
In
linear algebra
Linear algebra is the branch of mathematics concerning linear equations such as
:a_1x_1+\cdots +a_nx_n=b,
linear maps such as
:(x_1, \ldots, x_n) \mapsto a_1x_1+\cdots +a_nx_n,
and their representations in vector spaces and through matrix (mathemat ...
, the transpose of a
matrix is an operator which flips a matrix over its diagonal;
that is, it switches the row and column indices of the matrix by producing another matrix, often denoted by (among other notations).
The transpose of a matrix was introduced in 1858 by the British mathematician
Arthur Cayley.
Transpose of a matrix
Definition
The transpose of a matrix , denoted by ,
, ,
, , , or , may be constructed by any one of the following methods:
#
Reflect over its
main diagonal (which runs from top-left to bottom-right) to obtain
#Write the rows of as the columns of
#Write the columns of as the rows of
Formally, the -th row, -th column element of is the -th row, -th column element of :
:
If is an matrix, then is an matrix.
In the case of square matrices, may also denote the th power of the matrix . For avoiding a possible confusion, many authors use left upperscripts, that is, they denote the transpose as . An advantage of this notation is that no parentheses are needed when exponents are involved: as , notation is not ambiguous.
In this article, this confusion is avoided by never using the symbol as a
variable name.
Matrix definitions involving transposition
A square matrix whose transpose is equal to itself is called a ''
symmetric matrix
In linear algebra, a symmetric matrix is a square matrix that is equal to its transpose. Formally,
Because equal matrices have equal dimensions, only square matrices can be symmetric.
The entries of a symmetric matrix are symmetric with ...
''; that is, is symmetric if
:
A square matrix whose transpose is equal to its negative is called a ''
skew-symmetric matrix''; that is, is skew-symmetric if
:
A square
complex
Complex commonly refers to:
* Complexity, the behaviour of a system whose components interact in multiple ways so possible interactions are difficult to describe
** Complex system, a system composed of many components which may interact with each ...
matrix whose transpose is equal to the matrix with every entry replaced by its
complex conjugate
In mathematics, the complex conjugate of a complex number is the number with an equal real part and an imaginary part equal in magnitude but opposite in sign. That is, if a and b are real numbers, then the complex conjugate of a + bi is a - ...
(denoted here with an overline) is called a ''
Hermitian matrix
In mathematics, a Hermitian matrix (or self-adjoint matrix) is a complex square matrix that is equal to its own conjugate transpose—that is, the element in the -th row and -th column is equal to the complex conjugate of the element in the ...
'' (equivalent to the matrix being equal to its
conjugate transpose); that is, is Hermitian if
:
A square
complex
Complex commonly refers to:
* Complexity, the behaviour of a system whose components interact in multiple ways so possible interactions are difficult to describe
** Complex system, a system composed of many components which may interact with each ...
matrix whose transpose is equal to the negation of its complex conjugate is called a ''
skew-Hermitian matrix''; that is, is skew-Hermitian if
:
A square matrix whose transpose is equal to its
inverse is called an ''
orthogonal matrix''; that is, is orthogonal if
:
A square complex matrix whose transpose is equal to its conjugate inverse is called a ''
unitary matrix''; that is, is unitary if
:
Examples
*
*
*
Properties
Let and be matrices and be a
scalar.
*
*:The operation of taking the transpose is an
involution (self-
inverse).
*
*:The transpose respects
addition
Addition (usually signified by the Plus and minus signs#Plus sign, plus symbol, +) is one of the four basic Operation (mathematics), operations of arithmetic, the other three being subtraction, multiplication, and Division (mathematics), divis ...
.
*
*:The transpose of a scalar is the same scalar. Together with the preceding property, this implies that the transpose is a
linear map
In mathematics, and more specifically in linear algebra, a linear map (also called a linear mapping, linear transformation, vector space homomorphism, or in some contexts linear function) is a mapping V \to W between two vector spaces that p ...
from the
space
Space is a three-dimensional continuum containing positions and directions. In classical physics, physical space is often conceived in three linear dimensions. Modern physicists usually consider it, with time, to be part of a boundless ...
of matrices to the space of the matrices.
*
*:The order of the factors reverses. By induction, this result extends to the general case of multiple matrices, so
*::.
*
*:The
determinant
In mathematics, the determinant is a Scalar (mathematics), scalar-valued function (mathematics), function of the entries of a square matrix. The determinant of a matrix is commonly denoted , , or . Its value characterizes some properties of the ...
of a square matrix is the same as the determinant of its transpose.
*The
dot product
In mathematics, the dot product or scalar productThe term ''scalar product'' means literally "product with a Scalar (mathematics), scalar as a result". It is also used for other symmetric bilinear forms, for example in a pseudo-Euclidean space. N ...
of two column vectors and can be computed as the single entry of the matrix product
*If has only real entries, then is a
positive-semidefinite matrix.
