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# Calculus

Last updated Sep 12, 2022 Edit Source

## # Partial Derivatives

### # Linear

Functions with more than one variable. e.g. $f(x)$ where $x \in \mathbb{R}^3$ the following multivariate linear

$$$$\begin{split} f(x_1, x_2, x_3)& = a_1x_1 + a_2x_2 + a_3x_3 + b \\ & = \sum_{i=1}^3 a_ix_i + b \\ & = a^Tx + b \end{split}$$$$

The gradient is then the partial derivative with respect to each variable

$$\nabla f(x) = \begin{bmatrix}\frac{\partial f}{\partial x_1} \\ \frac{\partial f}{\partial x_2} \\ \frac{\partial f}{\partial x_3}\end{bmatrix} = \begin{bmatrix}a_1 \\ a_2 \\ a_3\end{bmatrix}$$

e.g. $f(x)$ where $x \in \mathbb{R}^2$ and $A = \begin{bmatrix}2 & -1 \\ -1 & 1\end{bmatrix}$
$$$$\begin{split} f(x)& = \frac 1 2 x^TAx + b^Tx + c \\ & = \sum_{i=1}^2 \sum_{j=1}^2 a_{ij}x_ix_j + \sum_{i=1}^2b_ix_i + c \end{split}$$$$
If $A$ is symmetric, $\nabla f(x) = Ax+b$. In the non-symmetric case, $\nabla f(x) = \frac 1 2 (A + A^T)x + b$