The overall objective of this blog post is to foster the intuition of inner products, norms and metrics since these concepts are crucial when it comes to the definition of *geometrical concepts* such as length, distance and angle. In addition, these concepts are also needed for fundamental topis such as convergence, continuity and several concepts within topology. The focus will therefore be on the motivation behind and the interrelation between inner products, norms and metrics.

All three function types are closely related to each other since an inner product can induce a norm, which itself can finally induce a corresponding metric.

*Metrics *are nothing but *distance functions* just like a reasonable person would imagine. A *norm *can also geometrically be interpreted as the distance of a real-valued point (e.g. in the Euclidean plane) to the origin measured via a corresponding distance function (, that can be induced by this norm). The interpretation of inner product is not that obvious, but there is a helpful animated illustration of 3Blue1Brown (see below).

All three function types life in a vector space. We assume the reader knows the basics about vector spaces. A rather short introduction to Euclidean vector spaces is provided, though.

**Euclidean Spaces**

A point in the 2-dimensional plane can be modeled as an ordered pair of real numbers. Similarly, a point in a 3-dimensional space can be interpreted as an ordered triple of real numbers. In general, we consider a set of -tuples of real numbers. Usually, these type of vectors are column vectors.

Let and be in . We define equality if, and only if, , , . The sum and the difference are component-wise, that is, . The multiplication by real numbers (also called scalars) is defined by .

In modern terms, an **Euclidean space** is a vector space equipped with an “inner product”. Actually, an Euclidean space is simply a space of classical geometry. In any case, we need to define what an inner product actually is.

**Inner Product**s

An **inner product** , also called **dot product**, is a function that enables us to define and apply geometrical terms such as length, distance and angle in an Euclidean (vector) space .

Please recall that metrics (distance functions) can be induced by inner products.

**Definition:**

Let be a vector space over . An **inner product** on is a function such that for all and , the following hold:

- and ;
- ;
- .

Inner products can be generally defined as a symmetric bilinear form and a bilinear form is a bivariate functions that is linear with respect to each isolated argument. Note that the above definition can be generalized to complex vector spaces employing sesquilinear forms. We, however, restrict ourselves to real vector spaces and to the consideration of the standard inner product as defined in the following example.

**Example 1: **

The so-called **standard inner product** or **Euclidean inner product** of the real vector space is defined by for . Please convince yourself that this function actually fulfills the required properties.

For and we get = =.

Note that you can also use matrix multiplication to define the Euclidean inner product. For instance, if you have , then =. Note that the last term of the equation shows a matrix multiplication of a 1-times-2 and a 2-times-1 matrix.

An interpretation of inner products is not that obvious. A geometric illustration of dot products is explained by 3Blue1Brown. While watching bear in mind that an inner product is a **symmetric **bilinear form.

If you are interested in the duality of vector spaces, refer to this article (in German).

It might also help to learn (more) about norms and metrics. Knowing that inner products, norms and metrics are closely related to each other means that understanding one concept provides also heuristic information about the other concept.

**Norms**

Functions closely related to inner products are so-called norms. Norms are specific functions that can be interpreted as a *distance function between a vector and the origin*.

**Definition:**

Let be a real vector space over . A function with is called **norm**, and, is called **normed space** if for all the following holds true:

**(i)** if (positivity);

**(ii)** for all (homogenity);

**(iii)** (triangle inequality).

Properties (ii) and (iii) make only sense over a vector space. The former property is stating how the scalar product of a vector and a field element needs to behave to be a valid norm function.

An example will help to clarify what a norm function is.

**Example 2:****(a)** Let defined by the absolute value, that is a norm over the one-dimensional real vector space. It is always positive and only zero if and it also fulfills homogenity by definition.

To see that the triangle inequality (also called subadditivity) holds, first bear the definition of the absolute value in mind. If and as well as then . In addition, , as well as , hold true. Hence, by adding both inequalities we get as well as , as desired.

**(b)** Let , defined by , which is called **Euclidean norm**. Note that is the Euclidean inner product as defined in Example 1.

If the Euclidean norm can be interpreted as the length between the origin and the vector , then the Euclidean inner product can be interpreted as the squared norm .

Example (a) is actually the most important example of a norm since basically every practically important norm can be traced back to the absolute value function in some sense.

The Euclidean norm of the vector equals , which is exactly the length of hypotenuse of the corresponding triangle in Figure 2. That is, the Euclidean norm equals the distance between the origin and the point in the Euclidean plane, which can also be derived from Pythagorean’s theorem. It also explains why the property (iii) is called **triangle property** ( and sometimes also subaditivity).

The norm is also called **distance **between the two real-valued vectors and . The **angle ** between two vectors and with is defined by . This naturally leads us to the definition of another closely related function type.

**Metrics**

Let us directly define the important term metric.

**Definition:**

Let be a real vector space over . A function is called **metric **or **distance function** on , and, a metric space, if *for all* the following holds true:

**(i)** and if and only if ;

**(ii)** ;

**(iii)** .

Based on a day-to-day experience, the following basic considerations are reflected in (i) to (iii) of the definition of a metric:

- Distances between two points can be represented by a
*positive real number*; - Distances are
*independent from the order of measuring*. That is, the distance between two points is always the same no matter at what point you start to measure; *Detours will imply longer distances*.

Hence, the definition of a metric is nothing but an abstraction of the main features of a reasonable distance concept.

**Example 3:**

Consider defined by , whereby is the absolute value. The function is a metric since the absolute value is always positive and if and only if . Also (ii) is obvious. Property (iii), also called **sub-additivity** or **triangle property**, can be shown by distinguishing several cases. For more details reg. the properties of the absolute value function please refer to the corresponding wiki article.

Topological features do play a big role in mathematical analysis as it is together with measure theory the foundation of how things can be measured. By using the norm and/or distances, we could also define topological terms such as open sets or balls.

Probably the most prominent **family of metric spaces** is , where is defined as

(1)

with and . The case of the metrics is the absolute value as outlined in Example 3. The corresponding **normed spaces** can be defined as follows:

(2)

is directly connected to the corresponding metric spaces via .

If is a metric space, then with is also a metric space. The necessary properties are transmitted to the sub-space simply by remembering the distances between two arbitrary points . The metric is also called **metric induced by** .