2001
DOI: 10.1007/3-540-44634-6_3
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Optimal Möbius Transformations for Information Visualization and Meshing

Abstract: Abstract. We give linear-time quasiconvex programming algorithms for finding a Möbius transformation of a set of spheres in a unit ball or on the surface of a unit sphere that maximizes the minimum size of a transformed sphere. We can also use similar methods to maximize the minimum distance among a set of pairs of input points. We apply these results to vertex separation and symmetry display in spherical graph drawing, viewpoint selection in hyperbolic browsing, element size control in conformal structured me… Show more

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Cited by 25 publications
(23 citation statements)
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“…The equation A similar interplay of geometries leads Bern and Eppstein, to another choice of a unique representative for each polyhedral type. Given n spheres in S d , Bern and Eppstein apply that Möbius transformation which makes the smallest sphere as large as possible [3]. It is not difficult to see that this Möbius transformation is unique up to post-composition with a rotation if n ≥ 3.…”
Section: Theoremmentioning
confidence: 99%
“…The equation A similar interplay of geometries leads Bern and Eppstein, to another choice of a unique representative for each polyhedral type. Given n spheres in S d , Bern and Eppstein apply that Möbius transformation which makes the smallest sphere as large as possible [3]. It is not difficult to see that this Möbius transformation is unique up to post-composition with a rotation if n ≥ 3.…”
Section: Theoremmentioning
confidence: 99%
“…This problem of finding a radius-maximizing Möbius transformation may be expressed (using the connection between Möbius transformations and three-dimensional hyperbolic geometry) as a quasiconvex program, a problem of finding the minimum value of a pointwise maximum of quasiconvex functions. It may be solved either combinatorially in linear time using LP-type optimization algorithms or numerically using local improvement procedures; the theory of quasiconvex programs guarantees that there are no local optima in which the local improvement might get stuck [1,11]. Since we are already using a numerical method to find circle packings, our implementation also takes the numerical approach to find the best transformation.…”
Section: Circle Packingmentioning
confidence: 99%
“…Quasiconvex programming is a general framework for LP-type computational geometry optimization problems, formulated by Amenta et al [4] in the context of mesh smoothing procedures, and later used by the authors for several applications in information visualization and mesh generation [5]. Define a nested convex family to be a function κ(t) mapping real values t to convex sets in some Euclidean space E d , such that for any t < t ′ , κ(t) ⊆ κ(t ′ ), and such that for all t, κ(t) = t ′ >t κ(t ′ ).…”
Section: Quasiconvex Programmingmentioning
confidence: 99%