Rank-Nullity Theorem

For a linear map, dimension(domain) = dimension(kernel) + dimension(image)
Rank-Nullity Theorem

Rank-Nullity Theorem: Let T:VWT:V\to W be a between finite-dimensional over the same field. Define

  • ker(T)={vV:T(v)=0}\ker(T)=\{v\in V:T(v)=0\} (kernel / null space),
  • im(T)={T(v):vV}\operatorname{im}(T)=\{T(v):v\in V\} (image),
  • nullity(T)=dim(ker(T))\operatorname{nullity}(T)=\dim(\ker(T)),
  • rank(T)=dim(im(T))\operatorname{rank}(T)=\dim(\operatorname{im}(T)), where dim(U)\dim(U) denotes the number of vectors in any basis of UU.

Then

dim(V)=nullity(T)+rank(T). \dim(V)=\operatorname{nullity}(T)+\operatorname{rank}(T).

This theorem is the basic dimension-counting tool for linear maps. For example, TT is if and only if ker(T)={0}\ker(T)=\{0\}, and it is if and only if rank(T)=dim(W)\operatorname{rank}(T)=\dim(W) (in finite dimensions). Standard proofs ultimately rely on the existence of bases, guaranteed in general by .