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transpose
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{{aboutthe transpose of a matrixTransposition (disambiguation)}}{{hatnoteNote that this article assumes that matrices are taken over a commutative ring. These results may not hold in the noncommutative case.}}(File:Matrix transpose.gifthumb200pxrightThe transpose AT of a matrix A can be obtained by reflecting the elements along its main diagonal. Repeating the process on the transposed matrix returns the elements to their original position.)In linear algebra, 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 denoted as AT (also written Aâ€², Atr, tA or At). It is achieved by any one of the following equivalent actions:  the content below is remote from Wikipedia
 it has been imported raw for GetWiki
 reflect A over its main diagonal (which runs from topleft to bottomright) to obtain AT,
 write the rows of A as the columns of AT,
 write the columns of A as the rows of AT.
left[mathbf{A}^operatorname{T}right]_{ij} = left[mathbf{A}right]_{ji}.
If A is an {{nowrapm Ã— n}} matrix, then AT is an {{nowrapn Ã— m}} matrix. To avoid confusing the reader between the transpose operation and a matrix raised to the tth power, the mathbf{A}^top symbol denotes the transpose operation.The transpose of a matrix was introduced in 1858 by the British mathematician Arthur Cayley.Arthur Cayley (1858) "A memoir on the theory of matrices", Philosophical Transactions of the Royal Society of London, 148 : 17â€“37. The transpose (or "transposition") is defined on page 31. Examples
 begin{bmatrix}
1 & 2
end{bmatrix}^{operatorname{T}}
= ,
begin{bmatrix}
1
2
end{bmatrix}
end{bmatrix}^{operatorname{T}}
= ,
begin{bmatrix}
1
2
end{bmatrix}
begin{bmatrix}
1 & 2
3 & 4
end{bmatrix}^{operatorname{T}}
=
begin{bmatrix}
1 & 3
2 & 4
end{bmatrix}
1 & 2
3 & 4
end{bmatrix}^{operatorname{T}}
=
begin{bmatrix}
1 & 3
2 & 4
end{bmatrix}
begin{bmatrix}
1 & 2
3 & 4
5 & 6
end{bmatrix}^{operatorname{T}}
=
begin{bmatrix}
1 & 3 & 5
2 & 4 & 6
end{bmatrix}
1 & 2
3 & 4
5 & 6
end{bmatrix}^{operatorname{T}}
=
begin{bmatrix}
1 & 3 & 5
2 & 4 & 6
end{bmatrix}
Properties
For matrices A, B and scalar c we have the following properties of transpose:{{ordered list1= left(mathbf{A}^operatorname{T} right)^operatorname{T} = mathbf{A}.
The operation of taking the transpose is an involution (selfinverse).2= left(mathbf{A} + mathbf{B}right)^operatorname{T} = mathbf{A}^operatorname{T} + mathbf{B}^operatorname{T}.
The transpose respects addition.3= left(mathbf{A B}right)^operatorname{T} = mathbf{B}^operatorname{T} mathbf{A}^operatorname{T}.
Note that the order of the factors reverses. From this one can deduce that a square matrix A is invertible if and only if AT is invertible, and in this case we have (Aâˆ’1)T = (AT)âˆ’1. By induction this result extends to the general case of multiple matrices, where we find that (A1A2...Akâˆ’1Ak)T = AkTAkâˆ’1Tâ€¦A2TA1T.4= left(c mathbf{A}right)^operatorname{T} = c mathbf{A}^operatorname{T}.
The transpose of a scalar is the same scalar. Together with (2), this states that the transpose is a linear map from the space of {{nowrapm Ã— n}} matrices to the space of all {{nowrapn Ã— m}} matrices.5= detleft(mathbf{A}^operatorname{T}right) = det(mathbf{A}).
The determinant of a square matrix is the same as the determinant of its transpose.
dot product of two column vector space>vectors a and b can be computed as the single entry of the matrix product:
left[ mathbf{a} cdot mathbf{b} right] = mathbf{a}^{operatorname{T}} mathbf{b},
which is written as aiâ€‰bi in Einstein summation convention.7= If A has only real entries, then ATA is a positivesemidefinite matrix.8= left(mathbf{A}^operatorname{T}right)^{1} = left(mathbf{A}^{1}right)^operatorname{T}.
The transpose of an invertible matrix is also invertible, and its inverse is the transpose of the inverse of the original matrix. The notation Aâˆ’T is sometimes used to represent either of these equivalent expressions.
 A is a square matrix, then its Eigenvalue, eigenvector and eigenspace>eigenvalues are equal to the eigenvalues of its transpose, since they share the same characteristic polynomial.}}Special transpose matricesA square matrix whose transpose is equal to itself is called a symmetric matrix; that is, A is symmetric if
mathbf{A}^{operatorname{T}} = mathbf{A}.
A square matrix whose transpose is equal to its negative is called a skewsymmetric matrix; that is, A is skewsymmetric if
mathbf{A}^{operatorname{T}} = mathbf{A}.
A square complex matrix whose transpose is equal to the matrix with every entry replaced by its complex conjugate (denoted here with an overline) is called a Hermitian matrix (equivalent to the matrix being equal to its conjugate transpose); that is, A is Hermitian if
mathbf{A}^{operatorname{T}} = overline{mathbf{A}}.
