# Vectors/Vector Algebra
## Vector Algebra Operations
### Addition and Subtraction

If $\mathbf {a} \,$ and $\mathbf {b} \,$ are vectors, then the sum $\mathbf {c} =\mathbf {a} +\mathbf {b} \,$ is also a vector.
The two vectors can also be subtracted from one another to give another vector $\mathbf {d} =\mathbf {a} -\mathbf {b} \,$.

### Multiplication by a scalar

Multiplication of a vector $\mathbf {b} \,$ by a scalar $\lambda \,$ has the effect of stretching or shrinking the vector.
You can form a unit vector ${\hat {\mathbf {b} }}\,$ that is parallel to $\mathbf {b} \,$ by dividing by the length of the vector $|\mathbf {b} |\,$. Thus,
    ${\hat {\mathbf {b} }}={\frac {\mathbf {b} }{|\mathbf {b} |}}~.$

### Scalar product of two vectors

The scalar product or inner product or dot product of two vectors is defined as
    $\mathbf {a} \cdot \mathbf {b} =|\mathbf {a} ||\mathbf {b} |\cos(\theta )$
where $\theta \,$ is the angle between the two vectors (see Figure 2(b)).
If $\mathbf {a} \,$ and $\mathbf {b} \,$ are perpendicular to each other, $\theta =\pi /2\,$ and $\cos(\theta )=0\,$. Therefore, ${\mathbf {a} }\cdot {\mathbf {b} }=0$.
The dot product therefore has the geometric interpretation as the length of the projection of $\mathbf {a} \,$ onto the unit vector ${\hat {\mathbf {b} }}\,$ when the two vectors are placed so that they start from the same point (tail-to-tail).
The scalar product leads to a scalar quantity and can also be written in component form (with respect to a given basis) as
    ${\mathbf {a} }\cdot {\mathbf {b} }=a_{1}b_{1}+a_{2}b_{2}+a_{3}b_{3}=\sum _{i=1..3}a_{i}b_{i}~.$
If the vector is $n$ dimensional, the dot product is written as
    ${\mathbf {a} }\cdot {\mathbf {b} }=\sum _{i=1..n}a_{i}b_{i}~.$
Using the Einstein summation convention, we can also write the scalar product as
    ${\mathbf {a} }\cdot {\mathbf {b} }=a_{i}b_{i}~.$
Also notice that the following also hold for the scalar product
* ${\mathbf {a} }\cdot {\mathbf {b} }={\mathbf {b} }\cdot {\mathbf {a} }$ (commutative law).
* ${\mathbf {a} }\cdot {(\mathbf {b} +\mathbf {c} )}={\mathbf {a} }\cdot {\mathbf {b} }+{\mathbf {a} }\cdot {\mathbf {c} }$ (distributive law).

### Vector product of two vectors

The vector product (or cross product) of two vectors $\mathbf {a} \,$ and $\mathbf {b} \,$ is another vector $\mathbf {c} \,$ defined as
    $\mathbf {c} ={\mathbf {a} }\times {\mathbf {b} }=|\mathbf {a} ||\mathbf {b} |\sin(\theta ){\hat {\mathbf {c} }}$
where $\theta \,$ is the angle between $\mathbf {a} \,$ and $\mathbf {b} \,$, and ${\hat {\mathbf {c} }}\,$ is a unit vector perpendicular to the plane containing $\mathbf {a} \,$ and $\mathbf {b} \,$ in the right-handed sense.
In terms of the orthonormal basis $(\mathbf {e} _{1},\mathbf {e} _{2},\mathbf {e} _{3})\,$, the cross product can be written in the form of a determinant
    ${\mathbf {a} }\times {\mathbf {b} }={\begin{vmatrix}\mathbf {e} _{1}&\mathbf {e} _{2}&\mathbf {e} _{3}\\a_{1}&a_{2}&a_{3}\\b_{1}&b_{2}&b_{3}\end{vmatrix}}.$
In index notation, the cross product can be written as
    ${\mathbf {a} }\times {\mathbf {b} }\equiv e_{ijk}a_{j}b_{k}.$
where $e_{ijk}$ is the Levi-Civita symbol (also called the permutation symbol, alternating tensor).

## Identities from Vector Algebra

Some useful vector identities are given below.
* ${\mathbf {a} }\times {\mathbf {b} }=-{\mathbf {b} }\times {\mathbf {a} }$.
* ${\mathbf {a} }\times {\mathbf {b} +\mathbf {c} }={\mathbf {a} }\times {\mathbf {b} }+{\mathbf {a} }\times {\mathbf {c} }$.
* ${\mathbf {a} }\times {({\mathbf {b} }\times {\mathbf {c} })}=\mathbf {b} ({\mathbf {a} }\cdot {\mathbf {c} })-\mathbf {c} ({\mathbf {a} }\cdot {\mathbf {b} })$
* ${({\mathbf {a} }\times {\mathbf {b} })}\times {\mathbf {c} }=\mathbf {b} ({\mathbf {a} }\cdot {\mathbf {c} })-\mathbf {a} ({\mathbf {b} }\cdot {\mathbf {c} })$
* ${\mathbf {a} }\times {\mathbf {a} }=\mathbf {0}$
* ${\mathbf {a} }\cdot {({\mathbf {a} }\times {\mathbf {b} })}={\mathbf {b} }\cdot {({\mathbf {a} }\times {\mathbf {b} })}=\mathbf {0}$
* ${({\mathbf {a} }\times {\mathbf {b} })}\cdot {\mathbf {c} }={\mathbf {a} }\cdot {({\mathbf {b} }\times {\mathbf {c} })}$
For more details on the topics of this chapter, see Vectors in the wikibook on Calculus.
← Introduction · Vector Calculus →