# Metric Space

### Vector Space

A vector space is set of mathemetical objects that can be multiplied and added together to produce objects of the same kind. This notion of vector spaces proves to be a very useful framework for extending methods and structures to very different types of problems. A few special types of vector spaces you may be already be familiar with:

• Function Spaces. We can add functions together and scale them as well. Figure 1.

In Figure 1 we can see that the function in blue $f(x) = x$, the function in red $g(x) = \sin(x)$ and in green $(f + g)(x) = \sin(x) + x$

• Euclidean Spaces. We can add $i, j, k$ together and scale them as well to produce a vector $a$. Figure 2.

Typically this is the vector space you learn in linear algebra because it is intuitive to understand and very helpful for extending additional structure into spaces.

### More Formally

Definition: A field is a set $\mathbb{F}$ of numbers, such that $a, b \in \mathbb{F} \implies a+ b, a - b, ab, \frac{a}{b} \in \mathbb{F}.$ Some examples of sets that would count as fields are the real numbers $\mathbb{R}$ and the complex numbers $\mathbb{C}$.

Definition: A Vector Space consists of a set $\textbf{V}$, a field $\mathbb{F}$ and two operations:

• Vector Addition: $\textbf{a}, \textbf{b} \in \textbf{V} \implies \textbf{a} + \textbf{b} \in \textbf{V}$
• Scalar Multiplication: $c \in \mathbb{F}, \textbf{v} \in \textbf{V} \implies c\textbf{v} \in \textbf{V}$

### Metric Space

Essentially a metric is a measurement to see how similar two vectors are to one another. In geometrical terms, this similarity could be interpreted as distance.

Definition: A Metric Space consists of a set $\textbf{V}$, and has a notion of distance $d(x,y) \ \forall x, y \in \textbf{V}$. That is $d : \textbf{V} \times \textbf{V} \to [0;\infty)$ and satisfies these following properties $\forall x,y,z \in \textbf{V}$:

• Identification: $d(x,y) = 0 \implies x = y$
• Symmetry: $d(x,y) = d(y,x)$
• Triangle Inequality: $d(x,y) \leq d(x,z) + d(z,y)$

As long as these axioms hold true for a particular vector space with a function d, then it can be categorized as a metric space. A few examples you may be familiar with are the euclidean distance metric, or L2. $$d(x,y) = \sqrt{\sum_{i = 0}^n \mid x_i - y_i\mid^2}$$