Smaller Selection Networks for Cardinality Constraints Encoding
February 16, 2015 Β· Declared Dead Β· π International Conference on Principles and Practice of Constraint Programming
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
MichaΕ KarpiΕski, Marek PiotrΓ³w
arXiv ID
1502.04551
Category
cs.DS: Data Structures & Algorithms
Cross-listed
cs.DC
Citations
8
Venue
International Conference on Principles and Practice of Constraint Programming
Last Checked
4 months ago
Abstract
Selection comparator networks have been studied for many years. Recently, they have been successfully applied to encode cardinality constraints for SAT-solvers. To decrease the size of generated formula there is a need for constructions of selection networks that can be efficiently generated and produce networks of small sizes for the practical range of their two parameters: n - the number of inputs (boolean variables) and k - the number of selected items (a cardinality bound). In this paper we give and analyze a new construction of smaller selection networks that are based on the pairwise selection networks introduced by Codish and Zanon-Ivry. We prove also that standard encodings of cardinality constraints with selection networks preserve arc-consistency.
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