Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/702#discussion_r12499609
--- Diff: docs/mllib-optimization.md ---
@@ -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
+[LogisticRegression.LogisticRegressionWithSGD](api/mllib/index.html#org.apache.spark.mllib.classification.LogisticRegression).
+See the example below. It will be addressed in the next release.
+
+The L1 regularization by using
+[Updater.L1Updater](api/mllib/index.html#org.apache.spark.mllib.optimization.Updater)
will not work since the
+soft-thresholding logic in L1Updater is designed for gradient descent.
+
+The L-BFGS method
+[LBFGS.runLBFGS](api/scala/index.html#org.apache.spark.mllib.optimization.LBFGS)
+has the following parameters:
+
+* `gradient` is a class that computes the gradient of the objective
function
+being optimized, i.e., with respect to a single training example, at the
+current parameter value. MLlib includes gradient classes for common loss
+functions, e.g., hinge, logistic, least-squares. The gradient class takes
as
+input a training example, its label, and the current parameter value.
+* `updater` is a class originally designed for gradient decent which
computes
--- End diff --
Agree. I will move it into the comment in the code.
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