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  2. 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.

  3. 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.

  4. Basis function - Wikipedia

    en.wikipedia.org/wiki/Basis_function

    The monomial basis also forms a basis for the vector space of polynomials. After all, every polynomial can be written as a 0 + a 1 x 1 + a 2 x 2 + ⋯ + a n x n {\displaystyle a_{0}+a_{1}x^{1}+a_{2}x^{2}+\cdots +a_{n}x^{n}} for some n ∈ N {\displaystyle n\in \mathbb {N} } , which is a linear combination of monomials.

  5. Vector space - Wikipedia

    en.wikipedia.org/wiki/Vector_space

    The closure property also implies that every intersection of linear subspaces is a linear subspace. [11] Linear span Given a subset G of a vector space V, the linear span or simply the span of G is the smallest linear subspace of V that contains G, in the sense that it is the intersection of all linear subspaces that contain G.

  6. 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.

  7. Examples of vector spaces - Wikipedia

    en.wikipedia.org/wiki/Examples_of_vector_spaces

    Both vector addition and scalar multiplication are trivial. A basis for this vector space is the empty set, so that {0} is the 0-dimensional vector space over F. Every vector space over F contains a subspace isomorphic to this one. The zero vector space is conceptually different from the null space of a linear operator L, which is the kernel of L.

  8. Minimal polynomial (linear algebra) - Wikipedia

    en.wikipedia.org/wiki/Minimal_polynomial_(linear...

    For example, if A is a multiple aI n of the identity matrix, then its minimal polynomial is X − a since the kernel of aI n − A = 0 is already the entire space; on the other hand its characteristic polynomial is (X − a) n (the only eigenvalue is a, and the degree of the characteristic polynomial is always equal to the dimension of the ...

  9. Monomial basis - Wikipedia

    en.wikipedia.org/wiki/Monomial_basis

    In mathematics the monomial basis of a polynomial ring is its basis (as a vector space or free module over the field or ring of coefficients) that consists of all monomials.The monomials form a basis because every polynomial may be uniquely written as a finite linear combination of monomials (this is an immediate consequence of the definition of a polynomial).