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  2. Vector space - Wikipedia

    en.wikipedia.org/wiki/Vector_space

    When the scalar field is the real numbers, the vector space is called a real vector space, and when the scalar field is the complex numbers, the vector space is called a complex vector space. [4] These two cases are the most common ones, but vector spaces with scalars in an arbitrary field F are also commonly considered.

  3. Examples of vector spaces - Wikipedia

    en.wikipedia.org/wiki/Examples_of_vector_spaces

    The dimension of this vector space, if it exists, [a] is called the degree of the extension. For example, the complex numbers C form a two-dimensional vector space over the real numbers R. Likewise, the real numbers R form a vector space over the rational numbers Q which has (uncountably) infinite dimension, if a Hamel basis exists. [b]

  4. Linear subspace - Wikipedia

    en.wikipedia.org/wiki/Linear_subspace

    If V is a vector space over a field K, a subset W of V is a linear subspace of V if it is a vector space over K for the operations of V.Equivalently, a linear subspace of V is a nonempty subset W such that, whenever w 1, w 2 are elements of W and α, β are elements of K, it follows that αw 1 + βw 2 is in W.

  5. Linear independence - Wikipedia

    en.wikipedia.org/wiki/Linear_independence

    A vector space can be of finite dimension or infinite dimension depending on the maximum number of linearly independent vectors. The definition of linear dependence and the ability to determine whether a subset of vectors in a vector space is linearly dependent are central to determining the dimension of a vector space.

  6. Basis (linear algebra) - Wikipedia

    en.wikipedia.org/wiki/Basis_(linear_algebra)

    The same vector can be represented in two different bases (purple and red arrows). In mathematics, a set B of vectors in a vector space V is called a basis (pl.: bases) if every element of V may be written in a unique way as a finite linear combination of elements of B.

  7. Kernel (linear algebra) - Wikipedia

    en.wikipedia.org/wiki/Kernel_(linear_algebra)

    The left null space of A is the same as the kernel of A T. The left null space of A is the orthogonal complement to the column space of A, and is dual to the cokernel of the associated linear transformation. The kernel, the row space, the column space, and the left null space of A are the four fundamental subspaces associated with the matrix A.

  8. Grassmann number - Wikipedia

    en.wikipedia.org/wiki/Grassmann_number

    For example, a single Grassmann number can be thought of as generating a one-dimensional space. A vector space, the m-dimensional superspace, then appears as the m-fold Cartesian product of these one-dimensional . [clarification needed] It can be shown that this is essentially equivalent to an algebra with m generators, but this requires work.

  9. Flag (linear algebra) - Wikipedia

    en.wikipedia.org/wiki/Flag_(linear_algebra)

    The stabilizer subgroup of the standard flag is the group of invertible upper triangular matrices.. More generally, the stabilizer of a flag (the linear operators on V such that () < for all i) is, in matrix terms, the algebra of block upper triangular matrices (with respect to an adapted basis), where the block sizes .