Updated read me.
-# finmath-AAD-extention
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+# finmath-lib automatic differentiation extentions
+
+This project provides and interface (RandomVariableDifferentiableInterface)
+for random variables which provide automatic differentiation. The interfaces
+extends RandomVariableInterface and hence allows to use auto-diff in all
+Monte-Carlo contexts (via a replacement of the corresponding parameters / factories).
+
+The project provides an impelemntation of the backward (a.k.a. adjoint)
+method via RandomVariableDifferentiableAADFactory.
+
+The interface RandomVariableDifferentiableInterface will introduce
+two additional methods:
+
+ Long getID();
+ Map<Long, RandomVariableInterface> getGradient();
+
+The method getGradient will return a map providing the first order
+differentiation of the given random variable (this) with respect to
+*all* its input <code>RandomVariableDifferentiableInterface</code>s (leave nodes). To get the differentation with respect to a specific object use
+
+ Map gradient = X.getGradient();
+ RandomVariableInterface derivative = X.get(Y.getID());
+
