approximation algorithms 1deterministic algorithms 1doubling metrics 1minimum spanning tree 1runtime analysis 1
From the 1 of 3 linked papers with an AI index.
3 papers
cs.DS2026
A Fast and Simple -Approximation for Minimum Spanning Trees in Doubling Metrics
Jan Höckendorff, Jan Höckendorff, Felix Hommelsheim +2
The paper presents a deterministic algorithm that computes a (1+ε)-approximation of the minimum spanning tree in metric spaces with bounded doubling dimension, achieving a runtime…
cs.DS2026
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…
cs.DS2025
Dimension Reduction for Clustering: The Curious Case of Discrete Centers
Shaofeng H. -C. Jiang, Robert Krauthgamer, Shay Sapir +2
The Johnson-Lindenstrauss transform is a fundamental method for dimension reduction in Euclidean spaces, that can map any dataset of points into dimension with low…