The rank–nullity theorem is a theorem in linear algebra, which asserts:
the number of columns of a matrix M is the sum of the rank of M and the nullity of M; and
the dimension of the domain of a linear transformation f is the sum of the rank of f (the dimension of the of f) and the nullity of f (the dimension of the kernel of f).
It follows that for linear transformations of vector spaces of finite dimension, either injectivity or surjectivity implies bijectivity.
Let be a linear transformation between two vector spaces where 's domain is finite dimensional. Then
where is the rank of (the dimension of its ) and is the nullity of (the dimension of its kernel). In other words,
This theorem can be refined via the splitting lemma to be a statement about an isomorphism of spaces, not just dimensions. Explicitly, since induces an isomorphism from to the existence of a basis for that extends any given basis of implies, via the splitting lemma, that Taking dimensions, the rank–nullity theorem follows.
Linear maps can be represented with matrices. More precisely, an matrix M represents a linear map where is the underlying field. So, the dimension of the domain of is n, the number of columns of M, and the rank–nullity theorem for an matrix M is
Here we provide two proofs. The first operates in the general case, using linear maps. The second proof looks at the homogeneous system where is a with rank and shows explicitly that there exists a set of linearly independent solutions that span the null space of .
While the theorem requires that the domain of the linear map be finite-dimensional, there is no such assumption on the codomain. This means that there are linear maps not given by matrices for which the theorem applies. Despite this, the first proof is not actually more general than the second: since the image of the linear map is finite-dimensional, we can represent the map from its domain to its image by a matrix, prove the theorem for that matrix, then compose with the inclusion of the image into the full codomain.
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In mathematics, a matrix (plural matrices) is a rectangular array or table of numbers, symbols, or expressions, arranged in rows and columns, which is used to represent a mathematical object or a property of such an object. For example, is a matrix with two rows and three columns. This is often referred to as a "two by three matrix", a " matrix", or a matrix of dimension . Without further specifications, matrices represent linear maps, and allow explicit computations in linear algebra.
In mathematics, the kernel of a linear map, also known as the null space or nullspace, is the linear subspace of the domain of the map which is mapped to the zero vector. That is, given a linear map L : V → W between two vector spaces V and W, the kernel of L is the vector space of all elements v of V such that L(v) = 0, where 0 denotes the zero vector in W, or more symbolically: The kernel of L is a linear subspace of the domain V.
In linear algebra, the column space (also called the range or ) of a matrix A is the span (set of all possible linear combinations) of its column vectors. The column space of a matrix is the or range of the corresponding matrix transformation. Let be a field. The column space of an m × n matrix with components from is a linear subspace of the m-space . The dimension of the column space is called the rank of the matrix and is at most min(m, n). A definition for matrices over a ring is also possible.
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