Section 4.5 Rank-Nullity Theorem
Definition 4.5.1.
Let \(V\) and \(W\) be vector spaces over a field \(F\text{.}\) Let \(T\colon V\to W\) be an \(F\)-linear map. The dimension of the kernel of \(T\) is called the nullity of \(T\text{.}\) So,Definition 4.5.2.
Let \(V\) and \(W\) be vector spaces over a field \(F\text{.}\) Let \(T\colon V\to W\) be an \(F\)-linear map. The dimension of the image of \(T\) is called the rank of \(T\text{.}\) So,Theorem 4.5.3. (Rank-Nullity Theorem).
Let \(V\) and \(W\) be vector spaces over a field \(F\text{.}\) Let \(T\colon V\to W\) be an \(F\)-linear map. If \(V\) is a finite-dimensional vector space over \(F\) then,Proof.
Let \(\{u_1,\ldots,u_r\}\) be a basis for \(\ker(T)\text{.}\) We extend this basis to a basis of \(V\text{,}\) say \(\{u_1,\ldots,u_r,v_1,\ldots,v_s\}\text{.}\) We claim that \(\{T(v_1),\ldots,T(v_s)\}\) is a basis for \(\Im(T)\text{.}\) We first check the independence. Suppose that
Thus, \(\sum\alpha_iv_i\in\ker(T)\) and hence there exists \(\beta_j\in F\) such that
Since \(\{u_1,\ldots,u_r,v_1,\ldots,v_n\}\) is a basis we have \(\alpha_i=0\) and \(\beta_j=0\) for each \(i\) and \(j\text{.}\) Therefore, \(\{T(v_j)\}\) is linearly independent.
Given any \(w\in\Im(T)\) there exists \(v\in V\) such that \(w=T(v)\text{.}\) Let
Using \(\{u_i\}\) is a basis of \(\ker(T)\) we have the following.
Hence \(\{T(v_j)\}\) spans \(\Im(T)\text{.}\) Since \(\{T(v_j)\}\) is linearly independent and spans the \(\Im(T)\) it is a basis of \(\Im(T)\text{.}\) We thus have
Therefore,