From: Jean-Paul Pelteret Date: Wed, 9 May 2018 19:43:10 +0000 (+0200) Subject: Fix DA's comments X-Git-Tag: v9.1.0-rc1~1162^2 X-Git-Url: https://gitweb.dealii.org/cgi-bin/gitweb.cgi?a=commitdiff_plain;h=ae2a648d9c973f9863997c97688466145812e7ce;p=dealii.git Fix DA's comments --- diff --git a/doc/doxygen/headers/automatic_and_symbolic_differentiation.h b/doc/doxygen/headers/automatic_and_symbolic_differentiation.h index 02afcfc991..6d4e25fc72 100644 --- a/doc/doxygen/headers/automatic_and_symbolic_differentiation.h +++ b/doc/doxygen/headers/automatic_and_symbolic_differentiation.h @@ -66,7 +66,7 @@ * numbers. * (In theory an entire program could be made differentiable. This could be useful in, for example, * the sentitivity analysis of solutions with respect to input parameters. However, to date this has - * not been been tested.) + * not been tested.) * * Implementations of specialized frameworks based on operator overloading typically fall into * one of three categories. In each, some customized data classes representing the floating point value @@ -81,7 +81,7 @@ * -# The first two tapeless approaches listed above (dual numbers and complex-step method) use some * variation of a truncated Taylor series, along with a particular choice of definition for the perturbation * parameter, to compute function derivatives using a finite-difference based approach. The "dual" number - * constitutes the accumulated directional derivatives computed simultaneously as the function values is + * constitutes the accumulated directional derivatives computed simultaneously as the function values are * evaluated; in the complex-step approach, the imaginary value effectively serves this purpose. The choice of * the perturbation parameter determines the numerical qualities of the scheme, such as the influence of the * truncation of the Taylor scheme; dual numbers do not contain any higher-order terms in their first derivative, @@ -152,7 +152,7 @@ * } * @endcode * - * ### Explotation of the chain-rule + * ### Exploitation of the chain-rule * * In the most practical sense, any of the above categories exploit the chain-rule to compute the total * derivative of a composite function. To perform this action, they typically use one of two mechanisms to @@ -191,9 +191,9 @@ * * * representing the calculation of the function $f (\mathbf{x}) = x_{1} \times x_{2} + \sin (x_{1})$, - * we will briefly describe what forward- and reverse- auto-differentiation are. + * we will briefly describe what forward and reverse auto-differentiation are. * Note that in the diagram, along the edges of the graph in text are the directional - * derivative of function $w$ with respect to the i-th variable, represented by + * derivative of function $w$ with respect to the $i$-th variable, represented by * the notation $\dot{w} = \dfrac{d w}{d x_{i}}$. * The specific computations used to render the function value and its directional derivatives * for this example are tabulated in the @@ -210,9 +210,8 @@ * @f] * As was previously mentioned, if each of the primitive operations $f_{n}$ is smooth and * differentiable, then the chain can be universally employed to compute the total derivative of $f$, - * namely $\dfrac{d f(x)}{d \mathbf{x}}$. How exactly the chain-rule is applied is what - * distinguishes the "forward" from the "reverse" mode, but ultimately both compute the total - * derivative + * namely $\dfrac{d f(x)}{d \mathbf{x}}$. What distinguishes the "forward" from the "reverse" mode + * is how the chain-rule is evaluated, but ultimately both compute the total derivative * @f[ * \dfrac{d f (\mathbf{x})}{d \mathbf{x}} * = \dfrac{d f_{0}}{d f_{1}} \dfrac{d f_{1}}{d f_{2}} \dfrac{d f_{2}}{d f_{3}} \ldots \dfrac{d f_{n} (\mathbf{x})}{d \mathbf{x}} @@ -251,7 +250,9 @@ * forward-mode can be shown to be more efficient than reverse-mode. The converse is true when the * number of input variables greatly exceeds that of the output variables. This point may be used to * help inform which number type is most suitable for which set of operations are to be performed - * using automatic differentiation. + * using automatic differentiation. For example, in many applications for which second derivatives + * are to be computed it is appropriate to combine both reverse- and forward-modes. The former would + * then typically be used to calculate the first derivatives, and the latter the second derivatives. * * @subsection auto_diff_1_1 Supported automatic differentiation libraries * @@ -286,7 +287,7 @@ * provides the principle insights into their taped and tapeless implementations, and how ADOL-C * can be incorporated into a user code. * Some further useful resources for understanding the implementation of ADOL-C, and possibilities - * for how it may be used within a numerical code, include + * for how it may be used within a numerical code, include: * * @code{.bib} * @Article{Griewank1996a, @@ -335,7 +336,7 @@ * @endcode * * Similarly, a selection of useful resources for understanding the implementation of Sacado - * number types (in particular, how expression templating is employed and exploited) include + * number types (in particular, how expression templating is employed and exploited) include: * * @code{.bib} * @InCollection{Bartlett2006a,