I am finding a vector $\mathbf{c}$ that minimizes $J(\mathbf{c})$, that is $J(\mathbf{c}) = \text{E}\left\{\left\vert\mathbf{c}^H\mathbf{r} - a_{t}\right\vert^2\right\}$.
$\mathbf{c}$ and $\mathbf{r}$ are both vector and $a_t$ is some scalar value.
My professor taught us:
We need to find $\mathbf{c}$ such that $$\frac{\partial J(\mathbf{c}) }{ \partial \mathbf{c}^H} = \text{E}\left\{ \mathbf{r}(\mathbf{r}^H\mathbf{c} - a_t^*) \right\} = 0$$
I agree with the direction of solving the above; since $f(t)=\vert t \vert^2$ is convex, it is enough to find the point such that $df(t)/dt=0$.
However, I do not understand how $$ \frac{\partial \left\vert\mathbf{c}^H\mathbf{r} - a_{t}\right\vert^2 }{ \partial \mathbf{c}^H} = \frac{\partial}{\partial \mathbf{c}^H}\left[ (\mathbf{c}^H\mathbf{r}-a_t)(\mathbf{r}^H\mathbf{c}-a_t^*)\right] $$ becomes $$ \mathbf{r}(\mathbf{r}^H\mathbf{c} - a_t^*) $$