GSoC 2017 - Large-scale Constrained Optimization
Hi all, During the last few months I have worked on my Google Summer of Code (GSoC) project, that consists of implementing a large-scale optimization algorithm to be integrated to Scipy. The algorithm implemented was an interior point method described in <https://antonior92.github.io/posts/2017/07/interior-point-method/ <https://antonior92.github.io/posts/2017/07/interior-point-method/>>. A series of blog post describe different aspects of the algorithm and its use <https://antonior92.github.io/tags/#gsoc-2017 <https://antonior92.github.io/tags/#gsoc-2017>>. The implementation can be found on the separate repository: <https://github.com/antonior92/ip-nonlinear-solver <https://github.com/antonior92/ip-nonlinear-solver>> and is being integrated to SciPy through the pull request #7729 <https://github.com/scipy/scipy/pull/7729 <https://github.com/scipy/scipy/pull/7729>> During my GSoC I have implemented the optimization algorithm, tested it on almost one hundred examples <https://antonior92.github.io/posts/2017/00/NumericalResults/ <https://antonior92.github.io/posts/2017/00/NumericalResults/>> and created an interface for using it. While the optimization solver is ready to be used and tested, there are still a features I want to include, namely quasi-Newton approximations to the Hessian matrix including: SR1 approximation; BFGS approximation; and, L-BFGS approximation. I included those as optional items on my GSoC proposal and, unfortunately, they will not be ready for the GSoC submission. Furthermore, there are still some questions about how to best integrate my implementation to the optimization SciPy library that are still under discussion. However, I will tend to those final points in the weeks following the end of the program. Finally, I would like to thanks SciPy community and my mentors: Nikolay, Matt and Ralf, with whom I have greatly enjoyed the opportunity to work with during these three months. Antônio H. Ribeiro
Hello Antonio, Is it true that there is currently no interior point optimizer in SciPy? If so, your algorithm will be an extremely valuable addition. Phillip P.S. `scipy.optimize.lsq_linear` is described as "interior-point-like", but I'm not entirely sure what this means. On Mon, Aug 28, 2017 at 3:47 PM, Antonio Ribeiro <antonior92@gmail.com> wrote:
Hi all,
During the last few months I have worked on my Google Summer of Code (GSoC) project, that consists of implementing a large-scale optimization algorithm to be integrated to Scipy.
The algorithm implemented was an interior point method described in < https://antonior92.github.io/posts/2017/07/interior-point-method/>. A series of blog post describe different aspects of the algorithm and its use <https://antonior92.github.io/tags/#gsoc-2017>.
The implementation can be found on the separate repository:
<https://github.com/antonior92/ip-nonlinear-solver>
and is being integrated to SciPy through the pull request #7729
<https://github.com/scipy/scipy/pull/7729>
During my GSoC I have implemented the optimization algorithm, tested it on almost one hundred examples <https://antonior92.github.io/posts/2017/00/ NumericalResults/> and created an interface for using it. While the optimization solver is ready to be used and tested, there are still a features I want to include, namely quasi-Newton approximations to the Hessian matrix including: SR1 approximation; BFGS approximation; and, L-BFGS approximation. I included those as optional items on my GSoC proposal and, unfortunately, they will not be ready for the GSoC submission. Furthermore, there are still some questions about how to best integrate my implementation to the optimization SciPy library that are still under discussion. However, I will tend to those final points in the weeks following the end of the program.
Finally, I would like to thanks SciPy community and my mentors: Nikolay, Matt and Ralf, with whom I have greatly enjoyed the opportunity to work with during these three months.
Antônio H. Ribeiro
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On Tue, Aug 29, 2017 at 10:47 AM, Antonio Ribeiro <antonior92@gmail.com> wrote:
Hi all,
During the last few months I have worked on my Google Summer of Code (GSoC) project, that consists of implementing a large-scale optimization algorithm to be integrated to Scipy.
The algorithm implemented was an interior point method described in < https://antonior92.github.io/posts/2017/07/interior-point-method/>. A series of blog post describe different aspects of the algorithm and its use <https://antonior92.github.io/tags/#gsoc-2017>.
The implementation can be found on the separate repository:
<https://github.com/antonior92/ip-nonlinear-solver>
and is being integrated to SciPy through the pull request #7729
<https://github.com/scipy/scipy/pull/7729>
During my GSoC I have implemented the optimization algorithm, tested it on almost one hundred examples <https://antonior92.github.io/posts/2017/00/ NumericalResults/> and created an interface for using it. While the optimization solver is ready to be used and tested, there are still a features I want to include, namely quasi-Newton approximations to the Hessian matrix including: SR1 approximation; BFGS approximation; and, L-BFGS approximation. I included those as optional items on my GSoC proposal and, unfortunately, they will not be ready for the GSoC submission. Furthermore, there are still some questions about how to best integrate my implementation to the optimization SciPy library that are still under discussion. However, I will tend to those final points in the weeks following the end of the program.
Finally, I would like to thanks SciPy community and my mentors: Nikolay, Matt and Ralf, with whom I have greatly enjoyed the opportunity to work with during these three months.
Thank you Antonio, was (is - don't go anywhere!) great working with you:) And thanks to Nikolay and Matt for doing all the hard mentoring work! Cheers, Ralf
Hello, This is really great. Thank you, Antônio! I will definitely try to learn from your implementation. Cheers, Melissa Em ter, 29 de ago de 2017 às 06:19, Ralf Gommers <ralf.gommers@gmail.com> escreveu:
On Tue, Aug 29, 2017 at 10:47 AM, Antonio Ribeiro <antonior92@gmail.com> wrote:
Hi all,
During the last few months I have worked on my Google Summer of Code (GSoC) project, that consists of implementing a large-scale optimization algorithm to be integrated to Scipy.
The algorithm implemented was an interior point method described in < https://antonior92.github.io/posts/2017/07/interior-point-method/>. A series of blog post describe different aspects of the algorithm and its use <https://antonior92.github.io/tags/#gsoc-2017>.
The implementation can be found on the separate repository:
<https://github.com/antonior92/ip-nonlinear-solver>
and is being integrated to SciPy through the pull request #7729
<https://github.com/scipy/scipy/pull/7729>
During my GSoC I have implemented the optimization algorithm, tested it on almost one hundred examples < https://antonior92.github.io/posts/2017/00/NumericalResults/> and created an interface for using it. While the optimization solver is ready to be used and tested, there are still a features I want to include, namely quasi-Newton approximations to the Hessian matrix including: SR1 approximation; BFGS approximation; and, L-BFGS approximation. I included those as optional items on my GSoC proposal and, unfortunately, they will not be ready for the GSoC submission. Furthermore, there are still some questions about how to best integrate my implementation to the optimization SciPy library that are still under discussion. However, I will tend to those final points in the weeks following the end of the program.
Finally, I would like to thanks SciPy community and my mentors: Nikolay, Matt and Ralf, with whom I have greatly enjoyed the opportunity to work with during these three months.
Thank you Antonio, was (is - don't go anywhere!) great working with you:) And thanks to Nikolay and Matt for doing all the hard mentoring work!
Cheers, Ralf
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participants (4)
-
Antonio Ribeiro -
Melissa Weber Mendonça -
Phillip Feldman -
Ralf Gommers