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ForbiddenSet Labeling on Graphs
"... We describe recent work on a variant of a distance labeling problem in graphs, called the forbiddenset labeling problem. Given a graph G = (V, E), we wish to assign labels L(x) to vertices and edges of G so that given {L(x)  x ∈ X} for any X ⊂ V ∪ E and L(u), L(v) for u, v ∈ V, we can decide if a ..."
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Cited by 121 (28 self)
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We describe recent work on a variant of a distance labeling problem in graphs, called the forbiddenset labeling problem. Given a graph G = (V, E), we wish to assign labels L(x) to vertices and edges of G so that given {L(x)  x ∈ X} for any X ⊂ V ∪ E and L(u), L(v) for u, v ∈ V, we can decide if a property holds in the graph G \ X, or compute a value like the distance between u, v in G \ X. The problem is motivated by routing in networks where some nodes or edges may fail, or where nodes may decide to route on paths avoiding some ‘forbidden’ set of nodes or edges.
A simple entropybased algorithm for planar point location
 In Proceedings of the Twelfth Annual ACMSIAM Symposium on Discrete Algorithms
, 2001
"... Abstract Given a planar polygonal subdivision S, point location involves preprocessing this subdivisioninto a data structure so that given any query point q, the cell of the subdivision containing qcan be determined efficiently. Suppose that for each cell z in the subdivision, the probability pz tha ..."
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Cited by 22 (4 self)
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Abstract Given a planar polygonal subdivision S, point location involves preprocessing this subdivisioninto a data structure so that given any query point q, the cell of the subdivision containing qcan be determined efficiently. Suppose that for each cell z in the subdivision, the probability pz that a query point lies within this cell is also given. The goal is to design the data structureto minimize the average search time. This problem has been considered before, but existing
Transdichotomous Results in Computational Geometry, I: Point Location in Sublogarithmic Time
, 2008
"... Given a planar subdivision whose coordinates are integers bounded by U ≤ 2 w, we present a linearspace data structure that can answer point location queries in O(min{lg n / lg lg n, √ lg U/lg lg U}) time on the unitcost RAM with word size w. Thisisthe first result to beat the standard Θ(lg n) bou ..."
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Cited by 20 (4 self)
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Given a planar subdivision whose coordinates are integers bounded by U ≤ 2 w, we present a linearspace data structure that can answer point location queries in O(min{lg n / lg lg n, √ lg U/lg lg U}) time on the unitcost RAM with word size w. Thisisthe first result to beat the standard Θ(lg n) bound for infinite precision models. As a consequence, we obtain the first o(n lg n) (randomized) algorithms for many fundamental problems in computational geometry for arbitrary integer input on the word RAM, including: constructing the convex hull of a threedimensional point set, computing the Voronoi diagram or the Euclidean minimum spanning tree of a planar point set, triangulating a polygon with holes, and finding intersections among a set of line segments. Higherdimensional extensions and applications are also discussed. Though computational geometry with bounded precision input has been investigated for a long time, improvements have been limited largely to problems of an orthogonal flavor. Our results surpass this longstanding limitation, answering, for example, a question of Willard (SODA’92).
Nearly Optimal ExpectedCase Planar Point Location
"... We consider the planar point location problem from the perspective of expected search time. We are given a planar polygonal subdivision S and for each polygon of the subdivision the probability that a query point lies within this polygon. The goal is to compute a search structure to determine which ..."
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Cited by 18 (5 self)
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We consider the planar point location problem from the perspective of expected search time. We are given a planar polygonal subdivision S and for each polygon of the subdivision the probability that a query point lies within this polygon. The goal is to compute a search structure to determine which cell of the subdivision contains a given query point, so as to minimize the expected search time. This is a generalization of the classical problem of computing an optimal binary search tree for onedimensional keys. In the onedimensional case it has long been known that the entropy H of the distribution is the dominant term in the lower bound on the expectedcase search time, and further there exist search trees achieving expected search times of at most H + 2. Prior to this work, there has been no known structure for planar point location with an expected search time better than 2H, and this result required strong assumptions on the nature of the query point distribution. Here we present a data structure whose expected search time is nearly equal to the entropy lower bound, namely H + o(H). The result holds for any polygonal subdivision in which the number of sides of each of the polygonal cells is bounded, and there are no assumptions on the query distribution within each cell. We extend these results to subdivisions with convex cells, assuming a uniform query distribution within each cell.
