On the Relative Efficiency of Dynamic and Static Top-Down Compilation to Decision-DNNF

Abstract

Top-down compilers of CNF formulas to circuits in decision-DNNF (Decomposable Negation Normal Form) have proved to be useful for model counting. These compilers rely on a common set of techniques including DPLL-style exploration of the set of models, caching of residual formulas, and connected components detection. Differences between compilers lie in the variable selection heuristics and in the additional processing techniques they may use. We investigate, from a theoretical perspective, the ability of top-down compilation algorithms to find small decision-DNNF circuits for two different variable selection strategies. Both strategies are guided by a graph of the CNF formula and are inspired by what is done in practice. The first uses a dynamic graph-partitioning approach while the second works with a static tree decomposition. We show that the dynamic approach performs significantly better than the static approach for some formulas, and that the opposite also holds for other formulas. Our lower bounds are proved despite loose settings where the compilation algorithm is only forced to follow its designed variable selection strategy and where everything else, including the many opportunities for tie-breaking, can be handled non-deterministically.