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By the above reasoning, the kernel of A is the orthogonal complement to the row space. That is, a vector x lies in the kernel of A, if and only if it is perpendicular to every vector in the row space of A. The dimension of the row space of A is called the rank of A, and the dimension of the kernel of A is called the nullity of A.
Let V and W be vector spaces over a field (or more generally, modules over a ring) and let T be a linear map from V to W.If 0 W is the zero vector of W, then the kernel of T is the preimage of the zero subspace {0 W}; that is, the subset of V consisting of all those elements of V that are mapped by T to the element 0 W.
Real vector spaces and complex vector spaces are kinds of vector spaces based on different kinds of scalars: ... The kernel of this map is the subspace of vectors ...
Dual vector spaces find application in many branches of mathematics that use vector spaces, such as in tensor analysis with finite-dimensional vector spaces. When applied to vector spaces of functions (which are typically infinite-dimensional), dual spaces are used to describe measures , distributions , and Hilbert spaces .
The cokernel of a linear mapping of vector spaces f : X → Y is the quotient space Y / im(f) of the codomain of f by the image of f. The dimension of the cokernel is called the corank of f . Cokernels are dual to the kernels of category theory , hence the name: the kernel is a subobject of the domain (it maps to the domain), while the cokernel ...
The first isomorphism theorem for vector spaces says that the quotient space V/ker(T) is isomorphic to the image of V in W. An immediate corollary, for finite-dimensional spaces, is the rank–nullity theorem: the dimension of V is equal to the dimension of the kernel (the nullity of T) plus the dimension of the image (the rank of T).
In mathematics, and more specifically in linear algebra, a linear map (also called a linear mapping, linear transformation, vector space homomorphism, or in some contexts linear function) is a mapping between two vector spaces that preserves the operations of vector addition and scalar multiplication.
In a reflexive space the left and right radicals agree and are termed the kernel or the radical of the bilinear form: the subspace of all vectors orthogonal with every other vector. A vector v, with matrix representation x, is in the radical of a bilinear form with matrix representation A, if and only if Ax = 0 ⇔ x T A = 0.