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  2. Rank–nullity theorem - Wikipedia

    en.wikipedia.org/wiki/Ranknullity_theorem

    Rank–nullity theorem. The rank–nullity theorem is a theorem in linear algebra, which asserts: the number of columns of a matrix M is the sum of the rank of M and the nullity of M; and; the dimension of the domain of a linear transformation f is the sum of the rank of f (the dimension of the image of f) and the nullity of f (the dimension of ...

  3. Rank (linear algebra) - Wikipedia

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

    A matrix is said to have full rank if its rank equals the largest possible for a matrix of the same dimensions, which is the lesser of the number of rows and columns. A matrix is said to be rank-deficient if it does not have full rank. The rank deficiency of a matrix is the difference between the lesser of the number of rows and columns, and ...

  4. Quotient space (linear algebra) - Wikipedia

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

    An immediate corollary, for finite-dimensional spaces, is the rank–nullity theorem: the dimension of V is equal to the dimension of the kernel (the nullity of T) plus the dimension of the image (the rank of T). The cokernel of a linear operator T : V → W is defined to be the quotient space W/im(T).

  5. Kernel (linear algebra) - Wikipedia

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

    In the case where V is finite-dimensional, this implies the rank–nullity theorem: ⁡ (⁡) + ⁡ (⁡) = ⁡ (). where the term rank refers to the dimension of the image of L, ⁡ (⁡), while nullity refers to the dimension of the kernel of L, ⁡ (⁡). [4] That is, ⁡ = ⁡ (⁡) ⁡ = ⁡ (⁡), so that the rank–nullity theorem can be ...

  6. Frobenius theorem (real division algebras) - Wikipedia

    en.wikipedia.org/wiki/Frobenius_theorem_(real...

    The set V of all elements a of D such that a 2 ≤ 0 is a vector subspace of D of dimension n − 1. Moreover D = R ⊕ V as R-vector spaces, which implies that V generates D as an algebra. Proof of Claim: Pick a in D with characteristic polynomial p(x). By the fundamental theorem of algebra, we can write

  7. Metric tensor - Wikipedia

    en.wikipedia.org/wiki/Metric_tensor

    It follows from the definition of non-degeneracy that the kernel of S g is reduced to zero, and so by the rank–nullity theorem, S g is a linear isomorphism. Furthermore, S g is a symmetric linear transformation in the sense that [,] = [,] for all tangent vectors X p and Y p.

  8. Schmidt decomposition - Wikipedia

    en.wikipedia.org/wiki/Schmidt_decomposition

    Consider a vector of the tensor product . in the form of Schmidt decomposition = =. Form the rank 1 matrix =.Then the partial trace of , with respect to either system A or B, is a diagonal matrix whose non-zero diagonal elements are | |.

  9. Nullity (graph theory) - Wikipedia

    en.wikipedia.org/wiki/Nullity_(graph_theory)

    The nullity of M is given by m − n + c, where, c is the number of components of the graph and n − c is the rank of the oriented incidence matrix. This name is rarely used; the number is more commonly known as the cycle rank, cyclomatic number, or circuit rank of the graph. It is equal to the rank of the cographic matroid of the graph.

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