GUB Covers and Power-Indexed formulations for Wireless Network Design
April 21, 2017 Β· Declared Dead Β· π Management Sciences
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Fabio D'Andreagiovanni, Carlo Mannino, Antonio Sassano
arXiv ID
1704.06674
Category
math.OC: Optimization & Control
Cross-listed
cs.DM,
cs.DS,
cs.NI
Citations
58
Venue
Management Sciences
Last Checked
5 months ago
Abstract
We propose a pure 0-1 formulation for the wireless network design problem, i.e. the problem of configuring a set of transmitters to provide service coverage to a set of receivers. In contrast with classical mixed integer formulations, where power emissions are represented by continuous variables, we consider only a finite set of powers values. This has two major advantages: it better fits the usual practice and eliminates the sources of numerical problems which heavily affect continuous models. A crucial ingredient of our approach is an effective basic formulation for the single knapsack problem representing the coverage condition of a receiver. This formulation is based on the GUB cover inequalities introduced by Wolsey (1990) and its core is an extension of the exact formulation of the GUB knapsack polytope with two GUB constraints. This special case corresponds to the very common practical situation where only one major interferer is present. We assess the effectiveness of our formulation by comprehensive computational results over realistic instances of two typical technologies, namely WiMAX and DVB-T.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Optimization & Control
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Local SGD Converges Fast and Communicates Little
R.I.P.
π»
Ghosted
On Lazy Training in Differentiable Programming
π
π
The Cartographer
A Review on Bilevel Optimization: From Classical to Evolutionary Approaches and Applications
R.I.P.
π»
Ghosted
Learned Primal-dual Reconstruction
R.I.P.
π»
Ghosted
On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transport
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