# GSoC 2017 - Submission

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.

# Numerical Results

In this blog post, I will present numerical results obtained solving problems from the CUTEst collection [1] using the algorithms implemented during my GSoC project.

# Usage Example

In this blog post I will provide an simple example of application.

# Interior-Point Method

In this post the interior point method described in [1] will be discussed. This algorithm solve the nonlinearly constrained optimization problem:

# Byrd-Omojokun Trust-Region SQP

During the previous two weeks I have been implementing a trust-region Sequential Quadratic Programming (SQP) method. This method is able to solve the equality-constrained nonlinear programming problem:

# Projected Conjugate Gradient

The projected conjugate gradient (CG) method was implemented during my first GSoC weeks. It solves the equality-constrained quadratic programming (EQP) problems of the form:

# GSoC 2017 - Scipy: Large-scale Constrained Optimization

This year I was chosen as the student for Google Summer of Code. I’ll be working on one of the core Python scientific libraries called Scipy. My task is to implement a constrained optimization algorithm able to deal with large (and possibly sparse) problems.