In linear algebra, a multilinear map is a function of several variables that is linear separately in each variable. More precisely, a multilinear map is a function

${\displaystyle f\colon V_{1}\times \cdots \times V_{n}\to W{\text{,))}$

where ${\displaystyle V_{1},\ldots ,V_{n))$ (${\displaystyle n\in \mathbb {Z} _{\geq 0))$) and ${\displaystyle W}$ are vector spaces (or modules over a commutative ring), with the following property: for each ${\displaystyle i}$, if all of the variables but ${\displaystyle v_{i))$ are held constant, then ${\displaystyle f(v_{1},\ldots ,v_{i},\ldots ,v_{n})}$ is a linear function of ${\displaystyle v_{i))$.[1] One way to visualize this is to imagine two orthogonal vectors; if one of these vectors is scaled by a factor of 2 while the other remains unchanged, the cross product likewise scales by a factor of two. If both are scaled by a factor of 2, the cross product scales by a factor of ${\displaystyle 2^{2))$.

A multilinear map of one variable is a linear map, and of two variables is a bilinear map. More generally, for any nonnegative integer ${\displaystyle k}$, a multilinear map of k variables is called a k-linear map. If the codomain of a multilinear map is the field of scalars, it is called a multilinear form. Multilinear maps and multilinear forms are fundamental objects of study in multilinear algebra.

If all variables belong to the same space, one can consider symmetric, antisymmetric and alternating k-linear maps. The latter two coincide if the underlying ring (or field) has a characteristic different from two, else the former two coincide.

## Examples

• Any bilinear map is a multilinear map. For example, any inner product on a ${\displaystyle \mathbb {R} }$-vector space is a multilinear map, as is the cross product of vectors in ${\displaystyle \mathbb {R} ^{3))$.
• The determinant of a matrix is an alternating multilinear function of the columns (or rows) of a square matrix.
• If ${\displaystyle F\colon \mathbb {R} ^{m}\to \mathbb {R} ^{n))$ is a Ck function, then the ${\displaystyle k}$th derivative of ${\displaystyle F}$ at each point ${\displaystyle p}$ in its domain can be viewed as a symmetric ${\displaystyle k}$-linear function ${\displaystyle D^{k}\!F\colon \mathbb {R} ^{m}\times \cdots \times \mathbb {R} ^{m}\to \mathbb {R} ^{n))$.[citation needed]

## Coordinate representation

Let

${\displaystyle f\colon V_{1}\times \cdots \times V_{n}\to W{\text{,))}$

be a multilinear map between finite-dimensional vector spaces, where ${\displaystyle V_{i}\!}$ has dimension ${\displaystyle d_{i}\!}$, and ${\displaystyle W\!}$ has dimension ${\displaystyle d\!}$. If we choose a basis ${\displaystyle $$(\textbf {e))_{i1},\ldots ,{\textbf {e))_{id_{i))$$)$ for each ${\displaystyle V_{i}\!}$ and a basis ${\displaystyle $$(\textbf {b))_{1},\ldots ,{\textbf {b))_{d}$$)$ for ${\displaystyle W\!}$ (using bold for vectors), then we can define a collection of scalars ${\displaystyle A_{j_{1}\cdots j_{n))^{k))$ by

${\displaystyle f({\textbf {e))_{1j_{1)),\ldots ,{\textbf {e))_{nj_{n)))=A_{j_{1}\cdots j_{n))^{1}\,{\textbf {b))_{1}+\cdots +A_{j_{1}\cdots j_{n))^{d}\,{\textbf {b))_{d}.}$

Then the scalars ${\displaystyle \{A_{j_{1}\cdots j_{n))^{k}\mid 1\leq j_{i}\leq d_{i},1\leq k\leq d\))$ completely determine the multilinear function ${\displaystyle f\!}$. In particular, if

