Yannik Stein

Algorithms for Tolerated Tverberg Partitions

Let P be a d-dimensional n-point set. A partition \(\mathcal{T}\) of P is called a Tverberg partition if the convex hulls of all sets in \(\mathcal{T}\) intersect in at least one point. We say \(\mathcal{T}\) is t-tolerated if it remains a Tverberg partition after deleting any t points from P. Soberon and Strausz proved that there is always a t-tolerated Tverberg partition with ⌈n / (d + 1)(t + 1) ⌉ sets. However, so far no nontrivial algorithms for computing or approximating such partitions have been presented.

Improved Time-Space Trade-Offs for Computing Voronoi Diagrams

Let P be a planar n-point set in general position. For k between 1 and n-1, the Voronoi diagram of order k is obtained by subdividing the plane into regions such that points in the same cell have the same set of nearest k neighbors in P. The (nearest point) Voronoi diagram (NVD) and the farthest point Voronoi diagram (FVD) are the particular cases of k=1 and k=n-1, respectively. It is known that the family of all higher-order Voronoi diagrams of order 1 to K for P can be computed in total time O(n K^2 + n log n) using O(K^2(n-K)) space. Also NVD and FVD can be computed in O(n log n) time using O(n) space. For s in {1, ..., n}, an s-workspace algorithm has random access to a read-only array with the sites of P in arbitrary order. Additionally, the algorithm may use O(s) words of Theta(log n) bits each for reading and writing intermediate data. The output can be written only once and cannot be accessed afterwards. We describe a deterministic s-workspace algorithm for computing an NVD and also an FVD for P that runs in O((n^2/s) log s) time. Moreover, we generalize our s-workspace algorithm for computing the family of all higher-order Voronoi diagrams of P up to order K in O(sqrt(s)) in total time O( (n^2 K^6 / s) log^(1+epsilon)(K) (log s / log K)^(O(1)) ) for any fixed epsilon > 0. Previously, for Voronoi diagrams, the only known s-workspace algorithm was to find an NVD for P in expected time O((n^2/s) log s + n log s log^*s). Unlike the previous algorithm, our new method is very simple and does not rely on advanced data structures or random sampling techniques.

Time–space trade-offs for triangulations and Voronoi diagrams

Let

Computational Aspects of the Colorful Carathéodory Theorem

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Time-Space Trade-offs for Triangulations and Voronoi Diagrams

be a planar

Routing in Polygonal Domains.

n

Computational Aspects of the Colorful Carathéodory Theorem.

-point set. A triangulation for

The rainbow at the end of the line: a PPAD formulation of the colorful carathéodory theorem with applications

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Approximate k -flat Nearest Neighbor Search

is a maximal plane straight-line graph with vertex set

Complexity of Finding Nearest Colorful Polytopes

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