Optimization Bounds from Binary Decision Diagrams
Bounds on the optimal value are often indispensable for the practical solution of discrete optimization problems, particularly in the branching procedures used by constraint programming (CP) and integer programming solvers. Such bounds are frequently obtained by solving a continuous relaxation of the problem, perhaps a linear programming (LP) relaxation. In this paper, we explore an alternative strategy of obtaining bounds from a discrete relaxation, namely a binary decision diagram (BDD). Such a strategy is particularly suitable for CP, because BDDs provide enhanced propagation as well [2-5].
KeywordsBoolean Function Constraint Programming Linear Programming Relaxation Discrete Optimization Problem Binary Decision Diagram
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- 6.Akers, S.B.: Binary decision diagrams. IEEE Transactions on Computers C-27, 509–516 (1978)Google Scholar
- 8.Bryant, R.E.: Graph-based algorithms for boolean function manipulation. IEEE Transactions on Computers C-35, 677–691 (1986)Google Scholar
- 9.Hu, A.J.: Techniques for efficient formal verification using binary decision diagrams. Thesis CS-TR-95-1561, Stanford University, Department of Computer Science (December 1995)Google Scholar
- 11.Wegener, I.: Branching programs and binary decision diagrams: theory and applications. SIAM monographs on discrete mathematics and applications. Society for Industrial and Applied Mathematics (2000)Google Scholar
- 12.Grötschel, M., Lovász, L., Schrijver, A.: Geometric Algorithms and Combinatorial Optimization, vol. 2. Springer (1993)Google Scholar