Gradient vector and Hessian matrix of function containing vector of complex exponentials I would like to obtain the gradient vector and Hessian matrix for the following function:  
$$f(\boldsymbol{\theta}) = \mathrm{real}\{u(\boldsymbol{\theta})^H\Gamma u(\boldsymbol{\theta})\}$$
where $u(\boldsymbol{\theta}) = \boldsymbol{u} = [e^{j\theta_1}, e^{j\theta_2}, \cdots, e^{j\theta_K}]^H$, $\boldsymbol{\theta} = [\theta_1, \theta_2, \cdots, \theta_K]^T \in \mathbb{R}^{K \times 1}$, $\Gamma \in \mathbb{C}^{K \times K}$, and $j = \sqrt{-1}$. 
From my low level of understanding, the gradient vector should look something like:
$$\boldsymbol{g} = g(\boldsymbol{\theta}) = \frac{\partial}{\partial \boldsymbol{\theta}} (\boldsymbol{u}^{H}\Gamma \boldsymbol{u}) = \frac{\partial \boldsymbol{u}}{\partial \boldsymbol{\theta}} \Gamma \boldsymbol{u} + \frac{\partial \boldsymbol{u}}{\partial \boldsymbol{\theta}} \Gamma^H \boldsymbol{u} = \frac{\partial \boldsymbol{u}}{\partial \boldsymbol{\theta}} \hat{\Gamma} \boldsymbol{u}$$
where $\hat{\Gamma} = \Gamma + \Gamma^H$.
If this is correct, then $\frac{\partial \boldsymbol{u}}{\partial \boldsymbol{\theta}} \in \mathbb{C}^{K \times K}$ for $\boldsymbol{g}$ to be a $K \times 1$ vector. Is $\frac{\partial \boldsymbol{u}}{\partial \boldsymbol{\theta}}$ therefore a diagonal matrix with $i$th diagonal entry equal to $\frac{\partial \boldsymbol{u}}{\partial \theta_i} = -je^{-j\theta_i}$?
The Hessian matrix should then be given by
$$\boldsymbol{H} = H(\boldsymbol{\theta}) = \frac{\partial }{\partial \boldsymbol{\theta}^T}(\frac{\partial \boldsymbol{u}}{\partial \boldsymbol{\theta}} \hat{\Gamma} \boldsymbol{u}) = \frac{\partial \boldsymbol{u}}{\partial \boldsymbol{\theta}}\hat{\Gamma} \frac{\partial \boldsymbol{u}}{\partial \boldsymbol{\theta}} + \mathrm{diag}\{\boldsymbol{u}^H\hat{\Gamma}^H\frac{\partial^2 \boldsymbol{u}}{\partial \boldsymbol{\theta}\partial \boldsymbol{\theta}^T}\}$$
with $\frac{\partial^2 \boldsymbol{u}}{\partial \boldsymbol{\theta}\partial \boldsymbol{\theta}^T}$ equal to a $K \times K$ diagonal matrix with $i$th diagonal entry equal to $\frac{\partial^2 \boldsymbol{u}}{\partial {\theta}_i \partial {\theta}_i} = -e^{j\theta_i}$. Here I believe the $\mathrm{diag}\{\cdot\}$ function is required to convert the $1 \times K$ vector $\boldsymbol{u}^H\hat{\Gamma}^H\frac{\partial^2 \boldsymbol{u}}{\partial \boldsymbol{\theta}\partial \boldsymbol{\theta}^T}$ into a $K \times K$ matrix; however, I am not confident about this.
The above equations produce $\boldsymbol{g} \in \mathbb{C}^{K \times 1}$ and $\boldsymbol{H} \in \mathbb{C}^{K \times K}$. Given that both $f(\boldsymbol{\theta})$ and $\boldsymbol{\theta}$ are real valued, should some modifications be made to $\boldsymbol{g}$ and $\boldsymbol{H}$ to produce a real vector and matrix, respectively? (For example, by taking only the real or imaginary component of $\boldsymbol{g}$ and $\boldsymbol{H}$.)