A square complex matrix whose transpose is equal to the negation of its complex conjugate is called a skewHermitian matrix; that is, A is skewHermitian if
mathbf{A}^{operatorname{T}} = overline{mathbf{A}}.
A square matrix whose transpose is equal to its inverse is called an orthogonal matrix; that is, A is orthogonal if
mathbf{A}^{operatorname{T}} = mathbf{A}^{1}.
A square complex matrix whose transpose is equal to its conjugate inverse is called a unitary matrix; that is, A is unitary if
mathbf{A}^{operatorname{T}} = overline{mathbf{A}^{1}}.
ProductsIf A is an m Ã— n matrix and AT is its transpose, then the result of matrix multiplication with these two matrices gives two square matrices: A AT is m Ã— m and AT A is n Ã— n. Furthermore, these products are symmetric matrices. Indeed, the matrix product A AT has entries that are the inner product of a row of A with a column of AT. But the columns of AT are the rows of A, so the entry corresponds to the inner product of two rows of A. If pi j is the entry of the product, it is obtained from rows i and j in A. The entry pj i is also obtained from these rows, thus pi j = pj i, and the product matrix (pi j) is symmetric. Similarly, the product AT A is a symmetric matrix.A quick proof of the symmetry of A AT results from the fact that it is its own transpose:
left(mathbf{A} mathbf{A}^operatorname{T}right)^operatorname{T} = left(mathbf{A}^operatorname{T}right)^operatorname{T} mathbf{A}^operatorname{T}= mathbf{A} mathbf{A}^operatorname{T} .Gilbert Strang (2006) Linear Algebra and its Applications 4th edition, page 51, Thomson Brooks/Cole {{ISBN0030105676}}
Transpose of a linear map{{see alsoTranspose of a linear map}}The transpose may be defined more generally:If {{nowrap1=f : V â†’ W}} is a linear map between right Rmodules V and W with respective dual modules Vâˆ— and Wâˆ—, the transpose of f is the linear map
{}^operatorname{T} f : W^* to V^* : varphi mapsto varphi circ f .
Equivalently, the transpose tf is defined by the relation
leftlangle varphi , f ( v ) rightrangle = leftlangle {}^operatorname{T} f ( varphi ) , v rightrangle quad forall varphi in W^* , v in V ,
where {{nowrapÂ·,Â·{{rangle}}}} is the natural pairing of each dual space with its respective vector space. This definition also applies unchanged to left modules and to vector spaces.{{citation author=Bourbaki title=Algebra I section=II Â§2.5 }}The definition of the transpose may be seen to be independent of any bilinear form on the vector spaces, unlike the adjoint (below).If the matrix A describes a linear map with respect to bases of V and W, then the matrix AT describes the transpose of that linear map with respect to the dual bases.Transpose of a bilinear formEvery linear map to the dual space {{math1=f : V â†’ Vâˆ—}} defines a bilinear form {{math1=B : V Ã— V â†’ F}}, with the relation {{math1=B(v, w) = f(v)(w)}}. By defining the transpose of this bilinear form as the bilinear form tB defined by the transpose {{math1=tf : Vâˆ—âˆ— â†’ Vâˆ—}} i.e. {{math1=tB(w, v) = tf(Î¨(w))(v)}}, we find that {{math1=B(v, w) = tB(w, v)}}. Here, Î¨ is the natural homomorphism {{mathV â†’ Vâˆ—âˆ—}} into the double dual.Adjoint{{distinguishHermitian adjoint}}If the vector spaces V and W have respectively nondegenerate bilinear forms B'V and B'W, a concept known as the adjoint, which is closely related to the transpose, may be defined:If {{nowrap1=f : V â†’ W}} is a linear map between vector spaces V and W, we define g as the adjoint of f if {{nowrap1=g : W â†’ V}} satisfies
B_Vbig(v, g(w)big) = B_Wbig(f(v), wbig) quad forall v in V, w in W.
These bilinear forms define an isomorphism between V and Vâˆ—, and between W and Wâˆ—, resulting in an isomorphism between the transpose and adjoint of f. 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 use the term transpose to refer to the adjoint as defined here.The adjoint allows us to consider whether {{nowrap1=g : W â†’ V}} is equal to {{nowrap1=fâ€‰âˆ’1 : W â†’ V}}. In particular, this allows the orthogonal group over a vector space V with a quadratic form to be defined without reference to matrices (nor the components thereof) as the set of all linear maps {{nowrapV â†’ V}} for which the adjoint equals the inverse.Over a complex vector space, one often works with sesquilinear forms (conjugatelinear in one argument) instead of bilinear forms. The Hermitian adjoint 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.Implementation of matrix transposition on computers{{See alsoInplace matrix transposition}}(File:Row_and_column_major_order.svgthumbuprightIllustration of row and columnmajor order)On a computer, one can often avoid explicitly transposing a matrix in memory by simply accessing the same data in a different order. For example, software libraries for linear algebra, 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 rowmajor 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 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 Ã— m matrix inplace, with O(1) additional storage or at most storage much less than mn. For n â‰ m, this involves a complicated permutation of the data elements that is nontrivial to implement inplace. Therefore, efficient inplace matrix transposition has been the subject of numerous research publications in computer science, starting in the late 1950s, and several algorithms have been developed.See alsoReferences{{reflist}}Further reading
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