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https://issues.apache.org/jira/browse/SPARK-10078?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15793760#comment-15793760
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Debasish Das edited comment on SPARK-10078 at 1/3/17 12:26 AM:
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Ideally feature partitioning should be automatically tuned...at 100M features
master only processing what we do with Breeze LBFGS / OWLQN will also get
benefitted by VL-BFGS....Ideally it should be part of breeze and a proper
interface should be defined so that the Breeze VL-BFGS solver can be called in
Spark ML...There are bounded BFGS that's in breeze...all of them will be
benefited by this change. A solver can be used in other frameworks as well and
may not be constrained to RDD if possible...
was (Author: debasish83):
Ideally feature partitioning should be automatically tuned...at 100M features
master only processing what we do with Breeze LBFGS / OWLQN will also get
benefitted by VL-BFGS....Ideally it should be part of breeze and a proper
interface should be defined so that the Breeze VL-BFGS solver can be called in
Spark ML...
> Vector-free L-BFGS
> ------------------
>
> Key: SPARK-10078
> URL: https://issues.apache.org/jira/browse/SPARK-10078
> Project: Spark
> Issue Type: New Feature
> Components: ML
> Reporter: Xiangrui Meng
> Assignee: Yanbo Liang
>
> This is to implement a scalable version of vector-free L-BFGS
> (http://papers.nips.cc/paper/5333-large-scale-l-bfgs-using-mapreduce.pdf).
> Design document:
> https://docs.google.com/document/d/1VGKxhg-D-6-vZGUAZ93l3ze2f3LBvTjfHRFVpX68kaw/edit?usp=sharing
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