5 citations · 5 across the 4 of their papers we have counts for
6 papers
Approximation Algorithms for the Traveling Thief Problem
Jan Eube, Kelin Luo, Heiko Röglin +1
The Traveling Thief Problem (TTP) combines the Traveling Salesperson Problem with the Knapsack Problem. In this problem, a finite metric space is given, and at each location an ite…
Effective Traveling for Metric Instances of the Traveling Thief Problem
Jan Eube, Kelin Luo, Aneta Neumann +2
The Traveling Thief Problem (TTP) is a multi-component optimization problem that captures the interplay between routing and packing decisions by combining the classical Traveling S…
New Algorithms and Hardness Results for Connected Clustering
Jan Eube, Heiko Röglin
Connected clustering denotes a family of constrained clustering problems in which we are given a distance metric and an undirected connectivity graph that can be completely unr…
Connected k-Median with Disjoint and Non-disjoint Clusters
Jan Eube, Kelin Luo, Dorian Reineccius +2
The connected -median problem is a constrained clustering problem that combines distance-based -clustering with connectivity information. The problem allows to input a metric…
Connected k-Center and k-Diameter Clustering
Lukas Drexler, Jan Eube, Kelin Luo +4
Motivated by an application from geodesy, we introduce a novel clustering problem which is a -center (or k-diameter) problem with a side constraint. For the side constraint, we…
Noisy, Greedy and Not So Greedy k-means++
Anup Bhattacharya, Jan Eube, Heiko Röglin +1
The k-means++ algorithm due to Arthur and Vassilvitskii has become the most popular seeding method for Lloyd's algorithm. It samples the first center uniformly at random from the d…