How to take the gradient of the quadratic form?

It's stated that the gradient of:

$$\frac{1}{2}x^TAx - b^Tx +c$$

is

$$\frac{1}{2}A^Tx + \frac{1}{2}Ax - b$$

How do you grind out this equation? Or specifically, how do you get from $x^TAx$ to $A^Tx + Ax$?

The only thing you need to remember/know is that $$\dfrac{\partial (x^Ty)}{\partial x} = y$$ and the chain rule, which goes as $$\dfrac{d(f(x,y))}{d x} = \dfrac{\partial (f(x,y))}{\partial x} + \dfrac{d( y^T(x))}{d x} \dfrac{\partial (f(x,y))}{\partial y}$$ Hence, $$\dfrac{d(b^Tx)}{d x} = \dfrac{d (x^Tb)}{d x} = b$$

$$\dfrac{d (x^TAx)}{d x} = \dfrac{\partial (x^Ty)}{\partial x} + \dfrac{d (y(x)^T)}{d x} \dfrac{\partial (x^Ty)}{\partial y}$$ where $y = Ax$. And then, that is,

$$\dfrac{d (x^TAx)}{d x} = \dfrac{\partial (x^Ty)}{\partial x} + \dfrac{d( y(x)^T)}{d x} \dfrac{\partial (x^Ty)}{\partial y} = y + \dfrac{d (x^TA^T)}{d x} x = y + A^Tx = (A+A^T)x$$

Let $f : \mathbb R^n \to \mathbb R$ be defined by

$$f (\mathrm x) := \rm x^\top A \, x$$

Hence,

$$f (\mathrm x + h \mathrm v) = (\mathrm x + h \mathrm v)^\top \mathrm A \, (\mathrm x + h \mathrm v) = f (\mathrm x) + h \, \mathrm v^\top \mathrm A \,\mathrm x + h \, \mathrm x^\top \mathrm A \,\mathrm v + h^2 \, \mathrm v^\top \mathrm A \,\mathrm v$$

Thus, the directional derivative of $f$ in the direction of $\rm v$ at $\rm x$ is

$$\lim_{h \to 0} \frac{f (\mathrm x + h \mathrm v) - f (\mathrm x)}{h} = \mathrm v^\top \mathrm A \,\mathrm x + \mathrm x^\top \mathrm A \,\mathrm v = \langle \mathrm v , \mathrm A \,\mathrm x \rangle + \langle \mathrm A^\top \mathrm x , \mathrm v \rangle = \left\langle \mathrm v , \color{blue}{\left(\mathrm A + \mathrm A^\top\right) \,\mathrm x} \right\rangle$$

Lastly, the gradient of $f$ with respect to $\rm x$ is

$$\nabla_{\mathrm x} \, f (\mathrm x) = \color{blue}{\left(\mathrm A + \mathrm A^\top\right) \,\mathrm x}$$

multivariable-calculus scalar-fields gradient quadratic-forms

I am just writing this answer for future reference and for clarity because the accepted answer is not completely correct and may case confusion.

I will use a simple proof to better understand the multiplication rule in the calculation of $\nabla\mathbf{x^TAx}$.

For $\mathbf{x}\in \mathbb{R}^n$ and $\mathbf{A}\in \mathbb{R}^{n\times n}$ let: $$f(g(\mathbf{x}),h(\mathbf{x}))=\langle g(\mathbf{x}),h(\mathbf{x})\rangle=g^T(\mathbf{x})h(\mathbf{x})$$

Where: \begin{equation} \begin{split} &g(\mathbf{x})=\mathbf{x}\\ &h(\mathbf{x})=\mathbf{Ax} \end{split} \end{equation}

From the definition of $f$, it is obvious that $f(g(\mathbf{x}),h(\mathbf{x}))=\mathbf{x^TAx}$.

