Are you talking about coordinate descent in the primal or in the dual?
There are papers for both on the liblinear page.
Primal: http://www.csie.ntu.edu.tw/~cjlin/papers/l1.pdf
Dual: http://www.csie.ntu.edu.tw/~cjlin/papers/cddual.pdf
Sequential Dual:  http://www.csie.ntu.edu.tw/~cjlin/papers/sdm_kdd.pdf

My main interest in this was having a SGD solver for Crammer-Singer
loss, which works nearly the same way as logistic regression.

That is already in Liblinear so I didn't have much interest in
such a solver ;)


On 03/06/2012 09:51 PM, Gael Varoquaux wrote:
> Yes, I was about to answer the same thing: SGD is great when n_samples>
> n_features, but the situation n_samples<<  n_features also exists.
>
> In such situation, I believe that a cyclic coordinate descent with a
> clever way of choosing the coordinates is the fastest approach. In some
> sens it is the transpose of the SGD (hand-wavingly).
>
> I would indeed like to see a fast coordinate descent solver for logistic
> regression. I am more interested in the l1 penalty, but the l2 penalty is
> also useful. Multinomial loss could fall in such work.
>
> For such contribution to be actually useful, I'd like the code to be
> really fast with large n_features: we don't need a solver that doesn't
> scale to real problem. I am not an expert, but I think that a reference
> that I recently mentionned could be useful:
>
> http://www.jmlr.org/papers/volume11/yuan10c/yuan10c.pdf
>
> Obviously doing this right is quite a lot of work. I think that my group
> could invest  some efforts in this direction. We were starting to discuss
> this a bit.
>
> Gaƫl
>
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