lines 6-76 of file: xrst/theory/log_forward.xrst {xrst_begin log_forward} Logarithm Function Forward Mode Theory ###################################### Derivatives *********** If :math:`F(x)` is :math:`\R{log} (x)` or :math:`\R{log1p} (x)` the corresponding derivative satisfies the equation .. math:: ( \bar{b} + x ) * F^{(1)} (x) - 0 * F (x) = 1 where .. math:: \bar{b} = \left\{ \begin{array}{ll} 0 & \R{if} \; F(x) = \R{log}(x) \\ 1 & \R{if} \; F(x) = \R{log1p}(x) \end{array} \right. In the :ref:`standard math function differential equation` , :math:`A(x) = 0`, :math:`B(x) = \bar{b} + x`, and :math:`D(x) = 1`. We use :math:`a`, :math:`b`, :math:`d`, and :math:`z` to denote the Taylor coefficients for :math:`A [ X (t) ]`, :math:`B [ X (t) ]`, :math:`D [ X (t) ]`, and :math:`F [ X(t) ]` respectively. Taylor Coefficients Recursion ***************************** For orders :math:`j = 0 , 1, \ldots`, .. math:: :nowrap: \begin{eqnarray} z^{(0)} & = & F ( x^{(0)} ) \\ e^{(j)} & = & d^{(j)} + \sum_{k=0}^{j} a^{(j-k)} * z^{(k)} \\ & = & \left\{ \begin{array}{ll} 1 & {\rm if} \; j = 0 \\ 0 & {\rm otherwise} \end{array} \right. \\ z^{(j+1)} & = & \frac{1}{j+1} \frac{1}{ b^{(0)} } \left( \sum_{k=1}^{j+1} k x^{(k)} e^{(j+1-k)} - \sum_{k=1}^j k z^{(k)} b^{(j+1-k)} \right) \\ & = & \frac{1}{j+1} \frac{1}{ \bar{b} + x^{(0)} } \left( (j+1) x^{(j+1) } - \sum_{k=1}^j k z^{(k)} x^{(j+1-k)} \right) \end{eqnarray} {xrst_end log_forward}