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From "DB Tsai (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-1157) L-BFGS Optimizer
Date Wed, 09 Apr 2014 04:56:16 GMT

    [ https://issues.apache.org/jira/browse/SPARK-1157?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13963803#comment-13963803
] 

DB Tsai commented on SPARK-1157:
--------------------------------

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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