Outer product

Outer product
In linear algebra, the outer product of two coordinate vectors is the matrix whose entries are all products of an element in the first vector with an element in the second vector. If the two coordinate vectors have dimensions n and m, then their outer product is an n × m matrix. More generally, given two tensors (multidimensional arrays of numbers), their outer product is a tensor. The outer product of tensors is also referred to as their tensor product, and can be used to define the tensor algebra.
The outer product contrasts with:
The dot product (a special case of "inner product"), which takes a pair of coordinate vectors as input and produces a scalar
The Kronecker product, which takes a pair of matrices as input and produces a block matrix
Standard matrix multiplication
Definition
Given two vectors of size and respectively
their outer product, denoted is defined as the matrix obtained by multiplying each element of by each element of :
Or, in index notation:
Denoting the dot product by if given an vector then If given a vector then
If and are vectors of the same dimension bigger than 1, then .
The outer product is equivalent to a matrix multiplication provided that is represented as a column vector and as a column vector (which makes a row vector). For instance, if and then
For complex vectors, it is often useful to take the conjugate transpose of denoted or :
Contrast with Euclidean inner product
If then one can take the matrix product the other way, yielding a scalar (or matrix):
which is the standard inner product for Euclidean vector spaces, better known as the dot product. The dot product is the trace of the outer product. Unlike the dot product, the outer product is not commutative.
Multiplication of a vector by the matrix can be written in terms of the inner product, using the relation .
The outer product of tensors
Given two tensors with dimensions and , their outer product is a tensor with dimensions and entries
For example, if is of order 3 with dimensions and is of order 2 with dimensions then their outer product is of order 5 with dimensions If has a component and has a component , then the component of formed by the outer product is .
Connection with the Kronecker product
The outer product and Kronecker product are closely related; in fact the same symbol is commonly used to denote both operations.
If and , we have:
In the case of column vectors, the Kronecker product can be viewed as a form of vectorization (or flattening) of the outer product. In particular, for two column vectors and , we can write:
(The order of the vectors is reversed on the right side of the equation.)
Another similar identity that further highlights the similarity between the operations is
where the order of vectors needs not be flipped. The middle expression uses matrix multiplication, where the vectors are considered as column/row matrices.
Connection with the matrix product
Given a pair of matrices of size and of size , consider the matrix product defined as usual as a matrix of size .
Now let be the -th column vector of and let be the -th row vector of . Then can be expressed as a sum of column-by-row outer products:
This expression has duality with the more common one as a matrix built with row-by-column inner product entries (or dot product):
This relation is relevant in the application of the Singular Value Decomposition (SVD) (and Spectral Decomposition as a special case). In particular, the decomposition can be interpreted as the sum of outer products of each left and right singular vectors, scaled by the corresponding nonzero singular value :
This result implies that can be expressed as a sum of rank-1 matrices with spectral norm in decreasing order. This explains the fact why, in general, the last terms contribute less, which motivates the use of the truncated SVD as an approximation. The first term is the least squares fit of a matrix to an outer product of vectors.
Properties
The outer product of vectors satisfies the following properties:
The outer product of tensors satisfies the additional associativity property:
Rank of an outer product
If u and v are both nonzero, then the outer product matrix uvT always has matrix rank 1. Indeed, the columns of the outer product are all proportional to the first column. Thus they are all linearly dependent on that one column, hence the matrix is of rank one.
("Matrix rank" should not be confused with "tensor order", or "tensor degree", which is sometimes referred to as "rank".)
Definition (abstract)
Let and be two vector spaces. The outer product of and is the element .
If is an inner product space, then it is possible to define the outer product as a linear map . In this case, the linear map is an element of the dual space of . The outer product is then given by
This shows why a conjugate transpose of is commonly taken in the complex case.
In programming languages
In some programming languages, given a two-argument function f (or a binary operator), the outer product, f, of two one-dimensional arrays, A and B, is a two-dimensional array C such that C[i, j] = f(A[i], B[j]). This is syntactically represented in various ways: in APL, as the infix binary operator ∘.f; in J, as the postfix adverb f/; in R, as the function outer(A, B, f) or the special %o%; in Mathematica, as Outer[f, A, B]. In MATLAB, the function kron(A, B) is used for this product. These often generalize to multi-dimensional arguments, and more than two arguments.
In the Python library NumPy, the outer product can be computed with function np.outer(). In contrast, np.kron results in a flat array. The outer product of multidimensional arrays can be computed using np.multiply.outer.
Applications
As the outer product is closely related to the Kronecker product, some of the applications of the Kronecker product use outer products. These applications are found in quantum theory, signal processing, and image compression.
Spinors
Suppose so that and are in . Then the outer product of these complex 2-vectors is an element of , the 2 × 2 complex matrices:
The determinant of this matrix is because of the commutative property of .
In the theory of spinors in three dimensions, these matrices are associated with isotropic vectors due to this null property. Élie Cartan described this construction in 1937, but it was introduced by Wolfgang Pauli in 1927 so that has come to be called Pauli algebra.
Concepts
The block form of outer products is useful in classification. Concept analysis is a study that depends on certain outer products:
When a vector has only zeros and ones as entries, it is called a logical vector, a special case of a logical matrix. The logical operation and takes the place of multiplication. The outer product of two logical vectors and is given by the logical matrix . This type of matrix is used in the study of binary relations, and is called a rectangular relation or a cross-vector.
See also
Dyadics
Householder transformation
Norm (mathematics)
Ricci calculus
Scatter matrix
Products
Cartesian product
Cross product
Exterior product
Hadamard product (matrices)
Duality
Bra–ket notation for outer product
Complex conjugate
Conjugate transpose
Transpose
References
Further reading
Bilinear maps
Higher-order functions
Operations on vectors
Articles with example Python (programming language) code