Past exam of the options II for mathematics and philosophy course of the University of Oxford / 2013 / F2 / a

The total derivative of $F$ at $x$ is the linear map $D{F}_x:{\mathbb{R}}^n\to {\mathbb{R}}^m$ such thatThe Jacobian matrix is the $m\times n$ matrix $J_F(x)=(\partial F_i/\partial x_j)(x)$. When the total derivative exists, this is its matrix in standard bases: $D{F}_x(h)=J_F(x)h$. Existence of partial derivatives alone need not imply existence of the total derivative.

The map is continuously differentiable if it is differentiable at every point of $U$ and the derivative map $x\mapsto D{F}_x$ is continuous. In finite dimensions this is equivalent to all first partial derivatives of all its components existing and being continuous on $U$. The class is denoted $C^1(U,{\mathbb{R}}^m)$.

If $F:U\subseteq {\mathbb{R}}^n\to {\mathbb{R}}^m$ is differentiable at $x$, and $G$ is defined on a neighborhood of $F(x)$ and differentiable there, the chain rule statesIn standard coordinates the same statement isThe evaluation point in the first factor is $F(x)$, and the matrix order follows the order in which the linear maps act on increments.

For an increment $h$, direct expansion and the chain rule giveFor example, $\Vert x+h{\Vert }^2-\Vert x{\Vert }^2=2x^{T}h+\Vert h{\Vert }^2$, so the quadratic remainder divided by $\Vert h\Vert$ tends to zero. The corresponding gradients are $\nabla g(x)=2x$ and $\nabla k(x)=2A^{T}Ax$; the derivative of $F$ is the constant matrix $A$.