Mixed Global Constraints and Inference in Hybrid CLP–IP Solvers

  • Authors:

  • Greger Ottosson;Erlendur S. Thorsteinsson;John N. Hooker

  • Affiliations:

  • Computing Science Department, Uppsala University, P.O. Box 311, S-75105 Uppsala, Sweden E-mail: greger@acm.org;Graduate School of Industrial Administration, Carnegie Mellon University, Pittsburgh, PA 15213, USA E-mail: esth@cmu.edu;Graduate School of Industrial Administration, Carnegie Mellon University, Pittsburgh, PA 15213, USA E-mail: jh38@cmu.edu

  • Venue:
  • Annals of Mathematics and Artificial Intelligence
  • Year:
  • 2002

Quantified Score

Hi-index 0.00

Visualization

Abstract

The complementing strengths of Constraint (Logic) Programming (CLP) and Mixed Integer Programming (IP) have recently received significant attention. Although various optimization and constraint programming packages at a first glance seem to support mixed models, the modeling and solution techniques encapsulated are still rudimentary. Apart from exchanging bounds for variables and objective, little is known of what constitutes a good hybrid model and how a hybrid solver can utilize the complementary strengths of inference and relaxations. This paper adds to the field by identifying constraints as the essential link between CLP and IP and introduces an algorithm for bidirectional inference through these constraints. Together with new search strategies for hybrid solvers and cut-generating mixed global constraints, solution speed is improved over both traditional IP codes and newer mixed solvers.