Arborescences and Shortest Path Trees when Colors Matter
March 11, 2024 Β· Declared Dead Β· π Theoretical Computer Science
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
P. S. Ardra, Jasine Babu, Kritika Kashyap, R. Krithika, Sreejith K. Pallathumadam, Deepak Rajendraprasad
arXiv ID
2403.06580
Category
cs.DS: Data Structures & Algorithms
Cross-listed
cs.CC,
cs.DM
Citations
1
Venue
Theoretical Computer Science
Last Checked
4 months ago
Abstract
Color-constrained subgraph problems are those where we are given an edge-colored (directed or undirected) graph and the task is to find a specific type of subgraph, like a spanning tree, an arborescence, a single-source shortest path tree, a perfect matching etc., with constraints on the number of edges of each color. Some of these problems, like color-constrained spanning tree, have elegant solutions and some of them, like color-constrained perfect matching, are longstanding open questions. In this work, we study color-constrained arborescences and shortest path trees. Computing a color-constrained shortest path tree on weighted digraphs turns out to be NP-hard in general but polynomial-time solvable when all cycles have positive weight. This polynomial-time solvability is due to the fact that the solution space is essentially the set of all color-constrained arborescences of a directed acyclic subgraph of the original graph. While finding color-constrained arborescence of digraphs is NP-hard in general, we give efficient algorithms when the input graph is acyclic. Consequently, a color-constrained shortest path tree on weighted digraphs having only positive weight cycles can be efficiently computed. Our algorithms also generalize to the problem of finding a color-constrained shortest path tree with minimum total weight. En route, we sight nice connections to colored matroids and color-constrained bases.
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