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  2. Linear span - Wikipedia

    en.wikipedia.org/wiki/Linear_span

    The cross-hatched plane is the linear span of u and v in both R 2 and R 3, here shown in perspective.. In mathematics, the linear span (also called the linear hull [1] or just span) of a set of elements of a vector space is the smallest linear subspace of that contains .

  3. Orthogonalization - Wikipedia

    en.wikipedia.org/wiki/Orthogonalization

    In linear algebra, orthogonalization is the process of finding a set of orthogonal vectors that span a particular subspace.Formally, starting with a linearly independent set of vectors {v 1, ... , v k} in an inner product space (most commonly the Euclidean space R n), orthogonalization results in a set of orthogonal vectors {u 1, ... , u k} that generate the same subspace as the vectors v 1 ...

  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. Gram–Schmidt process - Wikipedia

    en.wikipedia.org/wiki/Gram–Schmidt_process

    A variant of the Gram–Schmidt process using transfinite recursion applied to a (possibly uncountably) infinite sequence of vectors () < yields a set of orthonormal vectors () < with such that for any , the completion of the span of {: < (,)} is the same as that of {: <}.

  6. Row and column spaces - Wikipedia

    en.wikipedia.org/wiki/Row_and_column_spaces

    Another possible basis { [1, 0, 2], [0, 1, 0] } comes from a further reduction. [9] This algorithm can be used in general to find a basis for the span of a set of vectors. If the matrix is further simplified to reduced row echelon form, then the resulting basis is uniquely determined by the row space.

  7. 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.

  8. Projection (linear algebra) - Wikipedia

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

    For Banach spaces, a one-dimensional subspace always has a closed complementary subspace. This is an immediate consequence of Hahn–Banach theorem. Let be the linear span of . By Hahn–Banach, there exists a bounded linear functional such that φ(u) = 1.

  9. Eigenvalues and eigenvectors - Wikipedia

    en.wikipedia.org/wiki/Eigenvalues_and_eigenvectors

    Thus, the vectors v λ=1 and v ... Any subspace spanned by eigenvectors of T is an invariant subspace of T, and the restriction of T to such a subspace is diagonalizable.