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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. Jordan normal form - Wikipedia

    en.wikipedia.org/wiki/Jordan_normal_form

    By the rank-nullity theorem, dim(ker(A−λI))=n-r, so t=n-r-s, and so the number of vectors in the potential basis is equal to n. To show linear independence, suppose some linear combination of the vectors is 0.

  4. Rank (linear algebra) - Wikipedia

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

    The rank of a matrix plus the nullity of the matrix equals the number of columns of the matrix. (This is the rank–nullity theorem.) If A is a matrix over the real numbers then the rank of A and the rank of its corresponding Gram matrix are equal.

  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. Row and column spaces - Wikipedia

    en.wikipedia.org/wiki/Row_and_column_spaces

    The dimension of the row space is called the rank of the matrix. This is the same as the maximum number of linearly independent rows that can be chosen from the matrix, or equivalently the number of pivots. For example, the 3 × 3 matrix in the example above has rank two. [9] The rank of a matrix is also equal to the dimension of the column space.

  7. Wikipedia:Reference desk/Archives/Mathematics/2017 March 27

    en.wikipedia.org/wiki/Wikipedia:Reference_desk/...

    Use the given information to find the rank of the linear transformation T where T : V → W. The null space of T : P 5 → P 5 is P 5. I used the rank–nullity theorem and produced the following: rank(T) + nullity(T) = dim(V) nullity(T) = 6, dim(V) = 6 rank(T) + 6 = 6 rank(T) = 0. Is this result correct? I feel like I erred somewhere.

  8. List of theorems - Wikipedia

    en.wikipedia.org/wiki/List_of_theorems

    Principal axis theorem (linear algebra) Rank–nullity theorem (linear algebra) Rouché–Capelli theorem (Linear algebra) Sinkhorn's theorem (matrix theory) Specht's theorem (matrix theory) Spectral theorem (linear algebra, functional analysis) Sylvester's determinant theorem (determinants) Sylvester's law of inertia (quadratic forms)

  9. Isomorphism theorems - Wikipedia

    en.wikipedia.org/wiki/Isomorphism_theorems

    The isomorphism theorems for vector spaces (modules over a field) and abelian groups (modules over ) are special cases of these. For finite-dimensional vector spaces, all of these theorems follow from the rank–nullity theorem. In the following, "module" will mean "R-module" for some fixed ring R.