Name
Abstract | ||
|---|---|---|
Constraint programming is a powerful tool for modeling various problems in operations research. Its strength lies in the use of predicates, or global high-level constraints, on a few variables to efficiently model complex and varied problem structures. In this paper, we consider the predicate at-least. It bounds the number of variables in a set that may receive a specific value. This is a generalization of the standard logic condition expressed when the sum of binary variables is expressing a lower bound on the cardinality of a set. We have completely determined the convex hull representation of this predicate and provide a polynomial separation algorithm for inclusion in branch-and-bound integer programming software. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s13042-019-00935-4 | International Journal of Machine Learning and Cybernetics |
Keywords | Field | DocType |
Constraint programming, At-least predicate, Facet-inducing inequalities, Separation algorithm | Discrete mathematics,Polynomial,Upper and lower bounds,Constraint programming,Convex hull,Cardinality,Polytope,Integer programming,Classical logic,Mathematics | Journal |
Volume | Issue | ISSN |
10 | 12 | 1868-808X |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Niko Kaso | 1 | 0 | 0.34 |
| Serge Kruk | 2 | 10 | 1.64 |