Red-Blue-Partitioned MST, TSP, and Matching
July 21, 2018 Β· Declared Dead Β· π Canadian Conference on Computational Geometry
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Matthew P. Johnson
arXiv ID
1807.08065
Category
cs.DS: Data Structures & Algorithms
Citations
1
Venue
Canadian Conference on Computational Geometry
Last Checked
4 months ago
Abstract
Arkin et al.~\cite{ArkinBCCJKMM17} recently introduced \textit{partitioned pairs} network optimization problems: given a metric-weighted graph on $n$ pairs of nodes, the task is to color one node from each pair red and the other blue, and then to compute two separate \textit{network structures} or disjoint (node-covering) subgraphs of a specified sort, one on the graph induced by the red nodes and the other on the blue nodes. Three structures have been investigated by \cite{ArkinBCCJKMM17}---\textit{spanning trees}, \textit{traveling salesperson tours}, and \textit{perfect matchings}---and the three objectives to optimize for when computing such pairs of structures: \textit{min-sum}, \textit{min-max}, and \textit{bottleneck}. We provide improved approximation guarantees and/or strengthened hardness results for these nine NP-hard problem settings.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Data Structures & Algorithms
π
π
The Cartographer
R.I.P.
π»
Ghosted
Route Planning in Transportation Networks
R.I.P.
π»
Ghosted
Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration
R.I.P.
π»
Ghosted
Hierarchical Clustering: Objective Functions and Algorithms
R.I.P.
π»
Ghosted
Graph Isomorphism in Quasipolynomial Time
π
π
The Cartographer
Simulation optimization: A review of algorithms and applications
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted