Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 134 tok/s
Gemini 2.5 Pro 41 tok/s Pro
GPT-5 Medium 30 tok/s Pro
GPT-5 High 37 tok/s Pro
GPT-4o 98 tok/s Pro
Kimi K2 195 tok/s Pro
GPT OSS 120B 442 tok/s Pro
Claude Sonnet 4.5 37 tok/s Pro
2000 character limit reached

A Polynomial-Time Approximation Scheme for Facility Location on Planar Graphs (1904.10680v1)

Published 24 Apr 2019 in cs.DS

Abstract: We consider the classic Facility Location problem on planar graphs (non-uniform, uncapacitated). Given an edge-weighted planar graph $G$, a set of clients $C\subseteq V(G)$, a set of facilities $F\subseteq V(G)$, and opening costs $\mathsf{open} \colon F \to \mathbb{R}{\geq 0}$, the goal is to find a subset $D$ of $F$ that minimizes $\sum{c \in C} \min_{f \in D} \mathrm{dist}(c,f) + \sum_{f \in D} \mathsf{open}(f)$. The Facility Location problem remains one of the most classic and fundamental optimization problem for which it is not known whether it admits a polynomial-time approximation scheme (PTAS) on planar graphs despite significant effort for obtaining one. We solve this open problem by giving an algorithm that for any $\varepsilon>0$, computes a solution of cost at most $(1+\varepsilon)$ times the optimum in time $n{2{O(\varepsilon{-2} \log (1/\varepsilon))}}$.

Citations (10)

Summary

We haven't generated a summary for this paper yet.

Dice Question Streamline Icon: https://streamlinehq.com

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

Don't miss out on important new AI/ML research

See which papers are being discussed right now on X, Reddit, and more:

“Emergent Mind helps me see which AI papers have caught fire online.”

Philip

Philip

Creator, AI Explained on YouTube