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From: omegayen on 7 May 2010 12:59
"tibshirani tibshirani" <tibs(a)stanford.edu> wrote in message <h84tje$nr2$1(a)fred.mathworks.com>...
> Now (freely) available
>
> Glmnet for Matlab:
>
> Code for fitting Lasso (L1) and elastic-net regularized generalized linear models
>
>
> Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models.
>
> The algorithm uses cyclical coordinate descent in a pathwise fashion.
>
> Go to http://www-stat.stanford.edu/~tibs/glmnet-matlab/
>
> for more detals
>
> sincerely
>
> Rob Tibshirani

Thanks for providing this, I looked at the examples but was not sure. How would I solve for x in the equation Ax=b using the elastic net?
From: Hui Jiang on 7 May 2010 15:00
"omegayen " <omegayen(a)ameritech.net> wrote in message <hs1gsp$saa$1(a)fred.mathworks.com>...
> "tibshirani tibshirani" <tibs(a)stanford.edu> wrote in message <h84tje$nr2$1(a)fred.mathworks.com>...
> > Now (freely) available
> >
> > Glmnet for Matlab:
> >
> > Code for fitting Lasso (L1) and elastic-net regularized generalized linear models
> >
> >
> > Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models.
> >
> > The algorithm uses cyclical coordinate descent in a pathwise fashion.
> >
> > Go to http://www-stat.stanford.edu/~tibs/glmnet-matlab/
> >
> > for more detals
> >
> > sincerely
> >
> > Rob Tibshirani
>
> Thanks for providing this, I looked at the examples but was not sure. How would I solve for x in the equation Ax=b using the elastic net?

It depends on your problem. Suppose A is nxp. If n=p you can solve it using any linear solver. If n>p you can use linear regression. If n<p or you want x to be sparse, i.e. only a few elements in x are non-zeros, you can use Lasso or elastic-net.
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