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 here. A series of blog post describe different aspects of the algorithm and its use (link).

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:\(^1\)

https://github.com/scipy/scipy/pull/7729

During my GSoC I have implemented the optimization algorithm, tested it on almost one hundred examples (link) 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 Google for this awesome oportunity, the SciPy community, and my mentors: Nikolay, Matt and Ralf, with whom I have greatly enjoyed the oportunity to work with during these three months.

Notes

1. The last commit of the pull request #7729 made during the GSoC program is: “FIX: optimize: don’t test iterative refinements on win32” (identifier: 1d96711).