1 citations · 1 across the 8 of their papers we have counts for
5 papers · 1 filter
Connected Subspace Clustering: Hardness, a Scalable Heuristic, and an Application to Sea Level Geodesy
Johanna Hillebrand, Jan Höckendorff, Jürgen Kusche +5
Constrained optimization extends classical optimization by integrating side information, making it widely applicable across scientific and engineering domains. Consider a setting w…
A Fast and Simple -Approximation for Minimum Spanning Trees in Doubling Metrics
Jan Höckendorff, Felix Hommelsheim, Christian Sohler +1
The minimum spanning tree (MST) problem is one of the most basic optimization problems on metric spaces and graphs. We study the problem of computing a -approximation to the…
Time Series Decomposition using the Fréchet Distance
Anne Driemel, Jan Höckendorff, Ioannis Psarros +1
In this paper, we introduce a new data analysis problem that aims to decompose a set of univariate time series into a small set of base curves of length at most such that t…
Near Linear Time Approximation Schemes for Clustering of Partially Doubling Metrics
Anne Driemel, Jan Höckendorff, Ioannis Psarros +2
Given a finite metric space the -median problem is to find a set of centers that minimizes $\sum_{p\in X} \min_{c\in C} \mathbf{d}(p,c…
Learning to Approximate Uniform Facility Location via Graph Neural Networks
Chendi Qian, Christopher Morris, Stefanie Jegelka +1
Neural networks, particularly message-passing neural networks (MPNNs), are increasingly used as heuristics for hard combinatorial optimization problems. Yet many learning-based met…