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lines 6-71 of file: xrst/theory/erf_forward.xrst
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{xrst_begin erf_forward}

Error Function Forward Taylor Polynomial Theory
###############################################

Derivatives
***********
Given :math:`X(t)`, we define the function

.. math::

   Z(t) = \R{erf}[ X(t) ]

It follows that

.. math::
   :nowrap:

   \begin{eqnarray}
   \R{erf}^{(1)} ( u ) & = & ( 2 / \sqrt{\pi} ) \exp \left( - u^2 \right)
   \\
   Z^{(1)} (t) & = & \R{erf}^{(1)} [ X(t) ] X^{(1)} (t) = Y(t) X^{(1)} (t)
   \end{eqnarray}

where we define the function

.. math::

   Y(t) = \frac{2}{ \sqrt{\pi} } \exp \left[ - X(t)^2 \right]

Taylor Coefficients Recursion
*****************************
Suppose that we are given the Taylor coefficients
up to order :math:`j` for the function :math:`X(t)` and :math:`Y(t)`.
We need a formula that computes the coefficient of order :math:`j`
for :math:`Z(t)`.
Using the equation above for :math:`Z^{(1)} (t)`, we have

.. math::
   :nowrap:

   \begin{eqnarray}
   \sum_{k=1}^j k z^{(k)} t^{k-1}
   & = &
   \left[ \sum_{k=0}^j y^{(k)} t^k        \right]
   \left[ \sum_{k=1}^j k x^{(k)} t^{k-1}  \right]
   +
   o( t^{j-1} )
   \end{eqnarray}

Setting the coefficients of :math:`t^{j-1}` equal, we have

.. math::
   :nowrap:

   \begin{eqnarray}
   j z^{(j)}
   =
   \sum_{k=1}^j k x^{(k)} y^{(j-k)}
   \\
   z^{(j)}
   =
   \frac{1}{j} \sum_{k=1}^j k x^{(k)} y^{(j-k)}
   \end{eqnarray}

{xrst_end erf_forward}