An algorithm for finding the global maximum of a multimodal, multivariate function
Mathematical Programming: Series A and B
Power diagrams: properties, algorithms and applications
SIAM Journal on Computing
Algorithms in combinatorial geometry
Algorithms in combinatorial geometry
Voronoi diagrams—a survey of a fundamental geometric data structure
ACM Computing Surveys (CSUR)
Spatial tessellations: concepts and applications of Voronoi diagrams
Spatial tessellations: concepts and applications of Voronoi diagrams
Global optimization of univariate Lipschitz functions I: survey and properties
Mathematical Programming: Series A and B
Mathematical Programming: Series A and B
A deterministic algorithm for global optimization
Mathematical Programming: Series A and B
Locating An Undesirable Facility by Generalized Cutting Planes
Mathematics of Operations Research
Finding GM-estimators with global optimization techniques
Journal of Global Optimization
A D.C. biobjective location model
Journal of Global Optimization
Locating Objects in the Plane Using Global Optimization Techniques
Mathematics of Operations Research
Pattern classification with class probability output network
IEEE Transactions on Neural Networks
Journal of Global Optimization
A new linearization method for generalized linear multiplicative programming
Computers and Operations Research
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Covering methods constitute a broad class of algorithms for solving multivariate Global Optimization problems. In this note we show that, when the objective f is d.c. and a d.c. decomposition for f is known, the computational burden usually suffered by multivariate covering methods is significantly reduced. With this we extend to the (non-differentiable) d.c. case the covering method of Breiman and Cutler, showing that it is a particular case of the standard outer approximation approach. Our computational experience shows that this generalization yields not only more flexibility but also faster convergence than the covering method of Breiman-Cutler.