Max Weight Independent Set in sparse graphs with no long claws
September 29, 2023 Β· Declared Dead Β· π Symposium on Theoretical Aspects of Computer Science
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Tara Abrishami, Maria Chudnovsky, Cemil Dibek, Marcin Pilipczuk, PaweΕ RzΔ
ΕΌewski
arXiv ID
2309.16995
Category
cs.DS: Data Structures & Algorithms
Cross-listed
cs.DM
Citations
4
Venue
Symposium on Theoretical Aspects of Computer Science
Last Checked
4 months ago
Abstract
We revisit the recent polynomial-time algorithm for the MAX WEIGHT INDEPENDENT SET (MWIS) problem in bounded-degree graphs that do not contain a fixed graph whose every component is a subdivided claw as an induced subgraph [Abrishami, Dibek, Chudnovsky, RzΔ
ΕΌewski, SODA 2022]. First, we show that with an arguably simpler approach we can obtain a faster algorithm with running time $n^{\mathcal{O}(Ξ^2)}$, where $n$ is the number of vertices of the instance and $Ξ$ is the maximum degree. Then we combine our technique with known results concerning tree decompositions and provide a polynomial-time algorithm for MWIS in graphs excluding a fixed graph whose every component is a subdivided claw as an induced subgraph, and a fixed biclique as a subgraph.
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