Quasipolynomial multicut-mimicking networks and kernelization of multiway cut problems
February 20, 2020 Β· Declared Dead Β· π International Colloquium on Automata, Languages and Programming
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Magnus WahlstrΓΆm
arXiv ID
2002.08825
Category
cs.DS: Data Structures & Algorithms
Citations
5
Venue
International Colloquium on Automata, Languages and Programming
Last Checked
4 months ago
Abstract
We show the existence of an exact mimicking network of $k^{O(\log k)}$ edges for minimum multicuts over a set of terminals in an undirected graph, where $k$ is the total capacity of the terminals, as well as a method for computing a mimicking network of quasipolynomial size in polynomial time. As a consequence of the latter, several problems are shown to have quasipolynomial kernels, including Edge Multiway Cut, Group Feedback Edge Set for an arbitrary group, and Edge Multicut parameterized by the solution and the number of cut requests. The result combines the matroid-based irrelevant edge approach used in the kernel for $s$-Multiway Cut with a recursive decomposition and sparsification of the graph along sparse cuts. This is the first progress on the kernelization of Multiway Cut problems since the kernel for $s$-Multiway Cut for constant value of $s$ (Kratsch and WahlstrΓΆm, FOCS 2012).
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