Partitioning Vectors into Quadruples: Worst-Case Analysis of a Matching-Based Algorithm
Data Structures and Algorithms
2018-07-06 v1 Discrete Mathematics
Abstract
Consider a problem where 4k given vectors need to be partitioned into k clusters of four vectors each. A cluster of four vectors is called a quad, and the cost of a quad is the sum of the component-wise maxima of the four vectors in the quad. The problem is to partition the given 4k vectors into k quads with minimum total cost. We analyze a straightforward matching-based algorithm, and prove that this algorithm is a (3/2)-approximation algorithm for this problem. We further analyze the performance of this algorithm on a hierarchy of special cases of the problem, and prove that, in one particular case, the algorithm is a (5/4)-approximation algorithm. Our analysis is tight in all cases except one.
Cite
@article{arxiv.1807.01962,
title = {Partitioning Vectors into Quadruples: Worst-Case Analysis of a Matching-Based Algorithm},
author = {Annette M. C. Ficker and Thomas Erlebach and Matus Mihalak and Frits C. R. Spieksma},
journal= {arXiv preprint arXiv:1807.01962},
year = {2018}
}