Through trial and error, I have been able to use the following in an implementation of Powell's "dogleg" method to achieve a reasonable level of performance (i.e., minimisation of $f(\boldsymbol{\theta})$ with varying degrees of success):
$$
\hat{\boldsymbol{g}} = -\mathrm{real}\{\frac{\partial \boldsymbol{u}}{\partial \boldsymbol{\theta}} \hat{\Gamma} \boldsymbol{u}\}
$$
$$
\hat{\boldsymbol{H}} = -\mathrm{real}\{\frac{\partial \boldsymbol{u}}{\partial \boldsymbol{\theta}} \hat{\Gamma} \frac{\partial \boldsymbol{u}}{\partial \boldsymbol{\theta}}\} + \alpha\boldsymbol{I}_K
$$
where $\alpha$ is very small and $\boldsymbol{I}_K$ is a $K \times K$ identity matrix. I have found that $\mathrm{diag}\{\boldsymbol{u}^H\hat{\Gamma}^H\frac{\partial^2 \boldsymbol{u}}{\partial \boldsymbol{\theta}\partial \boldsymbol{\theta}^T}\}$ is of lower significance than $\frac{\partial \boldsymbol{u}}{\partial \boldsymbol{\theta}}\hat{\Gamma} \frac{\partial \boldsymbol{u}}{\partial \boldsymbol{\theta}}$, and that the addition of $\alpha\boldsymbol{I}_K$ aids with inversion of $\hat{\boldsymbol{H}}$.
Any help with this would be greatly appreciated. Even a confirmation that I am close to (or nowhere near) the correct solution would be fantastic.
Thanks for reading.
 A: For ease of typing, I'll use Latin in place of your Greek letters,
$$A = \Gamma, \,\,\,\,x = \theta $$
I'll also represent the trace/Frobenius and elementwise/Hadamard products by
$$\eqalign{
 \alpha &= B:C &\implies \alpha = {\rm tr}(B^TC) \cr
 A &= B\odot C &\implies A_k = B_k C_k \cr
}$$
and the transpose, complex and hermitian conjugate of $A$ by
$\{A^T, A^*, A^H\}$ respectively.
The function and its differential are
$$\eqalign{
 f &= \frac{1}{2}\big(Au:u^* + A^*u^*:u\big) \cr
df
 &= \frac{1}{2}\big(A\,du:u^* + Au:du^* + A^*\,du^*:u + A^*u^*:du\big) \cr
 &=(Bu)^*:du + (Bu):du^* \cr
}$$ where $B = \frac{1}{2}(A+A^H)$ is the hermitian component of $A$.
Next we need the differential of the exponential function applied elementwise to a vector
$$\eqalign{
 u &= \exp(jx) \cr
 du &= u\odot d(jx) = (ju)\odot dx \cr
}$$
Substituting this, we can continue on to find the gradient with respect to $x$
$$\eqalign{
 df
 &= (Bu)^*:(ju\odot dx) + (Bu):(ju\odot dx)^* \cr
 &= ju\odot(Bu)^*:dx + (ju)^*\odot(Bu):dx \cr
 &= (VB^*u^* + V^*Bu):dx \cr
g=\frac{\partial f}{\partial x} &= VB^*u^* + V^*Bu \cr
}$$ where $V={\rm Diag}(ju)$. Being of the form $(y+y^*)$, we see that $g\in{\mathbb R}^K$ as expected. 
At this point, you should pause and consider using Conjugate Gradients, or Barzilai-Borwein, or some other method that does not require the Hessian, since that is going to be complicated. 
Also, instead of insisting on a matrix product involving $V={\rm Diag}(ju)$, you should just write code for a Hadamard product with the underlying vector.
Let 
$$\eqalign{
 W &= {\rm Diag}(jBu) \cr
 L &= (V^*B+W^*)V \cr
}$$
then the Hessian is
$$H=\frac{\partial^2f}{\partial x\partial x^T}=L+L^*$$
