The $k$-Leaf Spanning Tree Problem Admits a Klam Value of 39
February 26, 2015 Β· Declared Dead Β· π International Workshop on Combinatorial Algorithms
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Meirav Zehavi
arXiv ID
1502.07725
Category
cs.DS: Data Structures & Algorithms
Citations
1
Venue
International Workshop on Combinatorial Algorithms
Last Checked
4 months ago
Abstract
Given an undirected graph $G$ and a parameter $k$, the $k$-Leaf Spanning Tree ($k$-LST) problem asks if $G$ contains a spanning tree with at least $k$ leaves. This problem has been extensively studied over the past three decades. In 2000, Fellows et al. [FSTTCS'00] explicitly asked whether it admits a klam value of 50. A steady progress towards an affirmative answer continued until 5 years ago, when an algorithm of klam value 37 was discovered. In this paper, we present an $O^*(3.188^k)$-time parameterized algorithm for $k$-LST, which shows that the problem admits a klam value of 39. Our algorithm is based on an interesting application of the well-known bounded search trees technique, where the correctness of rules crucially depends on the history of previously applied rules in a non-standard manner.
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