We may want to try to implement "covariance updates" for our solver, in precisely the same fashion as in the Friedman paper on the elastic net. So we incrementally build the Gram matrix as predictors become nonzero and use that in our updates. It should help a lot when n > p I think (and possibly even in more instances since we can cache more stuff efficiently). I wrote a little bit about it in section 2.1.3. As this only really works for least squares SLOPE I think it may be of middling interest however. What do you think?
We may want to try to implement "covariance updates" for our solver, in precisely the same fashion as in the Friedman paper on the elastic net. So we incrementally build the Gram matrix as predictors become nonzero and use that in our updates. It should help a lot when n > p I think (and possibly even in more instances since we can cache more stuff efficiently). I wrote a little bit about it in section 2.1.3. As this only really works for least squares SLOPE I think it may be of middling interest however. What do you think?