A fast multinomial logistic optimization routine - repost

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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: [login to view URL] GLMNET paper: [login to view URL]~hastie/Papers/[login to view URL] Optimization routine: [login to view URL]

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@samitXI

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@Zxiaonan

hi, i am mathematics expert. It is possible to handle discrete choice multinomial logistic regression. allow me for your project. thanks.

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