In mathematics, and in particular linear algebra, the transpose of a matrix is another matrix, produced by turning rows into columns and vice versa. Informally, the transpose of a square matrix is obtained by reflecting at the main diagonal (that runs from the top left to bottom right of the matrix). The transpose of the matrix A is written as Atr, tA, A', or AT, the latter notation being preferred in Wikipedia.
Formally, the transpose of the m-by-n matrix A is the n-by-m matrix AT defined by AT[i, j] = A[j, i] for 1 ≤ i ≤ n and 1 ≤ j ≤ m.
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2 Further nomenclature
3 Transpose of linear maps
For any two m-by-n matrices A and B and every scalar c, we have (A + B)T = AT + BT and (cA)T = c(AT). This shows that the transpose is a linear map from the space of all m-by-n matrices to the space of all n-by-m matrices.
The transpose operation is self-inverse, i.e taking the transpose of the transpose amounts to doing nothing: (AT)T = A.
If A is an m-by-n and B an n-by-k matrix, then we have (AB)T = (BT)(AT). Note that the order of the factors switches. 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 (A-1)T = (AT)-1.
If A is an arbitrary m-by-n matrix with real entries, then ATA is a positive semidefinite matrix.