An experimental study of point location in general planar arrangements
 In ALENEX/ANALCO
, 2006
"... We study the performance in practice of various pointlocation algorithms implemented in Cgal, including a newly devised Landmarks algorithm. Among the other algorithms studied are: a naïve approach, a “walk along a line ” strategy and a trapezoidaldecomposition based search structure. The current ..."
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Cited by 6 (3 self)
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We study the performance in practice of various pointlocation algorithms implemented in Cgal, including a newly devised Landmarks algorithm. Among the other algorithms studied are: a naïve approach, a “walk along a line ” strategy and a trapezoidaldecomposition based search structure. The current implementation addresses general arrangements of arbitrary planar curves, including arrangements of nonlinear segments (e.g., conic arcs) and allows for degenerate input (for example, more than two curves intersecting in a single point, or overlapping curves). All calculations use exact number types and thus result in the correct point location. In our Landmarks algorithm (a.k.a. Jump & Walk), special points, “landmarks”, are chosen in a preprocessing stage, their place in the arrangement is found, and they are inserted into a datastructure that enables efficient nearestneighbor search. Given a query point, the nearest landmark is located and then the algorithm “walks ” from the landmark to the query point. We report on extensive experiments with arrangements composed of line segments or conic arcs. The results indicate that the Landmarks approach is the most efficient when the overall cost of a query is taken into account, combining both preprocessing and query time. The simplicity of the algorithm enables an almost straightforward implementation and rather easy maintenance. The generic programming implementation allows versatility both in the selected type of landmarks, and in the choice of the nearestneighbor search structure. The end result is a highly effective pointlocation algorithm for most practical purposes. ∗ Work reported in this paper has been supported in part by the IST Programme of the EU as a Sharedcorst RTD
DISTRIBUTIONSENSITIVE POINT LOCATION IN CONVEX SUBDIVISIONS
"... A data structure is presented for point location in convex planar subdivisions when the distribution of queries is known in advance. The data structure has an expected query time that is within a constant factor of optimal. ..."
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Cited by 6 (5 self)
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A data structure is presented for point location in convex planar subdivisions when the distribution of queries is known in advance. The data structure has an expected query time that is within a constant factor of optimal.
ENTROPY, TRIANGULATION, AND POINT LOCATION IN PLANAR SUBDIVISIONS
, 2009
"... A data structure is presented for point location in connected planar subdivisions when the distribution of queries is known in advance. The data structure has an expected query time that is within a constant factor of optimal. More specifically, an algorithm is presented that preprocesses a connecte ..."
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Cited by 3 (3 self)
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A data structure is presented for point location in connected planar subdivisions when the distribution of queries is known in advance. The data structure has an expected query time that is within a constant factor of optimal. More specifically, an algorithm is presented that preprocesses a connected planar subdivision G of size n and a query distribution D to produce a point location data structure for G. The expected number of pointline comparisons performed by this data structure, when the queries are distributed according to D, is ˜ H + O ( ˜ H2/3 + 1) where ˜ H = ˜ H(G, D) is a lower bound on the expected number of pointline comparisons performed by any linear decision tree for point location in G under the query distribution D. The preprocessing algorithm runs in O(n log n) time and produces a data structure of size O(n). These results are obtained by creating a Steiner triangulation of G that has nearminimum entropy.
A Static Optimality Transformation with Applications to Planar Point Location
, 2012
"... Over the last decade, there have been several data structures that, given a planar subdivision and a probability distribution over the plane, provide a way for answering point location queries that is finetuned for the distribution. All these methods suffer from the requirement that the query distr ..."
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Cited by 2 (1 self)
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Over the last decade, there have been several data structures that, given a planar subdivision and a probability distribution over the plane, provide a way for answering point location queries that is finetuned for the distribution. All these methods suffer from the requirement that the query distribution must be known in advance. We present a new data structure for point location queries in planar triangulations. Our structure is asymptotically as fast as the optimal structures, but it requires no prior information about the queries. This is a 2d analogue of the jump from Knuth’s optimum binary search trees (discovered in 1971) to the splay trees of Sleator and Tarjan in 1985. While the former need to know the query distribution, the latter are statically optimal. This means that we can adapt to the query sequence and achieve the same asymptotic performance as an optimum static structure, without needing any additional information. 1