*
*: The transpose of an invertible matrix is also invertible, and its inverse is the transpose of the inverse of the original matrix.
The notation is sometimes used to represent either of these equivalent expressions.
*If is a square matrix, then its
eigenvalues are equal to the eigenvalues of its transpose, since they share the same
characteristic polynomial.
*
for two column vectors
and the standard
dot product
In mathematics, the dot product or scalar productThe term ''scalar product'' means literally "product with a Scalar (mathematics), scalar as a result". It is also used for other symmetric bilinear forms, for example in a pseudo-Euclidean space. N ...
.
*Over any field
, a square matrix
is
similar to
.
*:This implies that
and
have the same
invariant factors, which implies they share the same minimal polynomial, characteristic polynomial, and eigenvalues, among other properties.
*:A proof of this property uses the following two observations.
*:* Let
and
be
matrices over some base field
and let
be a
field extension
In mathematics, particularly in algebra, a field extension is a pair of fields K \subseteq L, such that the operations of ''K'' are those of ''L'' restricted to ''K''. In this case, ''L'' is an extension field of ''K'' and ''K'' is a subfield of ...
of
. If
and
are similar as matrices over
, then they are similar over
. In particular this applies when
is the
algebraic closure of
.
*:*If
is a matrix over an algebraically closed field in
Jordan normal form with respect to some basis, then
is similar to
. This further reduces to proving the same fact when
is a single Jordan block, which is a straightforward exercise.
Products
If is an matrix and is its transpose, then the result of
matrix multiplication with these two matrices gives two square matrices: is and is . Furthermore, these products are
symmetric matrices. Indeed, the matrix product has entries that are the
inner product
In mathematics, an inner product space (or, rarely, a Hausdorff pre-Hilbert space) is a real vector space or a complex vector space with an operation called an inner product. The inner product of two vectors in the space is a scalar, ofte ...
of a row of with a column of . But the columns of are the rows of , so the entry corresponds to the inner product of two rows of . If is the entry of the product, it is obtained from rows and in . The entry is also obtained from these rows, thus , and the product matrix () is symmetric. Similarly, the product is a symmetric matrix.
A quick proof of the symmetry of results from the fact that it is its own transpose:
:
Implementation of matrix transposition on computers

On a
computer
A computer is a machine that can be Computer programming, programmed to automatically Execution (computing), carry out sequences of arithmetic or logical operations (''computation''). Modern digital electronic computers can perform generic set ...
, one can often avoid explicitly transposing a matrix in
memory
Memory is the faculty of the mind by which data or information is encoded, stored, and retrieved when needed. It is the retention of information over time for the purpose of influencing future action. If past events could not be remembe ...
by simply accessing the same data in a different order. For example,
software libraries for
linear algebra
Linear algebra is the branch of mathematics concerning linear equations such as
:a_1x_1+\cdots +a_nx_n=b,
linear maps such as
:(x_1, \ldots, x_n) \mapsto a_1x_1+\cdots +a_nx_n,
and their representations in vector spaces and through matrix (mathemat ...
, such as
BLAS, typically provide options to specify that certain matrices are to be interpreted in transposed order to avoid the necessity of data movement.
However, there remain a number of circumstances in which it is necessary or desirable to physically reorder a matrix in memory to its transposed ordering. For example, with a matrix stored in
row-major order, the rows of the matrix are contiguous in memory and the columns are discontiguous. If repeated operations need to be performed on the columns, for example in a
fast Fourier transform
A fast Fourier transform (FFT) is an algorithm that computes the discrete Fourier transform (DFT) of a sequence, or its inverse (IDFT). A Fourier transform converts a signal from its original domain (often time or space) to a representation in ...
algorithm, transposing the matrix in memory (to make the columns contiguous) may improve performance by increasing
memory locality.
Ideally, one might hope to transpose a matrix with minimal additional storage. This leads to the problem of transposing an ''n'' × ''m'' matrix
in-place, with
O(1) additional storage or at most storage much less than ''mn''. For ''n'' ≠ ''m'', this involves a complicated
permutation
In mathematics, a permutation of a set can mean one of two different things:
* an arrangement of its members in a sequence or linear order, or
* the act or process of changing the linear order of an ordered set.
An example of the first mean ...
of the data elements that is non-trivial to implement in-place. Therefore, efficient
in-place matrix transposition has been the subject of numerous research publications in
computer science
Computer science is the study of computation, information, and automation. Computer science spans Theoretical computer science, theoretical disciplines (such as algorithms, theory of computation, and information theory) to Applied science, ...
, starting in the late 1950s, and several algorithms have been developed.
Transposes of linear maps and bilinear forms
As the main use of matrices is to represent linear maps between
finite-dimensional vector spaces, the transpose is an operation on matrices that may be seen as the representation of some operation on linear maps.
