Pythagorean Theorem \begin{equation} |u + v|^{2} = |u |^{2} + |v|^{2} \end{equation} if v and u are orthogonal vectors. Proof: An Useful Orthogonal Decomposition Suppose we have a vector u, and another v, both belonging to V. We can decompose u as a sum of two vectors given a choice of v: one a scalar multiple of v, and another orthogonal to v. That is: we can write u = cv + w, where c \in \mathbb{F} and w \in V, such that \langle w,v \rangle = 0. Here’s how: For nonzero v
and
We can show \langle w,v \rangle=0 as follows:
Cauchy-Schwartz Inequality \begin{equation} \left(\sum_{k=1}^{n} a_{k} b_{k}\right)^{2} \leq \left(\sum_{k=1}^{n} a_{k}^{2}\right)\left(\sum_{k=1}^{n} b_{k}^{2}\right) \end{equation} or more generally
and the expression is an equality of each vector u,v is the scalar multiple of the other. Proof: Pick some set of v and u and write out the orthogonal decomposition we had outlined above:
Now, recall c = \frac{\langle u,v \rangle}{\|v\|^{2}}. We now apply Pythagorean Theorem: Now we just multiply \|v\|^{2} to both sides and take square roots. If w = 0 (i.e. v and w have no othogonal component, and therefore they are scalar multiples), then this would turn into an equality as desired. triangle inequality (vectors) See also triangle inequality (complexes) “The length of u+v is always less than the length of each u plus v; the third side length is always shorter than the sum of both other sides’ lengths.”
Notably, the two lines between 2|\langle u,v \rangle| and 2 \|u\| \|v\| holds because of the Cauchy-Schwartz Inequality. This inequality becomes an equality if u and v are a non-negative multiple of the other. parallelogram equality The sums of squared side lengths of a parallelogram is equal to the sum of the squares of the length of diagonals: