updated docs

docs/docs/optimization.xml

@@ -504,11 +504,12 @@ subject to the following constraint:
              This function solves the following quadratic program:
 <pre>
    Minimize: f(alpha,lambda) == 0.5*trans(alpha)*Q*alpha - trans(alpha)*b + 
-                                0.5*trans(lambda)*lambda - trans(lambda)*A*alpha
+                                0.5*trans(lambda)*lambda - trans(lambda)*A*alpha - trans(lambda)*d
    subject to the following constraints:
       sum(alpha)  == C 
       min(alpha)  >= 0 
       min(lambda) >= 0
+      max(lambda) <= max_lambda 
    Where f is convex.  This means that Q should be positive-semidefinite.
 </pre>
 
@@ -742,6 +743,13 @@ Or it can alternatively solve:
 
    Where prior is a user supplied vector and R(w) has the same
    interpretation as above.
+
+Or it can use the elastic net regularizer:
+   Minimize: f(w) == 0.5*(1-lasso_lambda)*length_squared(w) + lasso_lambda*sum(abs(w)) + C*R(w)
+
+   Where lasso_lambda is a number in the range [0, 1) and controls
+   trade-off between doing L1 and L2 regularization.  R(w) has the same
+   interpretation as above.
 </pre>
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