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 descriptions in description.pdf and in arXiv. If you use SharpSAT-TD in a research paper, please cite at least one of (Korhonen and Järvisalo, CP 2021) or (Korhonen and Järvisalo, arXiv 2023).
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 2024.
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
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. WARNING: With this flag there may be large errors in the output.-prec- the number of digits in output of weighted model counting. Does not affect the internal precision.