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https://issues.apache.org/jira/browse/SPARK-10078?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15819851#comment-15819851
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Weichen Xu edited comment on SPARK-10078 at 1/12/17 4:43 AM:
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[~debasish83] Can L-BFGS-B be distributed computed when scaled to billions of
features in high efficiency ? If only the interface supporting distributed
vector, but internal computation still use local vector and/or local matrix,
then it seems won't make much sense...
Currently VF-LBFGS can turn LBFGS two loop recursion into distributed computing
mode, but the L-BFGS-B seems much more complex then L-BFGS, can it also be
computed in parallel ?
I look into L-BFGS-B code in breeze and the core updating Hessian and computing
descent direction in L-BFGS-B is very complex, this part it cannot reuse LBFGS
code. So, through which way LBFGS-B can take advantage of `Vector-free LBFGS` ?
was (Author: weichenxu123):
[~debasish83] Can L-BFGS-B be distributed computed when scaled to billions of
features in high efficiency ? If only the interface supporting distributed
vector, but internal computation still use local vector and/or local matrix,
then it seems won't make much sense...
Currently VF-LBFGS can turn LBFGS two loop recursion into distributed computing
mode, but the L-BFGS-B seems much more complex then L-BFGS, can it also be
computed in parallel ?
> 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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