Hardness and Approximation Algorithms for Balanced Districting Problems
Prathamesh Dharangutte, Jie Gao, Shang-En Huang, Fang-Yi Yu

TL;DR
This paper studies the computational complexity of balanced districting problems in graphs, providing hardness results and approximation algorithms for various graph classes, with applications to political redistricting.
Contribution
It introduces the balanced districting problem, proves its NP-hardness in multiple settings, and develops approximation algorithms with provable guarantees for specific graph classes.
Findings
NP-hardness of better than $n^{1/2- heta}$ approximation in general graphs
An $O( ext{sqrt}(n))$-approximation algorithm for star districting
An $O( ext{log} n)$ approximation for planar graphs
Abstract
We introduce and study the problem of balanced districting, where given an undirected graph with vertices carrying two types of weights (different population, resource types, etc) the goal is to maximize the total weights covered in vertex disjoint districts such that each district is a star or (in general) a connected induced subgraph with the two weights to be balanced. This problem is strongly motivated by political redistricting, where contiguity, population balance, and compactness are essential. We provide hardness and approximation algorithms for this problem. In particular, we show NP-hardness for an approximation better than for any constant in general graphs even when the districts are star graphs, as well as NP-hardness on complete graphs, tree graphs, planar graphs and other restricted settings. On the other hand, we develop an algorithm for…
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