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Reports (Research Report) Year : 2019

## Randomized incremental construction of Delaunay triangulations of nice point sets

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Jean-Daniel Boissonnat
• Function : Author
• PersonId : 830857
Olivier Devillers
Kunal Dutta
• Function : Author
• PersonId : 988195
Marc Glisse

#### Abstract

Randomized incremental construction (RIC) is one of the most important paradigms for building geometric data structures. Clarkson and Shor developed a general theory that led to numerous algorithms that are both simple and efficient in theory and in practice. Randomized incremental constructions are most of the time space and time optimal in the worst-case, as exemplified by the construction of convex hulls, Delaunay triangulations and arrangements of line segments. However, the worst-case scenario occurs rarely in practice and we would like to understand how RIC behaves when the input is nice in the sense that the associated output is significantly smaller than in the worst-case. For example, it is known that the Delaunay triangulations of nicely distributed points in E d or on polyhedral surfaces in E3 has linear complexity, as opposed to a worst-case complexity of Θ(n d/2) in the first case and quadratic in the second. The standard analysis does not provide accurate bounds on the complexity of such cases and we aim at establishing such bounds in this paper. More precisely, we will show that, in the two cases above and variants of them, the complexity of the usual RIC is O(n log n), which is optimal. In other words, without any modification, RIC nicely adapts to good cases of practical value. Along the way, we prove a probabilistic lemma for sampling without replacement, which may be of independent interest.

### Dates and versions

hal-01950119 , version 1 (10-12-2018)
hal-01950119 , version 2 (04-03-2019)

### Identifiers

• HAL Id : hal-01950119 , version 2

### Cite

Jean-Daniel Boissonnat, Olivier Devillers, Kunal Dutta, Marc Glisse. Randomized incremental construction of Delaunay triangulations of nice point sets. [Research Report] INRIA. 2019. ⟨hal-01950119v2⟩

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