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https://issues.apache.org/jira/browse/SPARK-1157?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13963803#comment-13963803
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DB Tsai commented on SPARK-1157:
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PR: https://github.com/apache/spark/pull/353
> L-BFGS Optimizer
> ----------------
>
> Key: SPARK-1157
> URL: https://issues.apache.org/jira/browse/SPARK-1157
> Project: Spark
> Issue Type: New Feature
> Reporter: DB Tsai
>
> L-BFGS (Limited-memory BFGS) is an optimization algorithm like BFGS which
> uses an approximation to the inverse of Hessian matrix to steer its search
> through the variable space, but where BFGS stores a dense nxn approximation
> to the inverse Hessian, L-BFGS only stores a few vectors to represent the
> approximation.
> For high dimensional optimization problems, the Newton method or BFGS is not
> applicable since the amount of memory needed to store the Hessian will grow
> exponentially, while L-BFGS only stores couple vectors.
> One of the use case can be training large-scale logistic regression with so
> many features.
> We'll use breeze implementation of L-BFGS.
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