https://github.com/meelgroup/arjun
CNF minimizer and minimal independent set minimizer
https://github.com/meelgroup/arjun
cnf dimacs-cnf model-counting simplifier
Last synced: about 1 month ago
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CNF minimizer and minimal independent set minimizer
- Host: GitHub
- URL: https://github.com/meelgroup/arjun
- Owner: meelgroup
- License: other
- Created: 2019-09-07T15:33:27.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2026-04-02T22:51:14.000Z (about 1 month ago)
- Last Synced: 2026-04-03T04:51:42.419Z (about 1 month ago)
- Topics: cnf, dimacs-cnf, model-counting, simplifier
- Language: C++
- Homepage:
- Size: 1.75 MB
- Stars: 26
- Watchers: 3
- Forks: 8
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
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# Arjun
A minimal independent set calculator and CNF minimizer that uses a combination
of steps to simplify a CNF such that its count stays the same. Name is taken
from the Hindu myth where [Arjun](https://en.wikipedia.org/wiki/Arjuna) is
known for being the "one who concentrates the most". This system is also used
as a preprocessor for our tool
[ApproxMC](https://github.com/meelgroup/ApproxMC) and should be used in front
of [GANAK](https://github.com/meelgroup/ganak). For the paper, see
[here](http://www.msoos.org/wordpress/wp-content/uploads/2022/08/arjun.pdf).
Note that the simplification part of Arjun contains code from SharpSAT-td by
Tuukka Korhonen and Matti Jarvisalo, see [this
PDF](https://raw.githubusercontent.com/Laakeri/sharpsat-td/main/description.pdf)
and [this code](https://github.com/Laakeri/sharpsat-td) for details. Note that
treewidth-decomposition is _not_ part of Arjun.
## How to Build
It is strongly recommended to not build, but to use the precompiled
binaries as in our [release](https://github.com/meelgroup/arjun/releases).
The second best thing to use is Nix. Simply [install
nix](https://nixos.org/download/) and then:
```shell
nix shell github:meelgroup/arjun
```
Then you will have `arjun` binary available and ready to use.
### Building from source
Arjun depends on [CaDiCaL](https://github.com/meelgroup/cadical),
[CryptoMiniSat](https://github.com/meelgroup/cryptominisat),
[SBVA](https://github.com/meelgroup/SBVA), and
[treedecomp](https://github.com/meelgroup/treedecomp).
**Automatic download and build (easiest):** if you do not specify where any
dependency lives, CMake will automatically clone and build it inside the `deps/`
subdirectory of your build directory:
```shell
mkdir build && cd build
cmake ..
make -j8
```
**Using pre-built dependencies:** pass `-D_DIR=` for any dependency
you have already built. CMake will then use that copy instead of fetching it:
```shell
mkdir build && cd build
cmake .. \
-Dcadical_DIR=/path/to/cadical/build \
-Dcryptominisat5_DIR=/path/to/cryptominisat/build \
-Dsbva_DIR=/path/to/sbva/build \
-Dtreedecomp_DIR=/path/to/treedecomp/build
make -j8
```
You can mix and match: omit any `-D_DIR` flag and that dependency will be
fetched and built automatically.
For CaDiCaL you can also set `-Dcadical_SRC_DIR=` to point to the
CaDiCaL source directory (needed for `cadical.hpp`) if it differs from
`/../src`.
### Building statically
To build a static binary, you first need to build GMP with static library support:
```shell
wget https://ftp.gnu.org/gnu/gmp/gmp-6.3.0.tar.xz
tar xf gmp-6.3.0.tar.xz
cd gmp-6.3.0
./configure --enable-static --enable-cxx --enable-shared --with-pic
make -j8
sudo make install
cd ..
```
Then point CMake to the installed GMP static libraries (note: use `/usr/local/lib/`,
not a custom build directory, as those may be compiled for the wrong architecture):
```shell
mkdir build && cd build
cmake -DBUILD_SHARED_LIBS=OFF \
-DGMPXX_LIBRARY=/usr/local/lib/libgmpxx.a \
-DGMP_INCLUDE_DIR=/usr/local/include \
..
make -j8
```
## How to Use
Run it on your instance and it will give you a reduced independent set:
```plain
$ ./arjun input.cnf output.cnf
c [arjun] original sampling set size: 500
c ind 1 4 5 20 31 0
[...]
c [arjun] Done dumping. T: 1.0406
```
This means that your input independent set of your input formula `input.cnf`,
which had a size of 500 has been reduced to 5, which is ony 1% of the original
set. The simplified formula with the smaller independent set has been output to
`output.cnf`. The final simplified will contain a line such as:
```plain
c MUST MULTIPLY BY 1024
```
This means that the final count of the CNF must be multiplied by 2^10 (i.e.
1024) in order to get the correct count. Note that if you forget to multiply,
the count will be wrong. So you must multiply.
## Only extracting independent set
In case you are only interested in a reduced independent set, use:
```plain
$ ./arjun input.cnf
c [arjun] original sampling set size: 500
c ind 1 4 5 20 31 0
```
This will not write an output file, but only display the reduced independent set.