Assume $x$ and $y$ are two vectors in $\mathbb{R}^3$ and we want to compute the acute angle $\alpha\in(0,\pi/2]$ between these two (noncolinear) vectors. There are (at least) two possibilities:
In the naive approach, we compute the absolute value of the dot product of the normalized vectors $x$ and $y$ $$\frac{x^Ty}{\|x\|\|y\|}$$ and take the inverse cosine of the result.
The less naive approach is based on the fact that $$\|x\times y\|=\|x\|\|y\|\sin\alpha\quad\text{and}\quad|x^Ty|=\|x\|\|y\|\cos\alpha$$ so $\alpha$ is equal to an angle in a right triangle with legs of the length $\|x\times y\|$ and $|x^Ty|$ (for convenience, one can use a variant of the inverse tangent implemented in the
atan2function which is available in most programming languages; the function takes the side lengths of the legs as two arguments).
Now assume that $x$ is given and $y=x+\Delta x$ where $\|\Delta x\|\leq\tau\|x\|$, $\tau\ll 1$, that is, the vectors are almost colinear (for simplicity also of almost same norms). Assume that $\tilde\alpha_1$ and $\tilde\alpha_2$ are, respectively, the angles computed by the naive and the less naive approaches. Recently, I've run several tests which suggest that $$\tag{1} \frac{|\alpha-\tilde\alpha_1|}{\alpha}\leq \epsilon\mathcal{O}(\tau^{-2}) \quad\text{and}\quad \frac{|\alpha-\tilde\alpha_2|}{\alpha}\leq \epsilon\mathcal{O}(\tau^{-1}), $$ where $\epsilon$ is the machine precision. I understand that both algorithms suffer from a certain inaccuracy when $y\approx x$; in particular, both computing the dot and cross products. I suppose the inverse trigonometric functions are not an issue as these are usually implemented to give very accurate results.
I'm not asking anybody for performing any kind of analysis. I was just wondering whether there is a known reference where the accuracy of the two approaches is considered, if possible revealing why (1) (probably) holds. Thanks a lot in advance.
