A fast multinomial logistic optimization routine - repost

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$750 - $1500

Project Description:
GLMNET is an R package which implements a fast algorithm for estimation of generalized linear models with convex penalties, such as a linear regression problem. The package isn't able to handle discrete choice multinomial regression.

I want an implementation which is able to handle discrete choice multinomial logistic regression, must be faster than a python numpy implementation and also be straightward to interface/implement with/in python.

Here are important links which will clarify the problem.

Model: http://en.wikipedia.org/wiki/Discrete_choice#E._Logit_with_attributes_of_the_person_but_no_attributes_of_the_alternatives
GLMNET paper: http://www.stanford.edu/~hastie/Papers/glmnet.pdf
Optimization routine: http://en.wikipedia.org/wiki/Coordinate_descent

Skills required:
C Programming, C++ Programming, Fortran, Mathematics, Python
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