From d4c050cc74070186b4812213802a67a9bd25e22f Mon Sep 17 00:00:00 2001 From: UDDALOK SARKAR Date: Sat, 27 Apr 2024 00:07:44 +0530 Subject: [PATCH 1/2] Update README.md - syned paper urls --- README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index dec0c5e..ad2e252 100644 --- a/README.md +++ b/README.md @@ -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. @@ -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://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/bib/MA20.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). From ef0574ae0637bac1dc849bef0d119809fca6961b Mon Sep 17 00:00:00 2001 From: UDDALOK SARKAR Date: Sat, 27 Apr 2024 00:23:55 +0530 Subject: [PATCH 2/2] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index ad2e252..629ec04 100644 --- a/README.md +++ b/README.md @@ -186,7 +186,7 @@ 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://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. +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. @@ -195,7 +195,7 @@ Please click on "issues" at the top and [create a new issue](https://github.com/ ## 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.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.cs.toronto.edu/~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.cs.toronto.edu/~meel/publications.html)