${\displaystyle {\textbf {v))_{i}=\sum _{j=1}^{d_{i))v_{ij}{\textbf {e))_{ij}\!}$

for ${\displaystyle 1\leq i\leq n\!}$, then

${\displaystyle f({\textbf {v))_{1},\ldots ,{\textbf {v))_{n})=\sum _{j_{1}=1}^{d_{1))\cdots \sum _{j_{n}=1}^{d_{n))\sum _{k=1}^{d}A_{j_{1}\cdots j_{n))^{k}v_{1j_{1))\cdots v_{nj_{n)){\textbf {b))_{k}.}$

## Example

Let's take a trilinear function

${\displaystyle g\colon R^{2}\times R^{2}\times R^{2}\to R,}$

where Vi = R2, di = 2, i = 1,2,3, and W = R, d = 1.

A basis for each Vi is ${\displaystyle $$(\textbf {e))_{i1},\ldots ,{\textbf {e))_{id_{i))\}=\((\textbf {e))_{1},{\textbf {e))_{2}\}=\{(1,0),(0,1)\}.}$ Let ${\displaystyle g({\textbf {e))_{1i},{\textbf {e))_{2j},{\textbf {e))_{3k})=f({\textbf {e))_{i},{\textbf {e))_{j},{\textbf {e))_{k})=A_{ijk},}$ where ${\displaystyle i,j,k\in \{1,2$$)$. In other words, the constant ${\displaystyle A_{ijk))$ is a function value at one of the eight possible triples of basis vectors (since there are two choices for each of the three ${\displaystyle V_{i))$), namely:

${\displaystyle \((\textbf {e))_{1},{\textbf {e))_{1},{\textbf {e))_{1}\},\((\textbf {e))_{1},{\textbf {e))_{1},{\textbf {e))_{2}\},\((\textbf {e))_{1},{\textbf {e))_{2},{\textbf {e))_{1}\},\((\textbf {e))_{1},{\textbf {e))_{2},{\textbf {e))_{2}\},\((\textbf {e))_{2},{\textbf {e))_{1},{\textbf {e))_{1}\},\((\textbf {e))_{2},{\textbf {e))_{1},{\textbf {e))_{2}\},\((\textbf {e))_{2},{\textbf {e))_{2},{\textbf {e))_{1}\},\((\textbf {e))_{2},{\textbf {e))_{2},{\textbf {e))_{2}\}.}$

Each vector ${\displaystyle {\textbf {v))_{i}\in V_{i}=R^{2))$ can be expressed as a linear combination of the basis vectors

${\displaystyle {\textbf {v))_{i}=\sum _{j=1}^{2}v_{ij}{\textbf {e))_{ij}=v_{i1}\times {\textbf {e))_{1}+v_{i2}\times {\textbf {e))_{2}=v_{i1}\times (1,0)+v_{i2}\times (0,1).}$

The function value at an arbitrary collection of three vectors ${\displaystyle {\textbf {v))_{i}\in R^{2))$ can be expressed as

${\displaystyle g({\textbf {v))_{1},{\textbf {v))_{2},{\textbf {v))_{3})=\sum _{i=1}^{2}\sum _{j=1}^{2}\sum _{k=1}^{2}A_{ijk}v_{1i}v_{2j}v_{3k},}$

or in expanded form as

{\displaystyle {\begin{aligned}g((a,b),(c,d)&,(e,f))=ace\times g({\textbf {e))_{1},{\textbf {e))_{1},{\textbf {e))_{1})+acf\times g({\textbf {e))_{1},{\textbf {e))_{1},{\textbf {e))_{2})\\&+ade\times g({\textbf {e))_{1},{\textbf {e))_{2},{\textbf {e))_{1})+adf\times g({\textbf {e))_{1},{\textbf {e))_{2},{\textbf {e))_{2})+bce\times g({\textbf {e))_{2},{\textbf {e))_{1},{\textbf {e))_{1})+bcf\times g({\textbf {e))_{2},{\textbf {e))_{1},{\textbf {e))_{2})\\&+bde\times g({\textbf {e))_{2},{\textbf {e))_{2},{\textbf {e))_{1})+bdf\times g({\textbf {e))_{2},{\textbf {e))_{2},{\textbf {e))_{2}).\end{aligned))}

