# Computing Hessian of a particular function

Let $x \in \mathbb{R}^k$, $D \in \mathbb{R}^{k \times k}$, and $b \in \mathbb{R}$. Assume $D$ is symmetric. Consider the function $f : \mathbb{R}^k \rightarrow \mathbb{R}$ defined by $$f(x) = (x^TDx - b^2)^2.$$ I want to compute the Hessian $\nabla^2f$. The gradient can be computed via the chain rule: \begin{align*} \nabla f(x) & = 2(x^TDx - b^2)Dx. \\ & = 2[(x^TDx)Dx - b^2Dx].\end{align*} If we differentiate the second term again, we get $\nabla(b^2Dx) = b^2D$. However, I am not sure how to differentiate the first term. Can anyone help with this?

• Shouldn't be \begin{align*} \nabla f(x) & = 4(x^TDx - b^2)Dx. \\ & = 4[(x^TDx)Dx - b^2Dx].\end{align*}. ? – Red shoes Jun 16 '17 at 7:14

Let $F(x) = (x^TDx)Dx$ then $F: R^k \to R^k.$ To find the derivative of $F$ we only need to find the gredient of each component (row) of $F$. Here is the gradient of first component of $F$ i.e., $$F_1 (x) = (x^TDx ) \langle d^1 , x \rangle$$

Where $d^1$ is the first row of $D$. Therefore

$$\nabla F_1 (x) = 2\langle d^1 , x \rangle Dx + (x^TDx ) d^1$$

Let's do the calculation in coordinates. Write $D = (d_{ij})$. Then

$$f(x) = \left( \sum_{i,j=1}^n d_{ij} x_i x_j - b^2 \right)^2.$$

We have

$$\frac{\partial f}{\partial x_k} = 2 \left( \sum_{i,j=1}^n d_{ij} x_i x_j - b^2 \right) \left( \sum_{j=1}^n d_{kj} x_j\right)$$

and

$$\frac{\partial^2 f}{\partial x_l x_k} = 2 \left( \left( \sum_{j = 1}^n d_{lj} x_j \right)\left( \sum_{j=1}^n d_{kj} x_j \right) + \left( \sum_{i,j=1}^n d_{ij} x_i x_j - b^2 \right) d_{kl}\right).$$