[scikit-learn] Optimization algorithms in scikit-learn

Andreas Mueller t3kcit at gmail.com
Tue Sep 4 13:44:31 EDT 2018

Hi Touqir.
We don't usually implement general purpose optimizers in
scikit-learn, in particular because usually different optimizers
apply to different kinds of problems.
For linear models we have SAG and SAGA, for neural nets we have adam.
I don't think the authors claim to be faster than SAG, so I'm not sure 
what the
motivation would be for using their method.


On 09/04/2018 12:55 PM, Touqir Sajed wrote:
> Hi,
> I have been looking for stochastic optimization algorithms in 
> scikit-learn that are faster than SGD and so far I have come across 
> Adam and momentum. Are there other methods implemented in 
> scikit-learn? Particularly, the variance reduction methods such as 
> (https://papers.nips.cc/paper/4937-accelerating-stochastic-gradient-descent-using-predictive-variance-reduction.pdf 
> <https://ml-trckr.com/link/https%3A%2F%2Fpapers.nips.cc%2Fpaper%2F4937-accelerating-stochastic-gradient-descent-using-predictive-variance-reduction.pdf/W7SK8K47xGR7dKCC8Wlv>) 
> ? These variance reduction methods are the current state of the art in 
> terms of convergence speed while maintaining runtime complexity of 
> order n -- number of features. If they are not implemented yet, I 
> think it would be really great to implement(I am happy to do so) them 
> since nowadays working on large datasets(where LBGFS may not be 
> practical) is the norm where the improvements are definitely worth it.
> Cheers,
> Touqir
> -- 
> Computing Science Master's student at University of Alberta, Canada, 
> specializing in Machine Learning. Website : 
> https://ca.linkedin.com/in/touqir-sajed-6a95b1126 
> <https://ml-trckr.com/link/https%3A%2F%2Fca.linkedin.com%2Fin%2Ftouqir-sajed-6a95b1126/W7SK8K47xGR7dKCC8Wlv>
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn

-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20180904/82f16708/attachment-0001.html>

More information about the scikit-learn mailing list