Lagrangean relaxation with clusters for point-feature cartographic label placement problems

Authors:
Glaydston Mattos Ribeiro;Luiz Antonio Nogueira Lorena
Affiliations:
LAC - Computer and Applied Mathematics Laboratory, INPE - Brazilian Space Research Institute, 12.227-010, São José dos Campos - SP, Brazil;LAC - Computer and Applied Mathematics Laboratory, INPE - Brazilian Space Research Institute, 12.227-010, São José dos Campos - SP, Brazil
Venue:
Computers and Operations Research
Year:
2008

Citing 12
Cited 4

Discrete optimization

Discrete optimization
The solution of large 0–1 integer programming problems encountered in automated cartography

Operations Research
Placing text labels on maps and diagrams

Graphics gems IV
An empirical study of algorithms for point-feature label placement

ACM Transactions on Graphics (TOG)
Solving the anti-covering location problem using Lagrangian relaxation

Computers and Operations Research
Multilevel k-way partitioning scheme for irregular graphs

Journal of Parallel and Distributed Computing
Tabu Search Heuristic for Point-Feature Cartographic Label Placement

Geoinformatica
Determining DNA Sequence Similarity Using Maximum Independent Set Algorithms for Interval Graphs

SWAT '92 Proceedings of the Third Scandinavian Workshop on Algorithm Theory
Optimized planning of frequency hopping in cellular networks

Computers and Operations Research
Harvest Scheduling Subject to Maximum Area Restrictions: Exploring Exact Approaches

Operations Research
Constructive Genetic Algorithm for Clustering Problems

Evolutionary Computation
Letting ants labeling point features [sic.: for 'labeling' read 'label']

CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02

A greedy randomized adaptive search procedure for the point-feature cartographic label placement

Computers & Geosciences
A decomposition approach for the probabilistic maximal covering location-allocation problem

Computers and Operations Research
A new mathematical model and a Lagrangean decomposition for the point-feature cartographic label placement problem

Computers and Operations Research
Dispersion for the point-feature cartographic label placement problem

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.01

Visualization

Abstract

This paper presents two new mathematical formulations for the point-feature cartographic label placement problem (PFCLP) and a new Lagrangean relaxation with clusters (LagClus) to provide bounds to these formulations. The PFCLP can be represented by a conflict graph and the relaxation divides the graph in small subproblems (clusters) that are easily solved. The edges connecting clusters are relaxed in a Lagrangean way and a subgradient algorithm improves the bounds. The LagClus was successfully applied to a set of instances up to 1000 points providing the best results of those reported in the literature.