Ddo, a Generic and Efficient Framework for MDD-Based Optimization

Ddo, a Generic and Efficient Framework for MDD-Based Optimization

Xavier Gillard, Pierre Schaus, Vianney Coppé

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence

This paper presents ddo, a generic and efficient library to solve constraint optimization problems with decision diagrams. To that end, our framework implements the branch-and-bound approach which has recently been introduced by Bergman et al., (2016) to solve dynamic programs to optimality. Our library allowed us to successfully reproduce the results of Bergman et al. for MISP, MCP and MAX2SAT while using a single generic library. As an additional benefit, our ddo library is able to exploit parallel computing for its purpose without imposing any constraint on the user (apart from memory safety). Ddo is released as an open source rust library (crate) alongside with its companion example programs to solve the aforementioned problems. To the best of our knowledge, this is the first public implementation of a generic library to solve combinatorial optimization problems with branch-and-bound MDD.
Keywords:
Constraints and Satisfiability: general
Knowledge Representation and Reasoning: general