Given a metric space ℳ = (X,δ), a weighted graph G over X is a metric tspanner of ℳ if for every u,v ∈ X, δ(u,v) ≤ δ_G(u,v) ≤ t⋅ δ(u,v), where δ_G is the shortest path metric in G. In this paper, we construct spanners for finite sets in metric spaces in the online setting. Here, we are given a sequence of points (s₁, …, s_n), where the points are presented one at a time (i.e., after i steps, we have seen S_i = {s₁, … , s_i}). The algorithm is allowed to add edges to the spanner when a new point arrives, however, it is not allowed to remove any edge from the spanner. The goal is to maintain a tspanner G_i for S_i for all i, while minimizing the number of edges, and their total weight.
Under the L₂norm in ℝ^d for arbitrary constant d ∈ ℕ, we present an online (1+ε)spanner algorithm with competitive ratio O_d(ε^{d} log n), improving the previous bound of O_d(ε^{(d+1)}log n). Moreover, the spanner maintained by the algorithm has O_d(ε^{1d}log ε^{1})⋅ n edges, almost matching the (offline) optimal bound of O_d(ε^{1d})⋅ n. In the plane, a tighter analysis of the same algorithm provides an almost quadratic improvement of the competitive ratio to O(ε^{3/2}logε^{1}log n), by comparing the online spanner with an instanceoptimal spanner directly, bypassing the comparison to an MST (i.e., lightness). As a counterpart, we design a sequence of points that yields a Ω_d(ε^{d}) lower bound for the competitive ratio for online (1+ε)spanner algorithms in ℝ^d under the L₁norm.
Then we turn our attention to online spanners in general metrics. Note that, it is not possible to obtain a spanner with stretch less than 3 with a subquadratic number of edges, even in the offline setting, for general metrics. We analyze an online version of the celebrated greedy spanner algorithm, dubbed ordered greedy. With stretch factor t = (2k1)(1+ε) for k ≥ 2 and ε ∈ (0,1), we show that it maintains a spanner with O(ε^{1}logε^{1})⋅ n^{1+1/k} edges and O(ε^{1}n^{1/k}log² n) lightness for a sequence of n points in a metric space. We show that these bounds cannot be significantly improved, by introducing an instance that achieves an Ω(1/k⋅ n^{1/k}) competitive ratio on both sparsity and lightness. Furthermore, we establish the tradeoff among stretch, number of edges and lightness for points in ultrametrics, showing that one can maintain a (2+ε)spanner for ultrametrics with O(ε^{1}logε^{1})⋅ n edges and O(ε^{2}) lightness.
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One Hop Greedy Permutations
We adapt and generalize a heuristic for kcenter clustering to the permutation case, where every prex of the ordering is a guaranteed approximate solution. The onehop greedy permutations work by choosing at each step the farthest unchosen point and then looking in its local neighborhood for a point that covers the most points at a certain scale. This balances the competing demands of reducing the coverage radius and also covering as many points as possible. This idea first appeared in the work of GarciaDiaz et al. and their
algorithm required O(n2 log n) time for a fixed k (i.e. not the whole permutation). We show how to use geometric data structures to approximate the entire permutation in O(n log D) time for metrics sets with spread D. Notably, this running time is asymptotically the same as the running time for computing the ordinary greedy permutation.
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 Award ID(s):
 2017980
 NSFPAR ID:
 10211951
 Editor(s):
 Keil, Mark; Mondal, Debajyoti
 Date Published:
 Journal Name:
 Proceedings of the 32nd Canadian Conference on Computational Geometry
 Page Range / eLocation ID:
 221  225
 Format(s):
 Medium: X
 Sponsoring Org:
 National Science Foundation
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Given a metric space ℳ = (X,δ), a weighted graph G over X is a metric tspanner of ℳ if for every u,v ∈ X, δ(u,v) ≤ δ_G(u,v) ≤ t⋅ δ(u,v), where δ_G is the shortest path metric in G. In this paper, we construct spanners for finite sets in metric spaces in the online setting. Here, we are given a sequence of points (s₁, …, s_n), where the points are presented one at a time (i.e., after i steps, we have seen S_i = {s₁, … , s_i}). The algorithm is allowed to add edges to the spanner when a new point arrives, however, it is not allowed to remove any edge from the spanner. The goal is to maintain a tspanner G_i for S_i for all i, while minimizing the number of edges, and their total weight. Under the L₂norm in ℝ^d for arbitrary constant d ∈ ℕ, we present an online (1+ε)spanner algorithm with competitive ratio O_d(ε^{d} log n), improving the previous bound of O_d(ε^{(d+1)}log n). Moreover, the spanner maintained by the algorithm has O_d(ε^{1d}log ε^{1})⋅ n edges, almost matching the (offline) optimal bound of O_d(ε^{1d})⋅ n. In the plane, a tighter analysis of the same algorithm provides an almost quadratic improvement of the competitive ratio to O(ε^{3/2}logε^{1}log n), by comparing the online spanner with an instanceoptimal spanner directly, bypassing the comparison to an MST (i.e., lightness). As a counterpart, we design a sequence of points that yields a Ω_d(ε^{d}) lower bound for the competitive ratio for online (1+ε)spanner algorithms in ℝ^d under the L₁norm. Then we turn our attention to online spanners in general metrics. Note that, it is not possible to obtain a spanner with stretch less than 3 with a subquadratic number of edges, even in the offline setting, for general metrics. We analyze an online version of the celebrated greedy spanner algorithm, dubbed ordered greedy. With stretch factor t = (2k1)(1+ε) for k ≥ 2 and ε ∈ (0,1), we show that it maintains a spanner with O(ε^{1}logε^{1})⋅ n^{1+1/k} edges and O(ε^{1}n^{1/k}log² n) lightness for a sequence of n points in a metric space. We show that these bounds cannot be significantly improved, by introducing an instance that achieves an Ω(1/k⋅ n^{1/k}) competitive ratio on both sparsity and lightness. Furthermore, we establish the tradeoff among stretch, number of edges and lightness for points in ultrametrics, showing that one can maintain a (2+ε)spanner for ultrametrics with O(ε^{1}logε^{1})⋅ n edges and O(ε^{2}) lightness.more » « less

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