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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]

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

Ver mais: stanford programming, python programming wiki, python programming pdf, programming models, optimization linear programming, linear programming problem, linear programming pdf, linear programming optimization, linear programming models, linear programming model, glmnet, programming wiki, convex programming, python numpy, regression model, r package, python linear programming, python algorithm, optimization model, optimization algorithm, linear programming, linear models, algorithm python, algorithm optimization, numpy optimization

Acerca do Empregador:
( 0 comentários ) United Kingdom

ID do Projeto: #5095772

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OriolAM

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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