Logistic regression can be formulated in multiple ways. This note clarifies the equivalence between the formulations.
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.
In this post the interior point method described in  will be discussed. This algorithm solve the nonlinearly constrained optimization problem:
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:
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:
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.