inequality constraints in linear regression.ipynb

inequality-constraints-in-linear-regression.ipynb

Hi, how to put both constraints together, min beta value should be 1 and sum of all the betas should be equal or less than a particular no

Hi, how to put both constraints together, min beta value should be 1 and sum of all the betas should be equal or less than a particular no

See my example above. -np.identity provides a single 1 for each beta under A, and you will then include the corresponding minimum value in B.

Its not working.
I am putting 2 conditions together:

1. min beta value - 1
2. sum of all beta <= a no

Please paste your code.

Can I also make intercept positive. if yes, please let me know how? I am using the same code, just making min_coef = 1

@avidale how is the coordinate descent computing the constraints? For example, if I used an interior point solver, it creates a barrier function for inequalities and then a newton line search is used.

EDIT: And what do you mean by "recalculating constraints for every coefficient on each step"?

Thank you very much for sharing. This is what I'm looking for. Is there a way to just constrain specific subset of features and leave everything else as is?

Brilliant solution and well explained. I'm late to the party here but how would one modify this to use weighted-least-squares with constraints?