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