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From mengxr <>
Subject [GitHub] spark pull request: L-BFGS Documentation
Date Fri, 09 May 2014 00:35:21 GMT
Github user mengxr commented on a diff in the pull request:
    --- Diff: docs/ ---
    @@ -163,3 +177,100 @@ each iteration, to compute the gradient direction.
     Available algorithms for gradient descent:
     * [GradientDescent.runMiniBatchSGD](api/mllib/index.html#org.apache.spark.mllib.optimization.GradientDescent)
    +### Limited-memory BFGS
    +L-BFGS is currently only a low-level optimization primitive in `MLlib`. If you want to
use L-BFGS in various 
    +ML algorithms such as Linear Regression, and Logistic Regression, you have to pass the
gradient of objective
    +function, and updater into optimizer yourself instead of using the training APIs like

    +See the example below. It will be addressed in the next release. 
    +The L1 regularization by using 
will not work since the 
    --- End diff --
    `L1Updater` is not under `Updater`. Should change to

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