A fast multinomial logistic optimization routine

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: [url removed, login to view]

GLMNET paper: [url removed, login to view]~hastie/Papers/[url removed, login to view]

Optimization routine: [url removed, login to view]

Pay will be correlated with speed, faster implementations relative to numpy get more money, if the algorithm is slower than numpy + newton method there will be no payment.

The routine needs to be able to handle several million observations with tens of explanatory variables/covariates.

Habilidades: Programação C, Programação C++ , Fortran, Matemática, Python

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( 0 comentários ) United Kingdom

ID do Projeto: #5095772

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Hi, I am interested in the project. I am familiar with multinomial logistic regression (I used that in microeconometrics), and also I have good skills in numerical optimization. Kind regards.

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