Fast Balanced Partitioning is Hard, Even on Grids and Trees

TL;DR

This paper proves that achieving fast, high-quality balanced partitioning solutions is computationally hard even on simple graph classes like grids and trees, establishing fundamental inapproximability results.

Contribution

It introduces a reduction framework to prove the NP-hardness and inapproximability of balanced partitioning on grids and trees, including bicriteria inapproximability results.

Findings

NP-hard to approximate on grid graphs

No polynomial-time algorithm for near-equal sets

First bicriteria inapproximability results for the problem

Abstract

Two kinds of approximation algorithms exist for the k-BALANCED PARTITIONING problem: those that are fast but compute unsatisfying approximation ratios, and those that guarantee high quality ratios but are slow. In this paper we prove that this tradeoff between runtime and solution quality is necessary. For the problem a minimum number of edges in a graph need to be found that, when cut, partition the vertices into k equal-sized sets. We develop a reduction framework which identifies some necessary conditions on the considered graph class in order to prove the hardness of the problem. We focus on two combinatorially simple but very different classes, namely trees and solid grid graphs. The latter are finite connected subgraphs of the infinite 2D grid without holes. First we use the framework to show that for solid grid graphs it is NP-hard to approximate the optimum number of cut edges within any satisfying ratio. Then we consider solutions in which the sets may deviate from being equal-sized. Our framework is used on grids and trees to prove that no fully polynomial time algorithm exists that computes solutions in which the sets are arbitrarily close to equal-sized. This is true even if the number of edges cut is allowed to increase the more stringent the limit on the set sizes is. These are the first bicriteria inapproximability results for the problem.