I am reading about the Hotelling $T^2$ test (A primer of multivariate statistic s by Richard J. Harris). It says here that the test can be seen as creating a linear combination of your variables and then obtaining the set of coefficients that maximizes the 'combined t radio'.
So I start on the math and I see how the t test is defined as:
$t(a) = \frac{a^T(\overline{X}-\mu_0)}{\sqrt{a^TSa/N}}$
When taking the square of this to get rid of the square root it looks like this:
$t^2(a) = \frac{N[a^T(\overline{X}-\mu_0)]^2}{a^TSa}$
So far so good. Now we formulate an optimization function like this:
$h(a)= N[a^T(\overline{X}-\mu_0)]^2 - \lambda(a^TSa-1) $
So basically the idea here is to maximize $h^2(a)$ subjected to the condition that $a^TSa = 1$. The lambda term appears as later on the idea is to show that the characteristic root of $NS^{-1}(\overline{X}-\mu_0)(\overline{X}-\mu_0)^T$ is the solution of the optimization problem.
Now here comes my question/problem:
The next thing that is done in this book is to derive $h(a)/da$. When I try to work this out knowing that for a symmetric matrix $A$: $\frac{\partial x^TAx}{\partial x} = 2x^TA$
I obtain: $h(a)/da = 2Na^T(\overline{X}-\mu_0)(\overline{X}-\mu_0)^T - 2\lambda a^TS$
However, the book and pretty much everywhere else shows this derivative as:
$h(a)/da = 2N(\overline{X}-\mu_0)(\overline{X}-\mu_0)^Ta - 2\lambda Sa$
What am I doing wrong? I know that $(\overline{X}-\mu_0)(\overline{X}-\mu_0)^T$ is symmetric (product of a matrix by its transpose always is), and also $S$ is symmetric because it is the estimated covariance matrix.
