SearcharxivSearch

arXiv · 2504.04556

Online Facility Assignments on Polygons

Abstract

We study the online facility assignment problem on regular polygons, where all sides are of equal length. The influence of specific geometric settings has remained mostly unexplored, even though classical online facility assignment problems have mainly dealt with linear and general metric spaces. We fill this gap by considering the following four basic geometric settings: equilateral triangles, rectangles, regular $n$-polygons, and circles. The facilities are situated at fixed positions on the boundary, and customers appear sequentially on the boundary. A customer needs to be assigned immediately without any information about future customer arrivals. We study a natural greedy algorithm. First, we study an equilateral triangle with three facilities at its corners; customers can appear anywhere on the boundary. We then analyze regular $n$-sided polygons, obtaining a competitive ratio of $2n-1$, showing that the algorithm performance degrades linearly with the number of corner points for polygons. For the circular configuration, the competitive ratio is $2n-1$ when the distance between two adjacent facilities is the same. And the competitive ratios are $n^2-n+1$ and $2^n - 1$ for varying distances linearly and exponentially respectively. Each facility has a fixed capacity proportional to the geometric configuration, and customers appear only along the boundary edges. Our results also show that simpler geometric configurations have more efficient performance bounds and that spacing facilities uniformly apart prevent worst-case scenarios. The findings have many practical implications because large networks of facilities are best partitioned into smaller and geometrically simple pieces to guarantee good overall performance.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Sumaiya Malik, Reyan Ahmed, Md. Manzurul Hasan. 2025-04-06. Online Facility Assignments on Polygons. https://arxiv.org/abs/2504.04556

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Quasi-Monte Carlo Beyond Hardy-Krause II: $(1 + \varepsilon)n$ Samples Suffice

Numerical integration studies how well one can estimate the integral of a function $f$ over $[0,1)^d$ using $n$ sample points. The two classical methods, Monte Carlo (MC) and quasi-Monte Carlo (QMC), have complementary strengths and weaknesses, and a fundamental question is to design an approach that combines the benefits of both. Recently, building on the transference principle in discrepancy theory, Bansal and Jiang~\cite{BJ25a} gave a randomized QMC method that bridges MC and QMC guarantees using only i.i.d.\ samples. Their method also goes beyond the classical Koksma--Hlawka inequality: it achieves integration error $\widetilde{O}_d(\sigma_{\mathsf{SO}}(f)/n)$, where the smoothed-out variation $\sigma_{\mathsf{SO}}(f)$ can be substantially smaller than the Hardy--Krause variation that governs the classical bound. However, their algorithm requires $n^2$ i.i.d.\ samples as input, and this quadratic blowup is inherent to any method based on the transference principle. In this work, we bypass the quadratic blowup: for any constant $\varepsilon > 0$, we show that $(1+\varepsilon)n$ i.i.d.\ samples suffice to both obtain the beyond-Hardy--Krause guarantee of~\cite{BJ25a}, resolving an open problem posed there, and to produce low-discrepancy point sequences. Our algorithms are variants of the online Haar-thinning method of Dwivedi, Feldheim, Gurel-Gurevich, and Ramdas~\cite{DFG+19}.

cs.DS

Single-Exponential Algorithms and a Polynomial Kernel for Strong Connectivity Augmentation

Strong Connectivity Augmentation (SCA) asks whether a directed acyclic graph can be made strongly connected by adding at most $k$ prescribed links whose total weight is within a given budget. Klinkby, Misra, and Saurabh (SODA 2021) gave an $O^*(2^{O(k\log k)})$-time algorithm and asked whether the problem admits a single-exponential parameterized algorithm and a polynomial kernel. We answer both questions affirmatively: SCA can be solved in $O^*(9^k)$ time and admits a polynomial kernel with $O(k^4)$ vertices and $O(k^{16})$ bits. For unweighted SCA, we obtain $O^*(4^k)$ time and a kernel with $O(k^3)$ vertices. Our algorithms are based on a particularly simple reduction to Strongly Connected Spanning Subgraph with two edge costs.

cs.DS