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  2. Linear independence - Wikipedia

    en.wikipedia.org/wiki/Linear_independence

    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.

  3. Gram matrix - Wikipedia

    en.wikipedia.org/wiki/Gram_matrix

    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 ...

  4. Free module - Wikipedia

    en.wikipedia.org/wiki/Free_module

    In mathematics, a free module is a module that has a basis, that is, a generating set that is linearly independent. Every vector space is a free module, [1] but, if the ring of the coefficients is not a division ring (not a field in the commutative case), then there exist non-free modules. Given any set S and ring R, there is a free R-module ...

  5. Matroid - Wikipedia

    en.wikipedia.org/wiki/Matroid

    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.

  6. Zassenhaus algorithm - Wikipedia

    en.wikipedia.org/wiki/Zassenhaus_algorithm

    Consider the two subspaces = (), and = (), of the vector space .. Using the standard basis, we create the following matrix of dimension (+) (): ().Using elementary row operations, we transform this matrix into the following matrix:

  7. Gram–Schmidt process - Wikipedia

    en.wikipedia.org/wiki/Gram–Schmidt_process

    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 ]

  8. Wronskian - Wikipedia

    en.wikipedia.org/wiki/Wronskian

    In mathematics, the Wronskian of n differentiable functions is the determinant formed with the functions and their derivatives up to order n – 1.It was introduced in 1812 by the Polish mathematician Józef Wroński, and is used in the study of differential equations, where it can sometimes show the linear independence of a set of solutions.

  9. Dual basis - Wikipedia

    en.wikipedia.org/wiki/Dual_basis

    In linear algebra, given a vector space with a basis of vectors indexed by an index set (the cardinality of is the dimension of ), the dual set of is a set of vectors in the dual space with the same index set such that and form a biorthogonal system.