Simple and Local Independent Set Approximation
March 02, 2018 Β· Declared Dead Β· π Colloquium on Structural Information & Communication Complexity
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Ravi B. Boppana, MagnΓΊs M. HalldΓ³rsson, Dror Rawitz
arXiv ID
1803.00786
Category
cs.DS: Data Structures & Algorithms
Citations
10
Venue
Colloquium on Structural Information & Communication Complexity
Last Checked
4 months ago
Abstract
We bound the performance guarantees that follow from TurΓ‘n-like bounds for unweighted and weighted independent sets in bounded-degree graphs. In particular, a randomized approach of Boppana forms a simple 1-round distributed algorithm, as well as a streaming and preemptive online algorithm. We show it gives a tight $(Ξ+1)/2$-approximation in unweighted graphs of maximum degree $Ξ$, which is best possible for 1-round distributed algorithms. For weighted graphs, it gives only a $Ξ$-approximation, but a simple modification results in an asymptotic expected $0.529 Ξ$-approximation. This compares with a recent, more complex $Ξ$-approximation~\cite{BCGS17}, which holds deterministically.
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