Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
Approximating the bandwidth via volume respecting embeddings
Journal of Computer and System Sciences - 30th annual ACM symposium on theory of computing
Techniques for Efficient Formal Verification Using Binary Decision Diagrams
Techniques for Efficient Formal Verification Using Binary Decision Diagrams
Integrated Methods for Optimization (International Series in Operations Research & Management Science)
IEEE Transactions on Computers
Approximate Compilation of Constraints into Multivalued Decision Diagrams
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
A constraint store based on multivalued decision diagrams
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
On threshold BDDs and the optimal variable ordering problem
COCOA'07 Proceedings of the 1st international conference on Combinatorial optimization and applications
A systematic approach to MDD-based constraint programming
CP'10 Proceedings of the 16th international conference on Principles and practice of constraint programming
BDDs in a branch and cut framework
WEA'05 Proceedings of the 4th international conference on Experimental and Efficient Algorithms
Variable ordering for the application of BDDs to the maximum independent set problem
CPAIOR'12 Proceedings of the 9th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Oblivious bounds on the probability of boolean functions
ACM Transactions on Database Systems (TODS)
BDD-based heuristics for binary optimization
Journal of Heuristics
Hi-index | 0.00 |
We study the application of limited-width MDDs (multivalued decision diagrams) as discrete relaxations for combinatorial optimization problems. These relaxations are used for the purpose of generating lower bounds. We introduce a new compilation method for constructing such MDDs, as well as algorithms that manipulate the MDDs to obtain stronger relaxations and hence provide stronger lower bounds. We apply our methodology to set covering problems, and evaluate the strength of MDD relaxations to relaxations based on linear programming. Our experimental results indicate that the MDD relaxation is particularly effective on structured problems, being able to outperform state-of-the-art integer programming technology by several orders of magnitude.