# Approximating activation edge-cover and facility location problems

## TL;DR

This paper introduces a unified approach to approximation algorithms for the Activation Edge-Cover and Facility Location problems, achieving improved ratios and extending known results through a generic algorithm for minimizing combined set functions.

## Contribution

It presents a new generic algorithm for minimizing sums of decreasing and sub-additive set functions, leading to improved approximation ratios for several problems including Facility Location and Min-Power Edge-Cover.

## Key findings

- Achieves approximation ratio of approximately ln(θ) - ln(ln(θ)) for the Activation Edge-Cover problem.
- Improves the ratio for Min-Power Edge-Cover to less than 1.2785.
- Provides better ratios for unit threshold variants of the problems.

## Abstract

What approximation ratio can we achieve for the Facility Location problem if whenever a client $u$ connects to a facility $v$,the opening cost of $v$ is at most $\theta$ times the service cost of $u$? We show that this and many other problems are a particular case of the Activation Edge-Cover problem. Here we are given a multigraph $G=(V,E)$, a set $R \subseteq V$ of terminals, and thresholds $\{t^e_u,t^e_v\}$ for each $uv$-edge $e \in E$. The goal is to find an assignment ${\bf a}=\{a_v:v \in V\}$ to the nodes minimizing $\sum_{v \in V} a_v$, such that the edge set $E_{\bf a}=\{e=uv: a_u \geq t^e_u, a_v \geq t^e_v\}$ activated by ${\bf a}$ covers $R$. We obtain ratio $1+\omega(\theta) \approx \ln \theta-\ln \ln \theta$ for the problem, where $\omega(\theta)$ is the root of the equation $x+1=\ln(\theta/x)$ and $\theta$ is a problem parameter. This result is based on a simple generic algorithm for the problem of minimizing a sum of a decreasing and a sub-additive set functions, which is of independent interest. As an application, we get that the above variant of Facility Location admits ratio $1+\omega(\theta)$; if for each facility all service costs are identical then we show a better ratio $\displaystyle 1+\max_{k \geq 1} \frac{H_k-1}{1+k/\theta}$, where $H_k=\sum_{i=1}^k 1/i$. For the Min-Power Edge-Cover problem we improve the ratio $1.406$ of Calinescu et. al. (achieved by iterative randomized rounding) to $1+\omega(1)<1.2785$. For unit thresholds we improve the ratio $73/60 \approx 1.217$ to $\frac{1555}{1347} \approx 1.155$.