAM Symp. Discrete Algorithms,*Orlando, 1992, pp. 48–57.

*Disc. Comp. Geom.*11: 163–191, 1994.Quadtree based triangulation gives a large but constant factor approximation to the minimum weight triangulation of a point set or convex polygon, allowing extra Steiner points to be added as vertices. Includes proofs of several bounds on triangulation weight relative to the minimum spanning tree or non-Steiner triangulation, and a conjecture that for convex polygons the only points that need to be added are on the polygon boundary.

(BibTeX – Citations – CiteSeer)

**Approximating center points with iterated Radon points**.

K. Clarkson, D. Eppstein, G.L. Miller, C. Sturtivant, and S.-H. Teng.

*9th ACM Symp. Comp. Geom.,*San Diego, 1993, pp. 91–98.

*Int. J. Comp. Geom. & Appl.*6 (3): 357–377, 1996.Given a collection of

*n*sites, a center point is a point (not necessarily a site) such that no hyperplane through the centerpoint partitions the collection into a very small and a very large subset. Center points have been used by Teng and others as a key step in the construction of geometric separators. One can find a point with this property by choosing a random sample of the collection and applying linear programming, but the complexity of that method grows exponentially with the dimension. This paper proposes an alternate method that produces lower quality approximations (in terms of the size of the worst hyperplane partition) but takes time polynomial in both*n*and*d.***Approximation algorithms for geometric problems**.

M. Bern and D. Eppstein.

*Approximation Algorithms for NP-hard Problems*, D. Hochbaum, ed., PWS Publishing, 1996, pp. 296–345.Considers problems for which no polynomial-time exact algorithms are known, and concentrates on bounds for worst-case approximation ratios, especially those depending intrinsically on geometry rather than on more general graph theoretic or metric space formulations. Includes sections on the traveling salesman problem, Steiner trees, minimum weight triangulation, clustering, and separation problems.

**Faster geometric**.*k*-point MST approximation

D. Eppstein.

Tech. Rep. 95-13, ICS, UCI, 1995.

*Comp. Geom. Theory & Applications*8: 231–240, 1997.Various authors have looked at a variant of geometric clustering in which one must select

*k*points that can be connected by a small spanning tree. The problem is NP-complete (for variable*k*); good approximations are known based on dynamic programming techniques but the time dependence on*n*is high. This paper describes a faster approximation algorithm based on dynamic programming in quadtrees, and a general technique based on that in "Iterated nearest neighbors" for reducing the dependence on*n*in any approximation algorithm.(BibTeX – Citations – CiteSeer – ACM DL)

**Deterministic sampling and range counting in geometric data streams**.

A. Bagchi, A. Chaudhary, D. Eppstein, and M. T. Goodrich.

arXiv:cs.CG/0307027.

*20th ACM Symp. Comp. Geom.,*Brooklyn, 2004, pp. 144–151.

*ACM Trans. Algorithms*3(2):A16, 2007.We describe an efficient streaming-model construction of epsilon-nets and epsilon-approximations, and use it to find deterministic streaming-model approximation algorithms for iceberg range queries and for various robust statistics problems.

Geometry – Publications – David Eppstein – Theory Group – Inf. & Comp. Sci. – UC Irvine

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