The preimage is
It contains , and if belong to it and , then , since is a vector subspace. Thus is a vector subspace. It contains the kernel of because .
To establish the dimension of the preimage of a vector subspace formula without assuming the theorem to be deduced, choose a basis of and a basis of , where . For each , choose with . These lie in .
The family is linearly independent: applying to a vanishing linear combination forces all coefficients to vanish by linear independence of the , and then all remaining coefficients vanish by linear independence of the . It also spans : for in that preimage, write . Then , so it is a linear combination of the . Consequently
Set . Then and , giving the rank-nullity theorem:
Since is the restriction of to ,
This image of a linear map is contained in , so . Also, applying to a basis of gives a spanning set for , of size . Thus , and
For , the vector already belongs to , so
Therefore . Since , its intersection with is itself. Applying a with yields
The inequality follows from the inclusion and monotonicity of dimension for vector subspaces.
By i and ii,
The domain of a function of is . Its rank-nullity theorem gives . Applying the rank-nullity theorem for now gives
which is exactly the rank-nullity theorem for the function composition .

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