# Linear Algebra/Inner Product Spaces

Recall that in your study of vectors, we looked at an operation known as the *dot product*, and that if we have two vectors in **R**^{n}, we simply multiply the components together and sum them up. With the dot product, it becomes possible to introduce important new ideas like length and angle. The lenght of a vector,, is just . The angle between two vectors, and , is related to the dot product by

It turns out that only a few properties of the dot product are necessary to define similar ideas in vector spaces other than **R**^{n}, such as the spaces of matrices, or polynomials. The more general operation that will take the place of the dot product in these other spaces is called the "inner product".

## The inner product[edit]

Say we have two vectors:

If we want to take their dot product, we would work as follows

Because in this case multiplication is commutative, we then have * a*·

*=*

**b***·*

**b***.*

**a**But then, we observe that

much like the regular algebraic equality *v*(*aA*+*bB*)=*avA*+*bvB*.
For regular dot products this is true since, for **R**^{3}, for example, one can expand both sides out to obtain

Finally, we can notice that * v*·

*is always positive or greater than zero - checking this for*

**v****R**

^{3}gives this as

which can never be less than zero since a real number squared is positive. Note that * v*·

*= 0 if and only if*

**v***=*

**v****0**.

In generalizing this sort of behaviour, we want to keep these three behaviours. We can then move on to a definition of a generalization of the dot product, which we call the *inner product*. An inner product of two vectors in some vector space *V*, written < * x*,

*> is a function that maps*

**y***V*×

*V*to

**R**, which obeys the property that

- <
,**x**> = <**y**,**y**>**x** - <
, α**v**+β**a**> = α <**b**,**v**> + β <**a**,**v**>**b** - <
,**a**> ≥ 0, <**a**,**a**> = 0 iff**a**=**a****0**.

The vector space *V* and some inner product together are known as an *inner product space*.