This leads to a much more general definition of the transpose that works on every linear map, even when linear maps cannot be represented by matrices (such as in the case of infinite dimensional vector spaces). In the finite dimensional case, the matrix representing the transpose of a linear map is the transpose of the matrix representing the linear map, independently of the
basis choice.
Transpose of a linear map
Let denote the
algebraic dual space of an -
module .
Let and be -modules.
If is a
linear map
In mathematics, and more specifically in linear algebra, a linear map (also called a linear mapping, linear transformation, vector space homomorphism, or in some contexts linear function) is a mapping V \to W between two vector spaces that p ...
, then its algebraic adjoint or dual, is the map defined by .
The resulting functional is called the
pullback of by .
The following
relation characterizes the algebraic adjoint of
: for all and
where is the
natural pairing (i.e. defined by ).
This definition also applies unchanged to left modules and to vector spaces.
The definition of the transpose may be seen to be independent of any bilinear form on the modules, unlike the adjoint (
below).
The
continuous dual space of a
topological vector space
In mathematics, a topological vector space (also called a linear topological space and commonly abbreviated TVS or t.v.s.) is one of the basic structures investigated in functional analysis.
A topological vector space is a vector space that is als ...
(TVS) is denoted by .
If and are TVSs then a linear map is weakly continuous if and only if , in which case we let denote the restriction of to .
The map is called the transpose of .
If the matrix describes a linear map with respect to
bases of and , then the matrix describes the transpose of that linear map with respect to the
dual bases.
Transpose of a bilinear form
Every linear map to the dual space defines a bilinear form , with the relation .
By defining the transpose of this bilinear form as the bilinear form defined by the transpose i.e. , we find that .
Here, is the natural
homomorphism
In algebra, a homomorphism is a morphism, structure-preserving map (mathematics), map between two algebraic structures of the same type (such as two group (mathematics), groups, two ring (mathematics), rings, or two vector spaces). The word ''homo ...
into the
double dual.
Adjoint
If the vector spaces and have respectively
nondegenerate bilinear form
In mathematics, a bilinear form is a bilinear map on a vector space (the elements of which are called '' vectors'') over a field ''K'' (the elements of which are called '' scalars''). In other words, a bilinear form is a function that is linea ...
s and , a concept known as the adjoint, which is closely related to the transpose, may be defined:
If is a
linear map
In mathematics, and more specifically in linear algebra, a linear map (also called a linear mapping, linear transformation, vector space homomorphism, or in some contexts linear function) is a mapping V \to W between two vector spaces that p ...
between
vector space
In mathematics and physics, a vector space (also called a linear space) is a set (mathematics), set whose elements, often called vector (mathematics and physics), ''vectors'', can be added together and multiplied ("scaled") by numbers called sc ...
s and , we define as the adjoint of if satisfies
:
for all and .
These bilinear forms define an
isomorphism
In mathematics, an isomorphism is a structure-preserving mapping or morphism between two structures of the same type that can be reversed by an inverse mapping. Two mathematical structures are isomorphic if an isomorphism exists between the ...
between and , and between and , resulting in an isomorphism between the transpose and adjoint of .
The matrix of the adjoint of a map is the transposed matrix only if the
bases are
orthonormal with respect to their bilinear forms.
In this context, many authors however, use the term transpose to refer to the adjoint as defined here.
The adjoint allows us to consider whether is equal to .
In particular, this allows the
orthogonal group
In mathematics, the orthogonal group in dimension , denoted , is the Group (mathematics), group of isometry, distance-preserving transformations of a Euclidean space of dimension that preserve a fixed point, where the group operation is given by ...
over a vector space with a quadratic form to be defined without reference to matrices (nor the components thereof) as the set of all linear maps for which the adjoint equals the inverse.
Over a complex vector space, one often works with
sesquilinear forms (conjugate-linear in one argument) instead of bilinear forms.
The
Hermitian adjoint
In mathematics, specifically in operator theory, each linear operator A on an inner product space defines a Hermitian adjoint (or adjoint) operator A^* on that space according to the rule
:\langle Ax,y \rangle = \langle x,A^*y \rangle,
where \l ...
of a map between such spaces is defined similarly, and the matrix of the Hermitian adjoint is given by the conjugate transpose matrix if the bases are orthonormal.
See also
*
Adjugate matrix, the transpose of the
cofactor matrix
*
Conjugate transpose
*
Converse relation
*
Moore–Penrose pseudoinverse
*
Projection (linear algebra)
References
Further reading
*
* .
*
*
*
*
External links
* Gilbert Strang (Spring 2010
Linear Algebrafrom MIT Open Courseware
{{Tensors
Matrices (mathematics)
Abstract algebra
Linear algebra