>[!summary]
Dot product describes the projection of one vector onto another.
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**Key equations:**
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Generalized Dot Product is always true:
$\vec{a} \cdot \vec{b} = ||\vec{a}|| \space ||\vec{b}|| cos(\theta)$
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Where the dot product of a and b:
$\vec{a} \cdot \vec{b} = a_1b_1 + a_2b_2 \dots a_n b_n$
>[!info]+ Read Time
**⏱ 3 min**
# Deriving Dot Products
Dot product describes how much one vector goes into another vector.
>[!warning] Assumptions
We will assume the Law of Cosines is valid without proving.
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We will assume our example in $\mathbb{R^2}$ but is valid in $\mathbb{R^n}$
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As well assume we can break vectors into components and add or subtract them like in [[Vectors, Vectors Addiction & Vector Multiplication]]
Suppose we had two vectors $\vec{p}, \vec{q}$ as described below and we want to know the angles between them.
We dont know the angles nor the vector $\vec{QP}$ that lines the two vectors and creates this type of triangle.
From [[Vectors, Vectors Addiction & Vector Multiplication]] we know the following information:
$\vec{p} = \begin{bmatrix} p_1 \\ p_2\end{bmatrix},\vec{p} = \begin{bmatrix} q_1 \\ q_2\end{bmatrix}, \vec{QP} = \begin{bmatrix} p_1 - q_1 \\ p_2 - q_2\end{bmatrix} $
![[Screenshot 2025-06-22 at 1.29.52 PM.png]]
$\begin{array}{c}
\text{From the Law of Cosines we know} \\
||\vec{QP}||^2 = ||\vec{p}||^2 + ||\vec{q}||^2 -2 ||\vec{p}||\space ||\vec{q}||cos(\theta) \end{array}$
>[!bug] Note
The Law of Cosines is a scalar equation not vector equation so cannot use [[Vectors Equations of a Line in 2D]] in this case
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Remember magnitudes are described by [[Vector Length]]
$\begin{array}{c}
\text{From our known vectors we can sub in we get a new result:} \\
\vec{p} = \begin{bmatrix} p_1 \\ p_2\end{bmatrix},\vec{p} = \begin{bmatrix} q_1 \\ q_2\end{bmatrix}, \vec{QP} = \begin{bmatrix} p_1 - q_1 \\ p_2 - q_2\end{bmatrix} \\
\\
||\vec{p}||^2 = p_1^2 + p_2^2 \\
||\vec{q} ||^2 = q_1 ^2 + q_2 ^2 \\
||\vec{QP}||^2 = (p_1 - q_1)^2 + (p_2 -q_2)^2 \\
||\vec{QP}||^2 = (p_1 ^2 -2p_1q_1 +q_1^2 ) + (p_2^2 - 2p_2q_2 + q_2 ^2)
\\\\
\text{Sub back into cosine equation}\\
(p_1 ^2 -2p_1q_1 +q_1^2 ) + (p_2^2 - 2p_2q_2 + q_2 ^2) = p_1^2 + p_2^2 + q_1 ^2 + q_2 ^2 - 2 ||\vec{p}||\space ||\vec{q}||cos(\theta) \\
\\
\cancel{p_1^2 + p_2 ^2 + q_1^2 + q_2 ^2} - 2(p_1q_1 + p_2q_2 ) = \cancel{p_1^2 + p_2^2 + q_1 ^2 + q_2 ^2} - 2 ||\vec{p}||\space ||\vec{q}||cos(\theta) \\
- 2(p_1q_1 + p_2q_2 ) = - 2 ||\vec{p}||\space ||\vec{q}||cos(\theta) \\\\
p_1q_1 + p_2q_2 = ||\vec{p}||\space ||\vec{q}||cos(\theta)
\end{array}$
We will make the definition that $\vec{p} \cdot \vec{q} = p_1q_1 + p_2q_2$
## Generalization
In our generalization its important to note that the following equation always hold true for any value of $\mathbb{R^n}$ $\vec{a} \cdot \vec{b} = ||\vec{a}|| \space ||\vec{b}|| cos(\theta)$
But the value for $\vec{a} \cdot \vec{b}$ will change depending on $\mathbb{R^n}$:
$\vec{a} \cdot \vec{b} = a_1b_1 + a_2b_2 \dots a_n b_n$
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