Just a quick cheatsheet on derivatives (of scalars and vectors) wrt of a vector. This is borrowed from the wiki page : Matrix Calculus.

## Vector Calculus CAS Tricks

The following document is borrowed from here.

Oftentimes, we need to get linear approximation of a complicated function. This is ho you can get one with maxima

f(x,y) := exp(x^2) * sin(y); taylor(f(x,y), [x,y], [1,2], 1);

**Maxima Resources**

- https://www2.palomar.edu/users/cchamberlin/Math%20205%20pages/Maxima/MaximaBook.pdf
- http://web.csulb.edu/~woollett/
- gkerns.people.ysu.edu/maxima/(opens in a new tab)

## Notations

Usually, in print following notations are in use:

**A** : Matrix (capital and bold)

**b** : Vector (small and bold)

c : scalar (small and not bold)

## The rules for derivatives of a scalar by a vector

## The rules for derivatives of a vector by a vector

Using all these rules, I have derived the commonly used least squares equation. The error function is a scalar. The optimization variable (the unknown is a vector). We are trying to minimize the error, hence we need to take the gradient (vector derivative) of error with respect to the optimization variable( the unknown) and set it to zero-vector. The values that make the gradient zeros will also be optimal value for the error (here minimum).

Hope this helps!