Residue classes of integers

Residue classes in the rings ${\displaystyle \mathbb {Z} /N\mathbb {Z} }$ can be added:

${\displaystyle (a+N\mathbb {Z} )+(b+N\mathbb {Z} )=a+b+N\mathbb {Z} }$

and multiplied:

${\displaystyle (a+N\mathbb {Z} )\cdot (b+N\mathbb {Z} )=a\cdot b+N\mathbb {Z} }$

Convolution

The convolution of the square wave with itself gives the triangular function

Two functions from the reals to itself can be multiplied in another way, called the convolution.

If

${\displaystyle \int \limits _{-\infty }^{\infty }|f(t)|\,\mathrm {d} t<\infty \qquad {\mbox{and}}\qquad \int \limits _{-\infty }^{\infty }|g(t)|\,\mathrm {d} t<\infty ,}$

then the integral

${\displaystyle (f*g)(t)\;:=\int \limits _{-\infty }^{\infty }f(\tau )\cdot g(t-\tau )\,\mathrm {d} \tau }$

is well defined and is called the convolution.

Under the Fourier transform, convolution becomes point-wise function multiplication.

Polynomial rings

The product of two polynomials is given by the following:

${\displaystyle \left(\sum _{i=0}^{n}a_{i}X^{i}\right)\cdot \left(\sum _{j=0}^{m}b_{j}X^{j}\right)=\sum _{k=0}^{n+m}c_{k}X^{k}}$

with

${\displaystyle c_{k}=\sum _{i+j=k}a_{i}\cdot b_{j}}$

Scalar multiplication

By the very definition of a vector space, one can form the product of any scalar with any vector, giving a map ${\displaystyle \mathbb {R} \times V\rightarrow V}$.

Scalar product

A scalar product is a bi-linear map:

${\displaystyle \cdot :V\times V\rightarrow \mathbb {R} }$

with the following conditions, that ${\displaystyle v\cdot v>0}$ for all ${\displaystyle 0\not =v\in V}$.

From the scalar product, one can define a norm by letting ${\displaystyle \|v\|:={\sqrt {v\cdot v}}}$.

The scalar product also allows one to define an angle between two vectors:

${\displaystyle \cos \angle (v,w)={\frac {v\cdot w}{\|v\|\cdot \|w\|}}}$

In ${\displaystyle n}$-dimensional Euclidean space, the standard scalar product (called the dot product) is given by:

${\displaystyle \left(\sum _{i=1}^{n}\alpha _{i}e_{i}\right)\cdot \left(\sum _{i=1}^{n}\beta _{i}e_{i}\right)=\sum _{i=1}^{n}\alpha _{i}\,\beta _{i}}$

Cross product in 3-dimensional space

The cross product of two vectors in 3-dimensions is a vector perpendicular to the two factors, with length equal to the area of the parallelogram spanned by the two factors.

The cross product can also be expressed as the formal^{[lower-alpha 1]} determinant:

${\displaystyle \mathbf {u\times v} ={\begin{vmatrix}\mathbf {i} &\mathbf {j} &\mathbf {k} \\u_{1}&u_{2}&u_{3}\\v_{1}&v_{2}&v_{3}\\\end{vmatrix}}}$

Composition of linear mappings

A linear mapping can be defined as a function f between two vector spaces V and W with underlying field F, satisfying^[3]

${\displaystyle f(t_{1}x_{1}+t_{2}x_{2})=t_{1}f(x_{1})+t_{2}f(x_{2}),\forall x_{1},x_{2}\in V,\forall t_{1},t_{2}\in \mathbb {F} .}$

If one only considers finite dimensional vector spaces, then

${\displaystyle f(\mathbf {v} )=f\left(v_{i}\mathbf {b_{V}} ^{i}\right)=v_{i}f\left(\mathbf {b_{V}} ^{i}\right)={f^{i}}_{j}v_{i}\mathbf {b_{W}} ^{j},}$

in which b_V and b_W denote the bases of V and W, and v_i denotes the component of v on b_Vⁱ, and Einstein summation convention is applied.

