Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Update README.md #52

Merged
merged 2 commits into from
Apr 29, 2024
Merged

Update README.md #52

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
d4c050c
Update README.md - syned paper urls
uddaloksarkar Apr 26, 2024
ef0574a
Update README.md
uddaloksarkar Apr 26, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
10 changes: 5 additions & 5 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,7 @@
# ApproxMC4: Approximate Model Counter
ApproxMCv4 is a state-of-the-art approximate model counter utilizing an improved version of CryptoMiniSat to give approximate model counts to problems of size and complexity that were not possible before.

This work is by Mate Soos, Stephan Gocht, and Kuldeep S. Meel, as [published in AAAI-19](https://www.comp.nus.edu.sg/~meel/Papers/aaai19-sm.pdf) and [in CAV2020](https://www.comp.nus.edu.sg/~meel/Papers/cav20-sgm.pdf). A large part of the work is in CryptoMiniSat [here](https://github.com/msoos/cryptominisat).
This work is by Mate Soos, Stephan Gocht, and Kuldeep S. Meel, as [published in AAAI-19](https://www.cs.toronto.edu/~meel/Papers/aaai19-sm.pdf) and [in CAV2020](https://www.cs.toronto.edu/~meel/Papers/cav20-sgm.pdf). A large part of the work is in CryptoMiniSat [here](https://github.com/msoos/cryptominisat).

ApproxMC handles CNF formulas and performs approximate counting.

Expand Down Expand Up @@ -186,18 +186,18 @@ int main() {
```

### ApproxMC5: Sparse-XOR based Approximate Model Counter
Note: this is beta version release, not recommended for general use. We are currently working on a tight integration of sparse XORs into ApproxMC based on our [LICS-20](http://comp.nus.edu.sg/~meel/Papers/lics20-ma.pdf) paper. You can turn on the sparse XORs using the flag "sparse" but beware as reported in LICS-20 paper, this may slow down in some cases; it is likely to give a significant speedup if the number of solutions is very large.
Note: this is beta version release, not recommended for general use. We are currently working on a tight integration of sparse XORs into ApproxMC based on our [LICS-20](http://www.cs.toronto.edu/~meel/Papers/lics20-ma.pdf) paper. You can turn on the sparse XORs using the flag "sparse" but beware as reported in LICS-20 paper, this may slow down in some cases; it is likely to give a significant speedup if the number of solutions is very large.


### Issues, questions, bugs, etc.
Please click on "issues" at the top and [create a new issue](https://github.com/meelgroup/mis/issues/new). All issues are responded to promptly.

## How to Cite
If you use ApproxMC, please cite the following papers: [CAV20](https://dblp.uni-trier.de/rec/conf/cav/SoosGM20.html?view=bibtex), [AAAI19](https://www.comp.nus.edu.sg/~meel/bib/SM19.bib) and [IJCAI16](https://www.comp.nus.edu.sg/~meel/bib/CMV16.bib).
If you use ApproxMC, please cite the following papers: [CAV20](https://dblp.uni-trier.de/rec/conf/cav/SoosGM20.html?view=bibtex), [AAAI19](https://www.cs.toronto.edu/~meel/bib/SM19.bib) and [IJCAI16](https://www.cs.toronto.edu/~meel/bib/CMV16.bib).

If you use sparse XORs, please also cite the [LICS20](https://www.comp.nus.edu.sg/~meel/bib/MA20.bib) paper.
If you use sparse XORs, please also cite the [LICS20](https://www.cs.toronto.edu/~meel/publications/AM20.bib) paper.

ApproxMC builds on a series of papers on hashing-based approach: [Related Publications](https://www.comp.nus.edu.sg/~meel/publications.html)
ApproxMC builds on a series of papers on hashing-based approach: [Related Publications](https://www.cs.toronto.edu/~meel/publications.html)

The benchmarks used in our evaluation can be found [here](https://zenodo.org/records/10449477).

Expand Down
Loading