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https://issues.apache.org/jira/browse/SPARK-14090?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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chenalong updated SPARK-14090:
------------------------------
Priority: Major (was: Critical)
Description:
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?
was:From now, The optimization method of convex function in MLlib is not
enough. The SGD and ALS method are slow compare with bundle method.so can we
realize this method in spark?
> The optimization method of convex function
> ------------------------------------------
>
> Key: SPARK-14090
> URL: https://issues.apache.org/jira/browse/SPARK-14090
> Project: Spark
> Issue Type: Task
> Components: MLlib, Optimizer
> Affects Versions: 2.1.0
> Reporter: chenalong
> Labels: 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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