Now we consider the composition of two linear mappings between finite dimensional vector spaces. Let the linear mapping f map V to W, and let the linear mapping g map W to U. Then one can get

${\displaystyle g\circ f(\mathbf {v} )=g\left({f^{i}}_{j}v_{i}\mathbf {b_{W}} ^{j}\right)={g^{j}}_{k}{f^{i}}_{j}v_{i}\mathbf {b_{U}} ^{k}.}$

Or in matrix form:

${\displaystyle g\circ f(\mathbf {v} )=\mathbf {G} \mathbf {F} \mathbf {v} ,}$

in which the i-row, j-column element of F, denoted by F_ij, is f^j_i, and G_ij=g^j_i.

The composition of more than two linear mappings can be similarly represented by a chain of matrix multiplication.

Product of two matrices

Given two matrices

${\displaystyle A=(a_{i,j})_{i=1\ldots s;j=1\ldots r}\in \mathbb {R} ^{s\times r}}$ and ${\displaystyle B=(b_{j,k})_{j=1\ldots r;k=1\ldots t}\in \mathbb {R} ^{r\times t}}$

their product is given by

${\displaystyle B\cdot A=\left(\sum _{j=1}^{r}a_{i,j}\cdot b_{j,k}\right)_{i=1\ldots s;k=1\ldots t}\;\in \mathbb {R} ^{s\times t}}$

Composition of linear functions as matrix product

There is a relationship between the composition of linear functions and the product of two matrices. To see this, let r = dim(U), s = dim(V) and t = dim(W) be the (finite) dimensions of vector spaces U, V and W. Let ${\displaystyle {\mathcal {U}}=\{u_{1},\ldots ,u_{r}\}}$ be a basis of U, ${\displaystyle {\mathcal {V}}=\{v_{1},\ldots ,v_{s}\}}$ be a basis of V and ${\displaystyle {\mathcal {W}}=\{w_{1},\ldots ,w_{t}\}}$ be a basis of W. In terms of this basis, let ${\displaystyle A=M_{\mathcal {V}}^{\mathcal {U}}(f)\in \mathbb {R} ^{s\times r}}$ be the matrix representing f : U → V and ${\displaystyle B=M_{\mathcal {W}}^{\mathcal {V}}(g)\in \mathbb {R} ^{r\times t}}$ be the matrix representing g : V → W. Then

${\displaystyle B\cdot A=M_{\mathcal {W}}^{\mathcal {U}}(g\circ f)\in \mathbb {R} ^{s\times t}}$

is the matrix representing ${\displaystyle g\circ f:U\rightarrow W}$.

In other words: the matrix product is the description in coordinates of the composition of linear functions.

Tensor product of vector spaces

Given two finite dimensional vector spaces V and W, the tensor product of them can be defined as a (2,0)-tensor satisfying:

${\displaystyle V\otimes W(v,m)=V(v)W(w),\forall v\in V^{*},\forall w\in W^{*},}$

where V^* and W^* denote the dual spaces of V and W.^[4]

For infinite-dimensional vector spaces, one also has the:

• Tensor product of Hilbert spaces
• Topological tensor product.

The tensor product, outer product and Kronecker product all convey the same general idea. The differences between these are that the Kronecker product is just a tensor product of matrices, with respect to a previously-fixed basis, whereas the tensor product is usually given in its intrinsic definition. The outer product is simply the Kronecker product, limited to vectors (instead of matrices).

The class of all objects with a tensor product

In general, whenever one has two mathematical objects that can be combined in a way that behaves like a linear algebra tensor product, then this can be most generally understood as the internal product of a monoidal category. That is, the monoidal category captures precisely the meaning of a tensor product; it captures exactly the notion of why it is that tensor products behave the way they do. More precisely, a monoidal category is the class of all things (of a given type) that have a tensor product.

Other products in linear algebra

Other kinds of products in linear algebra include:

• Hadamard product
• Kronecker product
• The product of tensors:
  • Wedge product or exterior product
  • Interior product
  • Outer product
  • Tensor product