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We study geometric set cover problems in dynamic settings, allowing insertions and deletions of points and objects. We present the first dynamic data structure that can maintain an $O(1)$-approximation in sublinear update time for set cover for axis-aligned squares in 2D. More precisely, we obtain randomized update time $O(n^{2/3+delta})$ for an arbitrarily small constant $delta>0$. Previously, a dynamic geometric set cover data structure with sublinear update time was known only for unit squares by Agarwal, Chang, Suri, Xiao, and Xue [SoCG 2020]. If only an approximate size of the solution is needed, then we can also obtain sublinear amortized update time for disks in 2D and halfspaces in 3D. As a byproduct, our techniques for dynamic set cover also yield an optimal randomized $O(nlog n)$-time algorithm for static set cover for 2D disks and 3D halfspaces, improving our earlier $O(nlog n(loglog n)^{O(1)})$ result [SoCG 2020].
We improve the running times of $O(1)$-approximation algorithms for the set cover problem in geometric settings, specifically, covering points by disks in the plane, or covering points by halfspaces in three dimensions. In the unweighted case, Agarwa
In this paper, we study two classic optimization problems: minimum geometric dominating set and set cover. Both the problems have been studied for different types of objects for a long time. These problems become APX-hard when the objects are axis-pa
In the dynamic minimum set cover problem, a challenge is to minimize the update time while guaranteeing close to the optimal $min(O(log n), f)$ approximation factor. (Throughout, $m$, $n$, $f$, and $C$ are parameters denoting the maximum number of se
We study metric data structures for curves in doubling spaces, such as trajectories of moving objects in Euclidean $mathbb{R}^d$, where the distance between two curves is measured using the discrete Frechet distance. We design data structures in an e
We consider the problem of maintaining an approximate maximum independent set of geometric objects under insertions and deletions. We present data structures that maintain a constant-factor approximate maximum independent set for broad classes of f