This is a modified version of SharpSAT-TD that can compile CNFs into sd-DNNFs. Use with

./sharpSAT -dDNNF -decot 1 -decow 100 -tmpdir . -cs 3500 in.cnf -dDNNF_out out.nnf

to compile the cnf in.cnfinto an sd-DNNF saved in the file out.cnf. Without -dDNNF_out the sd-DNNF is printed to stdout.

For this to work, the input CNF must have weight annotations of the form c p weight <lit> <lit> 0 for each literal 0 < abs(lit) <= #vars.

Submission to model counting competition 2021 by Tuukka Korhonen and Matti Järvisalo (University of Helsinki). SharpSAT-TD is based on SharpSAT, with the main new features being the use of tree decompositions in decision heuristics, new preprocessor, and directly supporting weighted model counting.

SharpSAT-TD supports exact model counting, exact weighted model counting with arbitrary precision floats, and exact weighted model counting with doubles. See a detailed description in description.pdf.

The external dependencies needed are the GMP library, the MPFR library, and CMAKE.

To compile and link dynamically use

./setupdev.sh

To compile and link statically use

./setupdev.sh static

The binaries sharpSAT and flow_cutter_pace17 will be copied to the bin/ directory.

The currently supported input/output formats are those of Model counting competition 2021.

Example unweighted model counting: cd bin ./sharpSAT -decot 1 -decow 100 -tmpdir . -cs 3500 ../examples/track1_009.cnf

Example weighted model counting with arbitrary precision: cd bin ./sharpSAT -WE -decot 1 -decow 100 -tmpdir . -cs 3500 -prec 20 ../examples/track2_003.wcnf

Example weighted model counting with double precision: cd bin ./sharpSAT -WD -decot 1 -decow 100 -tmpdir . -cs 3500 ../examples/track2_003.wcnf

In the competition setting the value of the -decot flag was 120.

• -decot - the number of seconds to run flowcutter to find a tree decomposition. Required. Recommended value 60-600 if running with a total time budjet of 1800-3600 seconds.
• -tpmdir - the directory to store temporary files for running flowcutter. Required.
• -decow - the weight of the tree decomposition in the decision heuristic. Recommended value >1 if the heuristic should care about the tree decomposition.
• -cs - limit of the cache size. If the memory upper bound is X megabytes, then the value here should be around x/2-500.
• -WE - enable weighted model counting with arbitrary precision.
• -WD - enable weighted model counting with double precision.
• -prec - the number of digits in output of weighted model counting. Does not affect the internal precision.