1 files changed, 40 insertions, 0 deletions
diff --git a/docs/mllib-optimization.md b/docs/mllib-optimization.md
new file mode 100644
index 0000000000..428284ef29
--- /dev/null
+++ b/docs/mllib-optimization.md
@@ -0,0 +1,40 @@
+---
+layout: global
+title: MLlib - Optimization
+---
+
+* Table of contents
+{:toc}
+
+
+# Gradient Descent Primitive
+
+[Gradient descent](http://en.wikipedia.org/wiki/Gradient_descent) (along with
+stochastic variants thereof) are first-order optimization methods that are
+well-suited for large-scale and distributed computation. Gradient descent
+methods aim to find a local minimum of a function by iteratively taking steps
+in the direction of the negative gradient of the function at the current point,
+i.e., the current parameter value. Gradient descent is included as a low-level
+primitive in MLlib, upon which various ML algorithms are developed, and has the
+following parameters:
+
+* *gradient* is a class that computes the stochastic gradient of the 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 that updates weights in each iteration of gradient
+descent. MLlib includes updaters for cases without regularization, as well as
+L1 and L2 regularizers.
+* *stepSize* is a scalar value denoting the initial step size for gradient
+descent. All updaters in MLlib use a step size at the t-th step equal to
+stepSize / sqrt(t). 
+* *numIterations* is the number of iterations to run.
+* *regParam* is the regularization parameter when using L1 or L2 regularization.
+* *miniBatchFraction* is the fraction of the data used to compute the gradient
+at each iteration.
+
+Available algorithms for gradient descent:
+
+* [GradientDescent](api/mllib/index.html#org.apache.spark.mllib.optimization.GradientDescent)
+