Add link to multiprecision repository

Author: usamamuneeb

docs/installation/index.md

@@ -13,7 +13,7 @@ SDPA for Python can be installed for `x86_64` through `pip`. Two variants of thi
 pip install sdpa-python
 ```
 
-The package using the SDPA Multiprecision (GMP) backend can be installed by
+The package using the [SDPA Multiprecision](https://github.com/sdpa-python/sdpa-multiprecision) (GMP) backend can be installed by
 
 ```bash
 pip install sdpa-multiprecision

docs/installation/windows.md

@@ -205,6 +205,7 @@ We need to edit it and provide
 
     ```python
     BLAS_LAPACK_LIBS = ['openblas', 'gomp']
+    ```
 
 5. Only if using the **SDPA Multiprecision** backend, the location of GMP library and headers:
 

index.md

@@ -19,7 +19,7 @@ Interior-point SDP solver for Python
 
 SDPA for Python (`sdpa-python`) is a Python 3 wrapper for SDPA (SemiDefinite Programming Algorithm). SDPA is a software package for solving general SDPs based on primal-dual interior-point methods with the HRVW/KSH/M search direction [1].
 
-This package is a fork of SDPAP, the Python interface for SDPA provided at the [official SDPA website](http://sdpa.sourceforge.net/download.html). This repository aims to provide Python 3 support for both SDPA and SDPA Multiprecision (fork of SDPA-GMP).
+This package is a fork of SDPAP, the Python interface for SDPA provided at the [official SDPA website](http://sdpa.sourceforge.net/download.html). This repository aims to provide Python 3 support for both SDPA and [SDPA Multiprecision](https://github.com/sdpa-python/sdpa-multiprecision) (fork of SDPA-GMP [4]).
 
 ## Getting Started
 
@@ -60,3 +60,5 @@ If you are using SDPA for Python in your research, please cite SDPA by citing th
 [2] Makoto Yamashita, Katsuki Fujisawa, Kazuhide Nakata, Maho Nakata, Mituhiro Fukuda, Kazuhiro Kobayashi, and Kazushige Goto, "A high-performance software package for semidefinite programs: SDPA 7," *[Research Report B-460](http://www.optimization-online.org/DB_HTML/2010/01/2531.html) Dept. of Mathematical and Computing Science, Tokyo Institute of Technology, Tokyo, Japan, September*, 2010.
 
 [3] Makoto Yamashita, Katsuki Fujisawa, Mituhiro Fukuda, Kazuhiro Kobayashi, Kazuhide Nakata and Maho Nakata, “Latest Developments in the SDPA Family for Solving Large-Scale SDPs,” in *Handbook on Semidefinite, Conic and Polynomial Optimization, M. F. Anjos and J. B. Lasserre, Eds. Boston, MA: Springer US*, 2012, pp. 687–713. doi: [10.1007/978-1-4614-0769-0_24](https://doi.org/10.1007/978-1-4614-0769-0_24).
+
+[4] Nakata, M. (2010). A numerical evaluation of highly accurate multiple-precision arithmetic version of semidefinite programming solver: SDPA-GMP, -QD and -DD. 2010 *IEEE International Symposium on Computer-Aided Control System Design*, 29–34. doi: [10.1109/CACSD.2010.5612693](https://doi.org/10.1109/CACSD.2010.5612693)