Fast Balanced Partitioning is Hard, Even on Grids and Trees
Abstract
Two kinds of approximation algorithms exist for the kBALANCED 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 equalsized 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 NPhard to approximate the optimum number of cut edges within any satisfying ratio. Then we consider solutions in which the sets may deviate from being equalsized. 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 equalsized. 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.
 Publication:

arXiv eprints
 Pub Date:
 November 2011
 DOI:
 10.48550/arXiv.1111.6745
 arXiv:
 arXiv:1111.6745
 Bibcode:
 2011arXiv1111.6745F
 Keywords:

 Computer Science  Computational Complexity;
 Computer Science  Data Structures and Algorithms