From: Jean-Paul Pelteret Date: Sun, 12 May 2019 22:22:26 +0000 (+0200) Subject: Fix some spelling errors in documentation of AD module X-Git-Tag: v9.1.0-rc1~37^2 X-Git-Url: https://gitweb.dealii.org/cgi-bin/gitweb.cgi?a=commitdiff_plain;h=refs%2Fpull%2F8120%2Fhead;p=dealii.git Fix some spelling errors in documentation of AD module --- diff --git a/doc/doxygen/headers/automatic_and_symbolic_differentiation.h b/doc/doxygen/headers/automatic_and_symbolic_differentiation.h index 6832ff3e7c..ff8f13cec9 100644 --- a/doc/doxygen/headers/automatic_and_symbolic_differentiation.h +++ b/doc/doxygen/headers/automatic_and_symbolic_differentiation.h @@ -65,7 +65,7 @@ * the Tensor and SymmetricTensor classes should support calculations performed with these specialized * 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 + * the sensitivity analysis of solutions with respect to input parameters. However, to date this has * not been tested.) * * Implementations of specialized frameworks based on operator overloading typically fall into @@ -120,7 +120,7 @@ * function. * * Each of these methods, of course, has its advantages and disadvantages, and one may be more appropriate - * than another for a given problem that is to be solved. As the aforemetioned implementational details + * than another for a given problem that is to be solved. As the aforementioned implementational details * (and others not discussed) may be hidden from the user, it may still be important to understand the * implications, run-time cost, and potential limitations, of using any one of these "black-box" * auto-differentiable numbers. @@ -157,8 +157,8 @@ * 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 * compute derivatives, specifically - * - forward-mode (or forward accumulation) auto-differentation, or - * - reverse-mode (or reverse accumulation) auto-differentation. + * - forward-mode (or forward accumulation) auto-differentiation, or + * - reverse-mode (or reverse accumulation) auto-differentiation. * * As a point of interest, the optimal Jacobian accumulation, which performs a minimal set of * computations, lies somewhere between these two limiting cases. Its computation for a general composite