linguistic:voice_change, information:extraction, stylistic:style_transfer, structural:reformatting, structural:expansion
Linear transformations

Matrices and matrix multiplication reveal their essential features when related to linear transformations, also known as linear maps. A real m -by- n matrix A gives rise to a linear transformation R → R mapping each vector x in R to the (matrix) product Ax, which is a vector in R. Conversely, each linear transformation f: R → R arises from a unique m -by- n matrix A: explicitly, the (i, j)-entry of A is the i coordinate of f (e), where e = (0,...,0,1,0,...,0) is the unit vector with 1 in the j position and 0 elsewhere. The matrix A is said to represent the linear map f, and A is called the transformation matrix of f.

For example, the 2×2 matrix

can be viewed as the transform of the unit square into a parallelogram with vertices at (0, 0), (a, b), (a + c, b + d), and (c, d). The parallelogram pictured at the right is obtained by multiplying A with each of the column vectors,, {\displaystyle {\begin{bmatrix}0\\0\end{bmatrix}},{\begin{bmatrix}1\\0\end{bmatrix}},{\begin{bmatrix}1\\1\end{bmatrix}}}, and {\displaystyle {\begin{bmatrix}0\\1\end{bmatrix}}} in turn. These vectors define the vertices of the unit square.

The following table shows several 2×2 real matrices with the associated linear maps of R. The blue original is mapped to the green grid and shapes. The origin (0,0) is marked with a black point.

Under the 1-to-1 correspondence between matrices and linear maps, matrix multiplication corresponds to composition of maps: if a k -by- m matrix B represents another linear map g: R → R, then the composition g ∘ f is represented by BA since

The last equality follows from the above-mentioned associativity of matrix multiplication.

The rank of a matrix A is the maximum number of linearly independent row vectors of the matrix, which is the same as the maximum number of linearly independent column vectors. Equivalently it is the dimension of the image of the linear map represented by A. The rank–nullity theorem states that the dimension of the kernel of a matrix plus the rank equals the number of columns of the matrix.