Em álgebra linear, o teorema do posto-nulidade afirma que para qualquer aplicação linear [latex]T: V to W[/latex] entre espaços vetoriais de dimensão finita, a dimensão do seu domínio [latex]V[/latex] é a soma do seu posto (a dimensão da sua imagem) e da sua nulidade (a dimensão do seu núcleo). A fórmula é [latex]dim(V) = text{posto}(T) + text{nulidade}(T)[/latex].
The rank-nullity theorem provides a fundamental relationship between the dimensions of the key subspaces associated with a linear transformation. Let [latex]T: V to W[/latex] be a linear map. The kernel of T, denoted [latex]ker(T)[/latex], is the set of vectors in [latex]V[/latex] that are mapped to the zero vector in [latex]W[/latex]. The dimension of the kernel is called the nullity of T. The image of T, denoted [latex]text{im}(T)[/latex], is the set of all vectors in [latex]W[/latex] that are the output of T for some input vector from [latex]V[/latex]. The dimension of the image is the rank of T.
The theorem states [latex]dim(text{domain}(T)) = dim(ker(T)) + dim(text{im}(T))[/latex]. A common proof strategy involves constructing a basis. First, find a basis for the kernel, say [latex]{u_1, dots, u_k}[/latex], where [latex]k = text{nullity}(T)[/latex]. Since the kernel is a subspace of [latex]V[/latex], this basis can be extended to a basis for all of [latex]V[/latex]: [latex]{u_1, dots, u_k, v_1, dots, v_r}[/latex]. The dimension of [latex]V[/latex] is thus [latex]k+r[/latex]. The final step is to show that the set [latex]{T(v_1), dots, T(v_r)}[/latex] forms a basis for the image of T. This proves that the rank is [latex]r[/latex], and therefore [latex]dim(V) = k+r = text{nullity}(T) + text{rank}(T)[/latex].
For matrices, if [latex]A[/latex] is an [latex]m times n[/latex] matrix, it represents a linear map from [latex]mathbb{R}^n[/latex] to [latex]mathbb{R}^m[/latex]. The domain’s dimension is [latex]n[/latex]. The rank of [latex]A[/latex] is the dimension of its column space, and its nullity is the dimension of its null space. The theorem becomes [latex]n = text{rank}(A) + text{nullity}(A)[/latex].
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 [latex]m times n[/latex] matrix [latex]A[/latex]: 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 [latex]Ax=0[/latex] (the null space) grows larger, the set of possible outputs [latex]Ax[/latex] (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.