Fundamental theorem of linear algebra

In mathematics, the fundamental theorem of linear algebra makes several statements regarding vector spaces. These may be stated concretely in terms of the rank r of an m×n matrix A and its LDU factorization:

P A = L D U

wherein P is a permutation matrix, L is a lower triangular matrix, D is a diagonal matrix, and U is an upper triangular matrix. At a more abstract level there is an interpretation that reads it in terms of a linear mapping and its transpose.

First, each matrix A induces four fundamental subspaces. These fundamental subspaces are:

name of subspace	definition	containing space	dimension	basis
column space or image	$im(A)$	$\mathbf{R}^m$	$r$	The $r$ columns corresponding to those with pivots in $\mathbf{U}$
nullspace or kernel	$ker(A)$	$\mathbf{R}^n$	$n - r$ (nullity)	The $(n - r)$ columns of $x$ in the solution of $\mathbf{U}\mathbf{x} = \mathbf{0}$
row space or coimage	$im(A T)$	$\mathbf{R}^n$	$r$	The $r$ rows corresponding to those with pivots in $\mathbf{U}$
left nullspace or cokernel	$ker(A T)$	$\mathbf{R}^m$	$m - r$	The last $(m - r)$ rows of $\mathbf{L}^{-1}\mathbf{P}$

Secondly:

In Rⁿ: $\mathrm{ker}(A) = (\mathrm{im}(A^T))^\perp$ , that is, the nullspace is the orthogonal complement of the row space
In $\mathbf{R}^m$ : $\mathrm{ker}(A^T) = (\mathrm{im}(A))^\perp$ , that is, the left nullspace is the orthogonal complement of the column space