Streaming Facility Location in High Dimension via Geometric Hashing

In Euclidean Uniform Facility Location, the input is a set of clients in \mathrm{R}^{d} and the goal is to place facilities to serve them, so as to minimize the total cost of opening facilities plus connecting the clients. We study the classical setting of dynamic geometric streams, where the client...

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Vydáno v:Proceedings / annual Symposium on Foundations of Computer Science s. 450 - 461
Hlavní autoři: Czumaj, Artur, Jiang, Shaofeng H.-C., Krauthgamer, Robert, Vesely, Pavel, Yang, Mingwei
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.10.2022
Témata:
Approximation algorithms
Complexity theory
Computer science
Costs
facility location
hash functions
Heuristic algorithms
high dimension
Monte Carlo methods
streaming algorithms
sublinear algorithms
ISSN:2575-8454
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Shrnutí:In Euclidean Uniform Facility Location, the input is a set of clients in \mathrm{R}^{d} and the goal is to place facilities to serve them, so as to minimize the total cost of opening facilities plus connecting the clients. We study the classical setting of dynamic geometric streams, where the clients are presented as a sequence of insertions and deletions of points in the grid \{1,ldots\,\Delta \}^{d}, and we focus on the high-dimensional regime, where the algorithm's space complexity must be polynomial (and certainly not exponential) in d \cdot \log \Delta.We present a new algorithmic framework, based on importance sampling from the stream, for O(1)-approximation of the optimal cost using only poly (d\cdot\log\Delta) space. This framework is easy to implement in two passes, one for sampling points and the other for estimating their contribution. Over random-order streams, we can extend this to a one-pass algorithm by using the two halves of the stream separately. Our main result, for arbitrary-order streams, computes O(d^{1.5})-approximation in one pass by using the new framework but combining the two passes differently. This improves upon previous algorithms that either need space exponential in d or only guarantee O(d\cdot\log^{2}\Delta)-approximation, and therefore our algorithms for high-dimensional streams are the first to avoid the O(\log\Delta) factor in approximation that is inherent to the widely-used quadtree decomposition. Our improvement is achieved by employing a geometric hashing scheme that maps points in \mathbb{R}^{d} into buckets of bounded diameter, with the key property that every point set of small-enough diameter is hashed into at most poly (d) distinct buckets.Finally, we complement our results with a proof that every streaming 1.085-approximation algorithm requires space exponential in poly (d \cdot log \Delta), even for insertion-only streams.
ISSN:2575-8454
DOI:10.1109/FOCS54457.2022.00050