In order to calculate the derivative, we will use the following fundamental properties, where $\mathbf{I}$ is the identity matrix:

\begin{equation} \begin{split} &\dfrac{\partial \mathbf{A^Tx}}{\partial\mathbf{x}}=\dfrac{\partial \mathbf{x^TA}}{\partial\mathbf{x}}=\mathbf{A^T}\\ &\dfrac{\partial \mathbf{x}}{\partial\mathbf{x}}=\mathbf{I} \end{split} \end{equation}

Hence, from the multiplication rule (you can see the rule at wikipedia), we got: \begin{equation} \begin{split} \dfrac{df(g(\mathbf{x}),h(\mathbf{x}))}{d\mathbf{x}}&=g^T(\mathbf{x})\dfrac{\partial h(\mathbf{x})}{\partial\mathbf{x}}+h^T(\mathbf{x})\dfrac{\partial g(\mathbf{x})}{\partial\mathbf{x}}=\\ &=\mathbf{x^T}\dfrac{\partial \mathbf{Ax}}{\partial\mathbf{x}}+(\mathbf{Ax})^T\dfrac{\partial \mathbf{x}}{\partial\mathbf{x}}=\\ &=\mathbf{x^TA}+\mathbf{x^TA^TI}=\\ &=\mathbf{x^TA}+\mathbf{x^TA^T}=\\ &=\mathbf{x^T}(\mathbf{A+A^T}) \end{split} \end{equation}

As a result, from the definition of gradient, we got: $$ \nabla f=\Bigg(\dfrac{df}{d\mathbf{x}}\Bigg)^T=(\mathbf{x^T}(\mathbf{A+A^T}))^T=(\mathbf{A^T+A})\mathbf{x} $$

Note: The reason I did the proof this way is to be more generalizable. So you can can plug arbitrary functions $g$ and $h$ and use the above multiplication rule to derive the result.

There is another way to calculate the most complex one, $\frac{\partial}{\partial \theta_k} \mathbf{x}^T A \mathbf{x}$. It only requires nothing but partial derivative of a variable instead of a vector.

This answer is for those who are not very familiar with partial derivative and chain rule for vectors, for example, me. Therefore, although it seems long, it is actually because I write down all the details. :)

Firstly, expanding the quadratic form yields: $$ \begin{align} f = \frac{\partial}{\partial \theta_k} \mathbf{x}^T A \mathbf{x} &= \frac{\partial}{\partial \theta_k} \sum_{i=1}^N \sum_{j=1}^N a_{ij}\frac{\partial}{\partial \theta_k}(\mathbf{x}_i \mathbf{x}_j) \end{align} $$ Since $$ \frac{\partial}{\partial \theta_k}(\mathbf{x}_i \mathbf{x}_j) = \begin{cases} \mathbf{x}_j, && \text{if } k = i \\ \mathbf{x}_i, && \text{if } k = j \\ 0, && \text{otherwise} \end{cases} $$ The equation is nothing but $$ f = \sum_{j=1}^N a_{kj} \mathbf{x}_j + \sum_{i=1}^N a_{ik} \mathbf{x}_i $$ Almost done! Now we only need some simplification. Recall the very simple rule that $$ \sum_{i=1}^N x_i y_i = \begin{bmatrix} x_1 \\ \vdots \\ x_n \end{bmatrix}^T \begin{bmatrix} y_1 \\ \vdots \\ y_n \end{bmatrix} = \mathbf{x}^T \mathbf{y} $$ Thus $$ \begin{align} f &= \text{(k-th row of A) } \mathbf{x} + \text{(k-th column of A)}^T \mathbf{x} \end{align} $$ Now it is time to compute the gradient from partial derivative! $$ \begin{align} \nabla_\mathbf{x} \mathbf{x}^T A \mathbf{x} & = \begin{bmatrix} \frac{\partial \mathbf{x}^T A \mathbf{x}}{\partial x_1} \\ \vdots \\ \frac{\partial \mathbf{x}^T A \mathbf{x}}{\partial x_k} \\ \vdots \\ \frac{\partial \mathbf{x}^T A \mathbf{x}}{\partial x_N} \\ \end{bmatrix} = \begin{bmatrix} \vdots \\ \text{(k-th row of A) } \mathbf{x} + \text{(k-th column of A)}^T \mathbf{x} \\ \vdots \end{bmatrix} \\ &= \left( \begin{bmatrix} \vdots \\ \text{(k-th row of A) } \\ \vdots \end{bmatrix} + \begin{bmatrix} \vdots \\ \text{(k-th column of A) }^T \\ \vdots \end{bmatrix} \right) \mathbf{x} \\ &= (A + A^T)\mathbf{x} \end{align} $$ So we are done!! The answer is: $$ \nabla_\mathbf{x} \mathbf{x}^T A \mathbf{x} = (A + A^T)\mathbf{x} $$