I'm beginning to study Distributions, and I've encountered the following definition in Georgiev's Theory of Distributions:
Such definition implies that $C^{\infty}_c$ (with the opology given by the norm) is a normed space; however, to my understanding $C^{\infty}_c$ (with the canonical $LF$-topology) is not metrizable, and thus not normable.
Why is Georgiev giving $C^{\infty}_c$ a different topology? Will the distributions defined in Georgiev's given topology match distributions as defined in, say, Rudin's functional analysis?


It is a proper subset, even a subspace, but it is not a closed subspace. Therefore, any sequence $\{ u_n \} \subset \mathscr{C}_0^{\infty}(\mathbb{R}^n) \subset \mathscr{S}(\mathbb{R}^n)$ converging in the $\mathscr{S}(\mathbb{R}^n)$ topology is not necessarily converging to an element in $u \in \mathscr{C}_0^{\infty}(\mathbb{R}^n)$. In particular, this topology is different from the topology you linked for $\mathscr{C}_0^{\infty}(\mathbb{R}^n)$, since $\mathscr{C}_0^{\infty}(\mathbb{R}^n)$ becomes a complete space with your suggested topology.
On top of that, $\mathscr{S}$ is not a normed subspace, but rather just a metrizable one. The Schwartz space is not even normable in the first place. However, each(!) of the expression (1.1) in your pictures defines a norm.
The clear upshot of $\mathscr{S}(\mathbb{R}^n)$ is that its convergence is more natural, in the sense that you don't need any sort of "concentration", i.e. requiring each $u_n$ to have support in some compact set $K$ independent of $n$. It is also a good space for Fourier transforms, as a function and its Fourier transform cannot have both compact support, unless it's $0$. Examples of a function in $\mathscr{S}(\mathbb{R}^n) \setminus \mathscr{C}_0^{\infty}(\mathbb{R}^n)$ are Gaussians of the form $u(x)=e^{-\frac{1}{2}|x|^2}$.