lsmear: a variable selection strategy for interval branch and bound solvers

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

4 Citas (Scopus)

Resumen

Smear-based variable selection strategies are well-known and commonly used by branch-and-prune interval-based solvers. They estimate the impact of the variables on each constraint of the system by using the partial derivatives and the sizes of the variable domains. Then they aggregate these values, in some way, to estimate the impact of each variable on the whole system. The variable with the greatest impact is then selected. A problem of these strategies is that they, generally, consider all constraints equally important. In this work, we propose a new variable selection strategy which first weights the constraints by using the optimal Lagrangian multipliers of a linearization of the original problem. Then, the impact of the variables is computed with a typical smear-based function but taking into account the weights of the constraints. The strategy isg tested on a set of well-known benchmark instances outperforming significantly the classical variable selection strategies.

Idioma originalInglés
Páginas (desde-hasta)483-500
Número de páginas18
PublicaciónJournal of Global Optimization
Volumen71
N.º3
DOI
EstadoPublicada - 1 jul 2018

Huella

Profundice en los temas de investigación de 'lsmear: a variable selection strategy for interval branch and bound solvers'. En conjunto forman una huella única.

Citar esto