From 5a5fc493e8fea11154084834c59dcba89a67e361 Mon Sep 17 00:00:00 2001 From: aryan eftekhari <aryan.eftekhari@usi.ch> Date: Mon, 25 Jul 2022 13:41:03 +0000 Subject: [PATCH] Update README.md --- README.md | 26 ++++++++++++++++++-------- 1 file changed, 18 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index dd2cf15..b15baac 100644 --- a/README.md +++ b/README.md @@ -1,23 +1,25 @@ # SQUIC Release Source +SQUIC is a second-order, L1-regularized maximum likelihood method for performant large-scale sparse precision matrix estimation. This code is a shared library 'libSQUIC', intended for Linux and Mac OS. The code is written in C++ and is parallelized with OpenMP. -SQUIC is a second-order, L1-regularized maximum likelihood method for performant large-scale sparse precision matrix estimation. The presented source code is the SQUIC shared library (libSQUIC) intended for Linux and Mac OS. -The following interface packages for SQUIC can be found here: https://www.gitlab.ci.inf.usi.ch/SQUIC +[](https://badge.fury.io/py/squic) +[](https://pypi.python.org/pypi/squic) +#[](https://github.com/amaiya/ktrain/blob/master/LICENSE) +#[](https://pepy.tech/project/ktrsquicain) +#[](https://twitter.com/ktrain_ai) -_Note: For all note interface packages, libSQUIC is required. Precompiled versions of libSQUIC for Mac and Linux are available and are ready to use._ -[](https://colab.research.google.com/drive/1iQB5hz07UMd5C1PR3w3xM3306BVcFGiO?usp=sharing) +The shared library can be used directly from the precompiled version see XXXXX, or compiled from source. Additional Python and R APIs can be found here: https://www.gitlab.ci.inf.usi.ch/SQUIC. Note, for all API, the hared library 'libSQUIC' is required. -#### References +[](https://colab.research.google.com/drive/1iQB5hz07UMd5C1PR3w3xM3306BVcFGiO?usp=sharing) -[1] [Bollhöfer, M., Eftekhari, A., Scheidegger, S. and Schenk, O., 2019. Large-scale sparse inverse covariance matrix estimation. SIAM Journal on Scientific Computing, 41(1), pp.A380-A401.](https://epubs.siam.org/doi/abs/10.1137/17M1147615?journalCode=sjoce3) -[2] [Eftekhari, A., Bollhöfer, M. and Schenk, O., 2018, November. Distributed memory sparse inverse covariance matrix estimation on high-performance computing architectures. In SC18: International Conference for High Performance Computing, Networking, Storage and Analysis (pp. 253-264). IEEE.](https://dl.acm.org/doi/10.5555/3291656.3291683) -[3] [Eftekhari, A., Pasadakis, D., Bollhöfer, M., Scheidegger, S. and Schenk, O., 2021. Block-Enhanced PrecisionMatrix Estimation for Large-Scale Datasets. Journal of Computational Science, p. 101389.](https://www.sciencedirect.com/science/article/pii/S1877750321000776) +[](https://colab.research.google.com/drive/1iQB5hz07UMd5C1PR3w3xM3306BVcFGiO?usp=sharing) + ## Precompiled libSQUIC @@ -79,3 +81,11 @@ using the command: ```angular2 make install ``` + +#### References + +[1] [Bollhöfer, M., Eftekhari, A., Scheidegger, S. and Schenk, O., 2019. Large-scale sparse inverse covariance matrix estimation. SIAM Journal on Scientific Computing, 41(1), pp.A380-A401.](https://epubs.siam.org/doi/abs/10.1137/17M1147615?journalCode=sjoce3) + +[2] [Eftekhari, A., Bollhöfer, M. and Schenk, O., 2018, November. Distributed memory sparse inverse covariance matrix estimation on high-performance computing architectures. In SC18: International Conference for High Performance Computing, Networking, Storage and Analysis (pp. 253-264). IEEE.](https://dl.acm.org/doi/10.5555/3291656.3291683) + +[3] [Eftekhari, A., Pasadakis, D., Bollhöfer, M., Scheidegger, S. and Schenk, O., 2021. Block-Enhanced PrecisionMatrix Estimation for Large-Scale Datasets. Journal of Computational Science, p. 101389.](https://www.sciencedirect.com/science/article/pii/S1877750321000776) -- GitLab