Inner product in vector space

The inner product is an operation that measures the similarity between vectors.  In a general way, the inner product could be defined as an operation of 2 operands, which are elements of a vector space. The result is a scalar in the set of the complex numbers:

$\left \langle \cdot, \cdot \right \rangle : V \times V \rightarrow \mathbb{C}$

Formal properties

For $x, y, z \in V$ and $\alpha \in \mathbb{C}$, the inner product must fulfill the following rules:

To be distributive to vector addition:

$\left \langle x+y, z \right \rangle = \left \langle x, z \right \rangle + \left \langle y, z \right \rangle$

Conmutative with conjugate (applies when vectors are complex):

$\left \langle x,y \right \rangle = \left \langle y, x \right \rangle^*$

Distributive respect scalar multiplication:

$\left \langle \alpha x, y \right \rangle = \alpha^* \left \langle x, u \right \rangle$

$\left \langle x, \alpha y \right \rangle = \alpha \left \langle x, u \right \rangle$

The self inner product must be necessarily a real number:

$\left \langle x, x \right \rangle \geq 0$

The self inner product can be zero only when the element is the null element:

$\left \langle x,x \right \rangle = 0 \Leftrightarrow x = 0$

Inner product in $\mathbb{R}^2$

The inner product in $\mathbb{R}^2$ is defined as follows:

$\left \langle x, y \right \rangle = x_0 y_0 + x_1 y_1$

In self inner product represents the squared norm of the vector:

$\left \langle x, x \right \rangle = x^2_0 + y^2_0 = \left \| x \right \|^2$

Inner product in finite length signals

In this case, the inner product is defined as:

$\left \langle x ,y \right \rangle = \sum_{n= 0}^{N-1} x^*[n] y[n]$

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