embray / cryptominisat

An advanced SAT Solver

CryptoMiniSat SAT solver

This system provides CryptoMiniSat, an advanced SAT solver. The system has 3 interfaces: command-line, C++ library and python. The command-line interface takes a cnf as an input in the DIMACS format with the extension of XOR clauses. The C++ interface mimics this except that it allows for a more efficient system, with assumptions and multiple solve() calls. A C compatible wrapper is also provided. The python interface provides a high-level yet efficient API to use most of the C++ interface with ease.

When citing, always reference our SAT 2009 conference paper, bibtex record is here.

Docker usage

To run on file myfile.cnf:

docker pull msoos/cryptominisat
cat myfile.cnf | docker run --rm -i msoos/cryptominisat

To run on a hand-written CNF:

docker pull msoos/cryptominisat
echo "1 2 0" | docker run --rm -i msoos/cryptominisat

To run on the file /home/myfolder/myfile.cnf.gz by mounting it (may be faster):

docker pull msoos/cryptominisat
docker run --rm -v /home/myfolder/myfile.cnf.gz:/f msoos/cryptominisat f

To build and run locally:

git clone https://github.com/msoos/cryptominisat.git
cd cryptominisat
git submodule update --init
docker build -t cms .
cat myfile.cnf | docker run --rm -i cms

To build and run the web interface:

git clone https://github.com/msoos/cryptominisat.git
cd cryptominisat
git submodule update --init
docker build -t cmsweb -f Dockerfile.web .
docker run --rm -i -p 80:80 cmsweb

Compiling in Linux

To build and install, issue:

sudo apt-get install build-essential cmake
# not required but very useful
sudo apt-get install libzip-dev libboost-program-options-dev libm4ri-dev libsqlite3-dev
tar xzvf cryptominisat-version.tar.gz
cd cryptominisat-version
mkdir build && cd build
cmake ..
make
sudo make install
sudo ldconfig

Compiling in Mac OSX

First, you must get Homebew from https://brew.sh/ then:

brew install cmake boost zlib
tar xzvf cryptominisat-version.tar.gz
cd cryptominisat-version
mkdir build && cd build
cmake ..
make
sudo make install

Compiling in Windows

You will need python installed, then for Visual Studio 2015:

C:\> [ download cryptominisat-version.zip ]
C:\> unzip cryptominisat-version.zip
C:\> rename cryptominisat-version cms
C:\> cd cms
C:\cms> mkdir build
C:\cms> cd build

C:\cms\build> [ download http://sourceforge.net/projects/boost/files/boost/1.59.0/boost_1_59_0.zip ]
C:\cms\build> unzip boost_1_59_0.zip
C:\cms\build> mkdir boost_1_59_0_install
C:\cms\build> cd boost_1_59_0
C:\cms\build\boost_1_59_0> bootstrap.bat --with-libraries=program_options
C:\cms\build\boost_1_59_0> b2 --with-program_options address-model=64 toolset=msvc-14.0 variant=release link=static threading=multi runtime-link=static install --prefix="C:\cms\build\boost_1_59_0_install" > boost_install.out
C:\cms\build\boost_1_59_0> cd ..

C:\cms\build> git clone https://github.com/madler/zlib
C:\cms\build> cd zlib
C:\cms\build\zlib> git checkout v1.2.8
C:\cms\build\zlib> mkdir build
C:\cms\build\zlib> mkdir myinstall
C:\cms\build\zlib> cd build
C:\cms\build\zlib\build> cmake -G "Visual Studio 14 2015 Win64" -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=C:\cms\build\zlib\myinstall ..
C:\cms\build\zlib\build> msbuild /t:Build /p:Configuration=Release /p:Platform="x64" zlib.sln
C:\cms\build\zlib\build> msbuild INSTALL.vcxproj
C:\cms\build> cd ..\..

C:\cms\build> cmake -G "Visual Studio 14 2015 Win64" -DCMAKE_BUILD_TYPE=Release -DSTATICCOMPILE=ON -DZLIB_ROOT=C:\cms\build\zlib\myinstall -DBOOST_ROOT=C:\cms\build\boost_1_59_0_install ..
C:\cms\build> cmake --build --config Release .

You now have the static binary under C:\cms\build\Release\cryptominisat5.exe

Compiling under Cygwin64 in Windows

This is just a rough guide, but it should work. Compiling with Visual Studio may be easier, and better, though:

get boost from Boost.org e.g. boost_1_66_0.tar.gz
$ tar xzvf cryptominisat-version.tar.gz
$ cd cryptominisat-version
$ mkdir build
$ cd build
$ gunzip -c ../../boost_1_66_0.tar.gz | tar -xvof -
$ cd boost_1_66_0/
$ ./bootstrap.sh --with-libraries=program_options
$ ./b2
$ export BOOST_ROOT=$(pwd)
$ cd ..
$ cmake ..
$ make
$ make install
$ cp ./boost_1_66_0/bin.v2/libs/program_options/build/gcc-gnu-6.4.0/release/threadapi-pthread/threading-multi/cygboost_program_options.dll /usr/local/bin

