Update README.md

Author: arjendeetman

README.md

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 # GCMMA-MMA-Python
-Python code of the Method of Moving Asymptotes ([Svanberg, 1987](https://onlinelibrary.wiley.com/doi/abs/10.1002/nme.1620240207)). Based on the GCMMA-MMA-code written for MATLAB by Krister Svanberg. The original work was taken from http://www.smoptit.se/ under the GNU General Public License. If you download and use the code, Krister Svanberg would appreciate if you could send him an e-mail and tell who you are and what your plan is (e-mail adress can be found on his website). The user should reference to the academic work of Krister Svanberg when work will be published. References can be find below. An example application in topology optimization can be found [here](https://github.com/arjendeetman/TopOpt-MMA-Python).
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+This repository contains the Python code of the Method of Moving Asymptotes ([Svanberg, 1987](https://onlinelibrary.wiley.com/doi/abs/10.1002/nme.1620240207)), originally developed and written in MATLAB by Krister Svanberg. The original work was taken from http://www.smoptit.se/ under the GNU General Public License. 
+
+If you opt to use this code, Krister Svanberg would appreciate it if you could send him an email sharing your application and intentions (the email address can be found on his website). When work is published, the authors must cite Krister Svanberg's academic work. The references can be found below. 
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+An example application of the code in topology optimization can be found [here](https://github.com/arjendeetman/TopOpt-MMA-Python).
 
 ## References
 
-Svanberg, K. (1987). The Method of Moving Asymptotes – A new method for structural optimization. International Journal 
+- Svanberg, K. (1987). The Method of Moving Asymptotes – A new method for structural optimization. International Journal 
 for Numerical Methods in Engineering 24, 359-373. [doi:10.1002/nme.1620240207](https://onlinelibrary.wiley.com/doi/abs/10.1002/nme.1620240207)
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-Svanberg, K. (n.d.). MMA and GCMMA – two methods for nonlinear optimization. Retrieved August 3, 2017 from  
+ - Svanberg, K. (n.d.). MMA and GCMMA – two methods for nonlinear optimization. Retrieved August 3, 2017 from  
 https://people.kth.se/~krille/mmagcmma.pdf 
 
 ## Used in
 
-Morin, R., Kim, I.Y. Partitioning a topology-optimized structure into additively manufacturable parts using a feature-mapping approach: a novel decomposition optimization method. Struct Multidisc Optim 65, 298 (2022). [https://doi.org/10.1007/s00158-022-03394-8](https://link.springer.com/article/10.1007/s00158-022-03394-8)
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-Frederiksen, A.H., Sigmund, O. & Poulios, K. Topology optimization of self-contacting structures. Comput Mech (2023). [https://doi.org/10.1007/s00466-023-02396-7](https://link.springer.com/article/10.1007/s00466-023-02396-7)
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-Fernandes P, Ferrer À, Gonçalves P, Parente M, Pinto R, Correia N. Stress-Constrained Topology Optimization for Commercial Software: A Python Implementation for ABAQUS®. Applied Sciences. 2023; 13(23):12916. [https://doi.org/10.3390/app132312916](https://www.mdpi.com/2076-3417/13/23/12916)
+- Morin, R., Kim, I.Y. Partitioning a topology-optimized structure into additively manufacturable parts using a feature-mapping approach: a novel decomposition optimization method. Struct Multidisc Optim 65, 298 (2022). [https://doi.org/10.1007/s00158-022-03394-8](https://link.springer.com/article/10.1007/s00158-022-03394-8)
+- Frederiksen, A.H., Sigmund, O. & Poulios, K. Topology optimization of self-contacting structures. Comput Mech (2023). [https://doi.org/10.1007/s00466-023-02396-7](https://link.springer.com/article/10.1007/s00466-023-02396-7)
+- Fernandes P, Ferrer À, Gonçalves P, Parente M, Pinto R, Correia N. Stress-Constrained Topology Optimization for Commercial Software: A Python Implementation for ABAQUS®. Applied Sciences. 2023; 13(23):12916. [https://doi.org/10.3390/app132312916](https://www.mdpi.com/2076-3417/13/23/12916)
 
 ## License
-Original work written in MATLAB: Copyright (c) Krister Svanberg
+Original work written in MATLAB: Copyright (c) 2006-2009 Krister Svanberg
 
 Derived Python implementation: Copyright (c) 2020-2023 Arjen Deetman