A linear-time parameterized algorithm for computing the width of a DAG
July 15, 2020 Β· Declared Dead Β· π International Workshop on Graph-Theoretic Concepts in Computer Science
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Manuel CΓ‘ceres, Massimo Cairo, Brendan Mumey, Romeo Rizzi, Alexandru I. Tomescu
arXiv ID
2007.07575
Category
cs.DS: Data Structures & Algorithms
Citations
11
Venue
International Workshop on Graph-Theoretic Concepts in Computer Science
Last Checked
4 months ago
Abstract
The width $k$ of a directed acyclic graph (DAG) $G = (V, E)$ equals the largest number of pairwise non-reachable vertices. Computing the width dates back to Dilworth's and Fulkerson's results in the 1950s, and is doable in quadratic time in the worst case. Since $k$ can be small in practical applications, research has also studied algorithms whose complexity is parameterized on $k$. Despite these efforts, it is still open whether there exists a linear-time $O(f(k)(|V| + |E|))$ parameterized algorithm computing the width. We answer this question affirmatively by presenting an $O(k^24^k|V| + k2^k|E|)$ time algorithm, based on a new notion of frontier antichains. As we process the vertices in a topological order, all frontier antichains can be maintained with the help of several combinatorial properties, paying only $f(k)$ along the way. The fact that the width can be computed by a single $f(k)$-sweep of the DAG is a new surprising insight into this classical problem. Our algorithm also allows deciding whether the DAG has width at most $w$ in time $O(f(\min(w,k))(|V|+|E|))$.
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