Command-line usage

Let's take the file:

p cnf 3 3
1 0
-2 0
-1 2 3 0

The file has 3 variables and 3 clauses, this is reflected in the header p cnf 3 3 which gives the number of variables as the first number and the number of clauses as the second. Every clause is ended by '0'. The clauses say: 1 must be True, 2 must be False, and either 1 has to be False, 2 has to be True or 3 has to be True. The only solution to this problem is:

cryptominisat5 --verb 0 file.cnf
s SATISFIABLE
v 1 -2 3 0

Which means, that setting variable 1 True, variable 2 False and variable 3 True satisfies the set of constraints (clauses) in the CNF. If the file had contained:

p cnf 3 4
1 0
-2 0
-3 0
-1 2 3 0

Then there is no solution and the solver returns s UNSATISFIABLE.

Python usage

The python module works with both Python 2 and Python 3. It must be compiled as per (notice "python-dev"):

sudo apt-get install build-essential cmake
sudo apt-get install libzip-dev libboost-program-options-dev libm4ri-dev libsqlite3-dev
sudo apt-get install python3-setuptools python3-dev
tar xzvf cryptominisat-version.tar.gz
cd cryptominisat-version
mkdir build && cd build
cmake ..
make
sudo make install
sudo ldconfig

You can then use it as:

>>> from pycryptosat import Solver
>>> s = Solver()
>>> s.add_clause([1])
>>> s.add_clause([-2])
>>> s.add_clause([3])
>>> s.add_clause([-1, 2, 3])
>>> sat, solution = s.solve()
>>> print sat
True
>>> print solution
(None, True, False, True)

We can also try to assume any variable values for a single solver run:

>>> sat, solution = s.solve([-3])
>>> print sat
False
>>> print solution
None
>>> sat, solution = s.solve()
>>> print sat
True
>>> print solution
(None, True, False, True)

For more detailed usage instructions, please see the README.rst under the python directory.

Library usage

The library uses a variable numbering scheme that starts from 0. Since 0 cannot be negated, the class Lit is used as: Lit(variable_number, is_negated). As such, the 1st CNF above would become:

#include <cryptominisat5/cryptominisat.h>
#include <assert.h>
#include <vector>
using std::vector;
using namespace CMSat;

int main()
{
    SATSolver solver;
    vector<Lit> clause;

    //Let's use 4 threads
    solver.set_num_threads(4);

    //We need 3 variables
    solver.new_vars(3);

    //adds "1 0"
    clause.push_back(Lit(0, false));
    solver.add_clause(clause);

    //adds "-2 0"
    clause.clear();
    clause.push_back(Lit(1, true));
    solver.add_clause(clause);

    //adds "-1 2 3 0"
    clause.clear();
    clause.push_back(Lit(0, true));
    clause.push_back(Lit(1, false));
    clause.push_back(Lit(2, false));
    solver.add_clause(clause);

    lbool ret = solver.solve();
    assert(ret == l_True);
    assert(solver.get_model()[0] == l_True);
    assert(solver.get_model()[1] == l_False);
    assert(solver.get_model()[2] == l_True);
    std::cout
    << "Solution is: "
    << solver.get_model()[0]
    << ", " << solver.get_model()[1]
    << ", " << solver.get_model()[2]
    << std::endl;

    return 0;
}

The library usage also allows for assumptions. We can add these lines just before the return 0; above:

vector<Lit> assumptions;
assumptions.push_back(Lit(2, true));
lbool ret = solver.solve(&assumptions);
assert(ret == l_False);

lbool ret = solver.solve();
assert(ret == l_True);

Since we assume that variabe 2 must be false, there is no solution. However, if we solve again, without the assumption, we get back the original solution. Assumptions allow us to assume certain literal values for a specific run but not all runs -- for all runs, we can simply add these assumptions as 1-long clauses.

Multiple solutions

To find multiple solutions to your problem, just run the solver in a loop and ban the previous solution found:

while(true) {
    lbool ret = solver->solve();
    if (ret != l_True) {
        assert(ret == l_False);
        //All solutions found.
        exit(0);
    }

    //Use solution here. print it, for example.

    //Banning found solution
    vector<Lit> ban_solution;
    for (uint32_t var = 0; var < solver->nVars(); var++) {
        if (solver->get_model()[var] != l_Undef) {
            ban_solution.push_back(
                Lit(var, (solver->get_model()[var] == l_True)? true : false));
        }
    }
    solver->add_clause(ban_solution);
}

The above loop will run as long as there are solutions. It is highly suggested to only add into the new clause(bad_solutions above) the variables that are "important" or "main" to your problem. Variables that were only used to translate the original problem into CNF should not be added. This way, you will not get spurious solutions that don't differ in the main, important variables.

