Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/702#discussion_r12501445
--- Diff: docs/mllib-optimization.md ---
@@ -163,3 +171,108 @@ 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
+[LogisticRegression.LogisticRegressionWithSGD](api/mllib/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithSGD).
--- End diff --
`[LogisticRegression.LogisticRegressionWithSGD]` ->
`[LogisticRegressionWithSGD]
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