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https://issues.apache.org/jira/browse/SPARK-14200?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15215715#comment-15215715
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Nick Pentreath commented on SPARK-14200:
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I would advise first implementing this as a Spark Package
(http://spark-packages.org/). See e.g.
https://github.com/databricks/spark-tfocs. If it gains wide user adoption it
could then be considered for inclusion in MLlib.
> The optimization method of convex function
> -------------------------------------------
>
> Key: SPARK-14200
> URL: https://issues.apache.org/jira/browse/SPARK-14200
> Project: Spark
> Issue Type: Question
> Components: MLlib, Optimizer
> Affects Versions: 2.1.0
> Reporter: chenalong
> Labels: BMRM, MLlib, Optimization
> Original Estimate: 1,344h
> Remaining Estimate: 1,344h
>
> I want to implement Bundle Methods for Regularized Risk Minimization(BMRM) in
> Spark MLlib. BMRM is a nonsmooth convex optimization techniques, which is
> more faster than SGD and can solve non-differentiable problems and
> differentiable problems. Is this idea OK, Can you give me some advices?
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