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Editors contains: "Gomes, L."

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  1. Gomes, L.; Lorenz, R. (Ed.)
    Efficient manipulation of binary or multi-valueddecision diagrams (BDDs or MDDs) is critical in symbolic verification tools. Despite the applicability of MDDs to real-world tasks such as discovering the reachable states of a model, their large demands on hardware resources, especially memory, limit algorithmic scalability. In this paper, we focus on memory-constrained algorithms that employ a novel O(m log n)-time under-approximation technique for MDDs, where m and n are the number of MDD edges and nodes, respectively. The effectiveness of our approach is demonstrated experimentally by a reduction in peak memory usage for the symbolic reachability computation of a set of Petri nets. 
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