diff --git a/src/Numerics/Optimization/LineSearch/WeakWolfeLineSearch.cs b/src/Numerics/Optimization/LineSearch/WeakWolfeLineSearch.cs
index b7acf184..bf12b146 100644
--- a/src/Numerics/Optimization/LineSearch/WeakWolfeLineSearch.cs
+++ b/src/Numerics/Optimization/LineSearch/WeakWolfeLineSearch.cs
@@ -3,6 +3,19 @@ using MathNet.Numerics.LinearAlgebra;
 
 namespace MathNet.Numerics.Optimization.LineSearch
 {
+    /// <summary>
+    /// Search for a step size alpha that satisfies the weak wolfe conditions. The weak Wolfe
+    /// Conditions are
+    /// i)  Armijo Rule:         f(x_k + alpha_k p_k) <= f(x_k) + c1 alpha_k p_k^T g(x_k)
+    /// ii) Curvature Condition: p_k^T g(x_k + alpha_k p_k) >= c2 p_k^T g(x_k)
+    /// where g(x) is the gradient of f(x), 0 < c1 < c2 < 1.
+    ///
+    /// Implementation is based on http://www.math.washington.edu/~burke/crs/408/lectures/L9-weak-Wolfe.pdf
+    ///
+    /// references:
+    /// http://en.wikipedia.org/wiki/Wolfe_conditions
+    /// http://www.math.washington.edu/~burke/crs/408/lectures/L9-weak-Wolfe.pdf
+    /// </summary>
     public class WeakWolfeLineSearch
     {
         readonly double _c1;
@@ -12,15 +25,27 @@ namespace MathNet.Numerics.Optimization.LineSearch
 
         public WeakWolfeLineSearch(double c1, double c2, double parameterTolerance, int maxIterations = 10)
         {
+            if (c1 <= 0)
+                throw new ArgumentException(string.Format("c1 {0} should be greater than 0", c1));
+            if (c2 <= c1)
+                throw new ArgumentException(string.Format("c1 {0} should be less than c2 {1}", c1, c2));
+            if (c2 >= 1)
+                throw new ArgumentException(string.Format("c2 {0} should be less than 1", c2));
+
             _c1 = c1;
             _c2 = c2;
             _parameterTolerance = parameterTolerance;
             _maximumIterations = maxIterations;
         }
 
-        // Implemented following http://www.math.washington.edu/~burke/crs/408/lectures/L9-weak-Wolfe.pdf
+        /// <param name="startingPoint">The objective function being optimized, evaluated at the starting point of the search</param>
+        /// <param name="searchDirection">Search direction</param>
+        /// <param name="initialStep">Initial size of the step in the search direction</param>
         public LineSearchResult FindConformingStep(IObjectiveFunctionEvaluation startingPoint, Vector<double> searchDirection, double initialStep)
         {
+            if (!startingPoint.IsGradientSupported)
+                throw new ArgumentException("objective function does not support gradient");
+
             double lowerBound = 0.0;
             double upperBound = Double.PositiveInfinity;
             double step = initialStep;