In linear algebra, the rank-nullity theorem states that for any linear map \(T: V \to W\) between finite-dimensional vector spaces, the dimension of its domain \(V\) is the sum of its rank (the dimension of its image) and its nullity (the dimension of its kernel). The formula is \(\dim(V) = \text{rank}(T) + \text{nullity}(T)\).
The rank-nullity theorem provides a fundamental relationship between the dimensions of the key subspaces associated with a linear transformation. Let \(T: V \to W\) be a linear map. The kernel of T, denoted \(\ker(T)\), is the set of vectors in \(V\) that are mapped to the zero vector in \(W\). The dimension of the kernel is called the nullity of T. The image of T, denoted \(\text{im}(T)\), is the set of all vectors in \(W\) that are the output of T for some input vector from \(V\). The dimension of the image is the rank of T.
The theorem states \(\dim(\text{domain}(T)) = \dim(\ker(T)) + \dim(\text{im}(T))\). A common proof strategy involves constructing a basis. First, find a basis for the kernel, say \(\{u_1, \dots, u_k\}\), where \(k = \text{nullity}(T)\). Since the kernel is a subspace of \(V\), this basis can be extended to a basis for all of \(V\): \(\{u_1, \dots, u_k, v_1, \dots, v_r\}\). The dimension of \(V\) is thus \(k+r\). The final step is to show that the set \(\{T(v_1), \dots, T(v_r)\}\) forms a basis for the image of T. This proves that the rank is \(r\), and therefore \(\dim(V) = k+r = \text{nullity}(T) + \text{rank}(T)\).
For matrices, if \(A\) is an \(m \times n\) matrix, it represents a linear map from \(\mathbb{R}^n\) to \(\mathbb{R}^m\). The domain’s dimension is \(n\). The rank of \(A\) is the dimension of its column space, and its nullity is the dimension of its null space. The theorem becomes \(n = \text{rank}(A) + \text{nullity}(A)\).
The theorem is a core component of what is sometimes called the fundamental theorem of linear algebra, which provides a comprehensive description of the structure of the four fundamental subspaces associated with an \(m \times n\) matrix \(A\): the column space, the null space, the row space, and the left null space. It beautifully illustrates the trade-off that as the set of solutions to \(Ax=0\) (the null space) grows larger, the set of possible outputs \(Ax\) (the column space) must become smaller, with their dimensions summing to the total dimension of the input space.
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Related to: rank-nullity theorem, linear algebra, vector space, dimension, kernel, nullity, image, rank, linear transformation, matrix theory.