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 singular value decomposition:
First, each matrix ( has rows and columns) induces four fundamental subspaces. These fundamental subspaces are:
name of subspace | definition | containing space | dimension | basis |
---|---|---|---|---|
column space, range or image | or | (rank) | The first columns of | |
nullspace or kernel | or | (nullity) | The last columns of | |
row space or coimage | or | The first rows of | ||
left nullspace or cokernel | or | The last rows of |
Secondly:
The dimensions of the subspaces are related by the rank–nullity theorem, and follow from the above theorem.
Further, all these spaces are intrinsically defined – they do not require a choice of basis – in which case one rewrites this in terms of abstract vector spaces, operators, and the dual spaces as and : the kernel and image of are the cokernel and coimage of .
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