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An infinite set of vectors is linearly independent if every nonempty finite subset is linearly independent. Conversely, an infinite set of vectors is linearly dependent if it contains a finite subset that is linearly dependent, or equivalently, if some vector in the set is a linear combination of other vectors in the set.
For finite-dimensional real vectors in with the usual Euclidean dot product, the Gram matrix is =, where is a matrix whose columns are the vectors and is its transpose whose rows are the vectors . For complex vectors in C n {\displaystyle \mathbb {C} ^{n}} , G = V † V {\displaystyle G=V^{\dagger }V} , where V † {\displaystyle V^{\dagger ...
In mathematics, the Zassenhaus algorithm [1] is a method to calculate a basis for the intersection and sum of two subspaces of a vector space. It is named after Hans Zassenhaus, but no publication of this algorithm by him is known. [2] It is used in computer algebra systems. [3]
In combinatorics, a matroid / ˈ m eɪ t r ɔɪ d / is a structure that abstracts and generalizes the notion of linear independence in vector spaces.There are many equivalent ways to define a matroid axiomatically, the most significant being in terms of: independent sets; bases or circuits; rank functions; closure operators; and closed sets or flats.
The Gram–Schmidt process takes a finite, linearly independent set of vectors = {, …,} for k ≤ n and generates an orthogonal set ′ = {, …,} that spans the same -dimensional subspace of as . The method is named after Jørgen Pedersen Gram and Erhard Schmidt , but Pierre-Simon Laplace had been familiar with it before Gram and Schmidt. [ 1 ]
A linearly independent set L is a basis if and only if it is maximal, that is, it is not a proper subset of any linearly independent set. If V is a vector space of dimension n, then: A subset of V with n elements is a basis if and only if it is linearly independent. A subset of V with n elements is a basis if and only if it is a spanning set of V.
Suppose the eigenvectors of A form a basis, or equivalently A has n linearly independent eigenvectors v 1, v 2, ..., v n with associated eigenvalues λ 1, λ 2, ..., λ n. The eigenvalues need not be distinct. Define a square matrix Q whose columns are the n linearly independent eigenvectors of A,
Since these four row vectors are linearly independent, the row space is 4-dimensional. Moreover, in this case it can be seen that they are all orthogonal to the vector n = [6, −1, 4, −4, 0] ( n is an element of the kernel of J ), so it can be deduced that the row space consists of all vectors in R 5 {\displaystyle \mathbb {R} ^{5}} that are ...