For a linear map between finite-dimensional vector spaces, the image of its dual map is the annihilator of a vector subspace given by its kernel. Inclusion follows by composition. Conversely a linear functional vanishing on the kernel descends to the image and extends to the target by basis extension. The identity and the rank-nullity theorem show that a linear map and its dual have equal rank.
For a linear map with finite-dimensional, the rank-nullity theorem states
To prove it, choose a basis of the kernel of a linear map and use basis extension to obtain as a basis of . The vectors span the image of a linear map: applying to a basis expansion kills the first terms. They are also linearly independent. Indeed, if , then , so it is a combination of . Independence of the full basis forces every . Consequently , proving the theorem.
Here the image of a linear map has dimension , since it lies in . Thus
Every possibility occurs: for , map to . This map has rank and kernel dimension .
The algebraic dual map is , defined by . The annihilator of a vector subspace is
Every functional vanishes on , so . Conversely, let . Define a functional on by . It is well defined because two preimages differ by an element of . Extend a basis of to one of and extend the functional arbitrarily, for instance by zero on the extra basis vectors. The resulting satisfies .
This is the dual image and kernel annihilator identity. Therefore
The basis extension theorem also gives . By the rank-nullity theorem,
Apply rank-nullity theorem to both maps, using , to obtain
Over , this is the algebraic dual, without complex conjugation; the adjoint operator in part (b) uses the inner products instead.