Preprocessor usage

Run cryptominisat5 as:

./cryptominisat5 -p1 input.cnf simplified.cnf
some_sat_solver simplified.cnf > output
./cryptominisat5 -p2 output

where some_sat_solver is a SAT solver of your choice that outputs a solution in the format of:

s SATISFIABLE
v [solution] 0

or

s UNSATISFIABLE

You can tune the schedule of simplifications by issuing --sched "X,Y,Z...". The default schedule for preprocessing is:

handle-comps,scc-vrepl, cache-clean, cache-tryboth,sub-impl, intree-probe, probe,
sub-str-cls-with-bin, distill-cls, scc-vrepl, sub-impl,occ-backw-sub-str,
occ-xor, occ-clean-implicit, occ-bve, occ-bva, occ-gates,str-impl, cache-clean,
sub-str-cls-with-bin, distill-cls, scc-vrepl, sub-impl,str-impl, sub-impl,
sub-str-cls-with-bin, occ-backw-sub-str, occ-bve,check-cache-size, renumber

It is a good idea to put renumber as late as possible, as it renumbers the variables for memory usage reduction.

Gaussian elimination

For building with Gaussian Elimination, you need to build as per:

sudo apt-get install build-essential cmake
sudo apt-get install libzip-dev libboost-program-options-dev libm4ri-dev libsqlite3-dev
tar xzvf cryptominisat-version.tar.gz
cd cryptominisat-version
mkdir build && cd build
cmake -DUSE_GAUSS=ON ..
make
sudo make install

To use Gaussian elimination, provide a CNF with xors in it (either in CNF or XOR+CNF form) and tune the gaussian parameters. Use --hhelp to find all the gaussian elimination options:

Gauss options:
  --iterreduce arg (=1)       Reduce iteratively the matrix that is updated.We
                              effectively are moving the start to the last
                              column updated
  --maxmatrixrows arg (=3000) Set maximum no. of rows for gaussian matrix. Too
                              large matrixes should be discarded for reasons of
                              efficiency
  --autodisablegauss arg (=1) Automatically disable gauss when performing badly
  --minmatrixrows arg (=5)    Set minimum no. of rows for gaussian matrix.
                              Normally, too small matrixes are discarded for
                              reasons of efficiency
  --savematrix arg (=2)       Save matrix every Nth decision level
  --maxnummatrixes arg (=3)   Maximum number of matrixes to treat.

If any of these options seem to be non-existent, then either you forgot to compile the SAT solver with the above options, or you forgot to re-install it with sudo make install.

Testing

For testing you will need the GIT checkout and build as per:

sudo apt-get install build-essential cmake git
sudo apt-get install libzip-dev libboost-program-options-dev libm4ri-dev libsqlite3-dev
sudo apt-get install git python3-pip python3-setuptools python3-dev
sudo pip3 install --upgrade pip
sudo pip3 install lit
git clone https://github.com/msoos/cryptominisat.git
cd cryptominisat
git submodule update --init
mkdir build && cd build
cmake -DENABLE_TESTING=ON ..
make -j4
make test
sudo make install
sudo ldconfig

Fuzzing

Build for test as per above, then:

cd ../cryptominisat/scripts/fuzz/
./fuzz_test.py

Using the Machine Learning System

This is experimental but should work relatively well:

git checkout clauseID
sudo apt-get install build-essential cmake git
sudo apt-get install libzip-dev libboost-program-options-dev libm4ri-dev libsqlite3-dev
sudo apt-get install graphviz
sudo apt-get install python3-pip python3-setuptools python3-dev
sudo apt-get install python3-numpy
sudo pip3 install --upgrade pip
sudo pip3 install lit
sudo pip3 install scikit-learn pandas scipy
git clone https://github.com/msoos/cryptominisat.git
cd cryptominisat
git submodule update --init
mkdir build && cd build
ln -s ../scripts/build_scripts/* .
ln -s ../scripts/learn/* .
./build_stats.sh
sudo make install
sudo ldconfig
./test_predict.sh

The prediction datas are now written to the directory build/test_predict/. You can use e.g. Weka to examine the CSV found there. Please note that this is under heavy development

Configuring a build for a minimal binary&library

The following configures the system to build a bare minimal binary&library. It needs a compiler, but nothing much else:

cmake -DONLY_SIMPLE=ON -DNOZLIB=ON -DNOM4RI=ON -DSTATS=OFF -DNOVALGRIND=ON -DENABLE_TESTING=OFF .

Trying different configurations

Try solving using different reconfiguration values between 1..15 as per:

./cryptominisat5 --reconfat 0 --reconf 1 my_hard_problem.cnf
./cryptominisat5 --reconfat 0 --reconf 2 my_hard_problem.cnf
...
./cryptominisat5 --reconfat 0 --reconf 15 my_hard_problem.cnf

These configurations are designed to be relatively orthogonal. Check if any of them solve a lot faster. If it does, try using that for similar problems going forward. Please do come back to the author with what you have found to work best for you.

C usage

See src/cryptominisat_c.h.in for details. This is an experimental feature.