Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
Efficient Intelligent Backtracking Using Linear Programming
INFORMS Journal on Computing
Constraint Processing
Propagation via lazy clause generation
Constraints
CPAIOR '09 Proceedings of the 6th International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Constraint integer programming: a new approach to integrate CP and MIP
CPAIOR'08 Proceedings of the 5th international conference on Integration of AI and OR techniques in constraint programming for combinatorial optimization problems
Operations Research Letters
Conflict analysis in mixed integer programming
Discrete Optimization
| Hi-index | 0.00 |
Learning during search allows solvers for discrete optimization problems to remember parts of the search that they have already performed and avoid revisiting redundant parts. Learning approaches pioneered by the SAT and CP communities have been successfully incorporated into the SCIP constraint integer programming platform. In this paper we show that performing a heuristic constraint programming search during root node processing of a binary program can rapidly learn useful nogoods, bound changes, primal solutions, and branching statistics that improve the remaining IP search.