## Relation to tensor products

There is a natural one-to-one correspondence between multilinear maps

${\displaystyle f\colon V_{1}\times \cdots \times V_{n}\to W{\text{,))}$

and linear maps

${\displaystyle F\colon V_{1}\otimes \cdots \otimes V_{n}\to W{\text{,))}$

where ${\displaystyle V_{1}\otimes \cdots \otimes V_{n}\!}$ denotes the tensor product of ${\displaystyle V_{1},\ldots ,V_{n))$. The relation between the functions ${\displaystyle f}$ and ${\displaystyle F}$ is given by the formula

${\displaystyle f(v_{1},\ldots ,v_{n})=F(v_{1}\otimes \cdots \otimes v_{n}).}$

## Multilinear functions on n×n matrices

One can consider multilinear functions, on an n×n matrix over a commutative ring K with identity, as a function of the rows (or equivalently the columns) of the matrix. Let A be such a matrix and ai, 1 ≤ in, be the rows of A. Then the multilinear function D can be written as

${\displaystyle D(A)=D(a_{1},\ldots ,a_{n}),}$

satisfying

${\displaystyle D(a_{1},\ldots ,ca_{i}+a_{i}',\ldots ,a_{n})=cD(a_{1},\ldots ,a_{i},\ldots ,a_{n})+D(a_{1},\ldots ,a_{i}',\ldots ,a_{n}).}$

If we let ${\displaystyle {\hat {e))_{j))$ represent the jth row of the identity matrix, we can express each row ai as the sum

${\displaystyle a_{i}=\sum _{j=1}^{n}A(i,j){\hat {e))_{j}.}$

Using the multilinearity of D we rewrite D(A) as

${\displaystyle D(A)=D\left(\sum _{j=1}^{n}A(1,j){\hat {e))_{j},a_{2},\ldots ,a_{n}\right)=\sum _{j=1}^{n}A(1,j)D({\hat {e))_{j},a_{2},\ldots ,a_{n}).}$

Continuing this substitution for each ai we get, for 1 ≤ in,

${\displaystyle D(A)=\sum _{1\leq k_{1}\leq n}\ldots \sum _{1\leq k_{i}\leq n}\ldots \sum _{1\leq k_{n}\leq n}A(1,k_{1})A(2,k_{2})\dots A(n,k_{n})D({\hat {e))_{k_{1)),\dots ,{\hat {e))_{k_{n))).}$

Therefore, D(A) is uniquely determined by how D operates on ${\displaystyle {\hat {e))_{k_{1)),\dots ,{\hat {e))_{k_{n))}$.

## Example

In the case of 2×2 matrices, we get

${\displaystyle D(A)=A_{1,1}A_{1,2}D({\hat {e))_{1},{\hat {e))_{1})+A_{1,1}A_{2,2}D({\hat {e))_{1},{\hat {e))_{2})+A_{1,2}A_{2,1}D({\hat {e))_{2},{\hat {e))_{1})+A_{1,2}A_{2,2}D({\hat {e))_{2},{\hat {e))_{2}),\,}$

where ${\displaystyle {\hat {e))_{1}=[1,0]}$ and ${\displaystyle {\hat {e))_{2}=[0,1]}$. If we restrict ${\displaystyle D}$ to be an alternating function, then ${\displaystyle D({\hat {e))_{1},{\hat {e))_{1})=D({\hat {e))_{2},{\hat {e))_{2})=0}$ and ${\displaystyle D({\hat {e))_{2},{\hat {e))_{1})=-D({\hat {e))_{1},{\hat {e))_{2})=-D(I)}$. Letting ${\displaystyle D(I)=1}$, we get the determinant function on 2×2 matrices:

${\displaystyle D(A)=A_{1,1}A_{2,2}-A_{1,2}A_{2,1}.}$

## Properties

• A multilinear map has a value of zero whenever one of its arguments is zero.