Partitioning Vectors into Quadruples: Worst-Case Analysis of a Matching-Based Algorithm
Annette Ficker, Thomas Erlebach, Matúš Mihaľák, Frits C. R. Spieksma · arXiv (Cornell University) · 2018
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.