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arxiv: 1604.06715 · v1 · pith:X6PBXBOLnew · submitted 2016-04-22 · 💻 cs.AI · cs.CC

Parameterized Compilation Lower Bounds for Restricted CNF-formulas

classification 💻 cs.AI cs.CC
keywords sizeboundscnf-formulascompilationdnnfdnnf-encodingformulasgraph
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We show unconditional parameterized lower bounds in the area of knowledge compilation, more specifically on the size of circuits in decomposable negation normal form (DNNF) that encode CNF-formulas restricted by several graph width measures. In particular, we show that - there are CNF formulas of size $n$ and modular incidence treewidth $k$ whose smallest DNNF-encoding has size $n^{\Omega(k)}$, and - there are CNF formulas of size $n$ and incidence neighborhood diversity $k$ whose smallest DNNF-encoding has size $n^{\Omega(\sqrt{k})}$. These results complement recent upper bounds for compiling CNF into DNNF and strengthen---quantitatively and qualitatively---known conditional low\-er bounds for cliquewidth. Moreover, they show that, unlike for many graph problems, the parameters considered here behave significantly differently from treewidth.

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