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  2. Kernel (linear algebra) - Wikipedia

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

    The left null space, or cokernel, of a matrix A consists of all column vectors x such that x T A = 0 T, where T denotes the transpose of a matrix. The left null space of A is the same as the kernel of A T. The left null space of A is the orthogonal complement to the column space of A, and is dual to the cokernel of the

  3. Row and column spaces - Wikipedia

    en.wikipedia.org/wiki/Row_and_column_spaces

    The left null space of A is the set of all vectors x such that x T A = 0 T. It is the same as the null space of the transpose of A. The product of the matrix A T and the vector x can be written in terms of the dot product of vectors:

  4. Transpose of a linear map - Wikipedia

    en.wikipedia.org/wiki/Transpose_of_a_linear_map

    The assignment produces an injective linear map between the space of linear operators from to and the space of linear operators from # to #. If X = Y {\displaystyle X=Y} then the space of linear maps is an algebra under composition of maps , and the assignment is then an antihomomorphism of algebras, meaning that t ( u v ) = t v t u ...

  5. EP matrix - Wikipedia

    en.wikipedia.org/wiki/EP_matrix

    This is similar to the characterization of normal matrices where A commutes with its conjugate transpose. [4] As a corollary, nonsingular matrices are always EP matrices. The sum of EP matrices A i is an EP matrix if the null-space of the sum is contained in the null-space of each matrix A i. [6]

  6. Transpose - Wikipedia

    en.wikipedia.org/wiki/Transpose

    In linear algebra, the transpose of a matrix is an operator which flips a matrix over its diagonal; that is, it switches the row and column indices of the matrix A by producing another matrix, often denoted by A T (among other notations). [1] The transpose of a matrix was introduced in 1858 by the British mathematician Arthur Cayley. [2]

  7. Rank–nullity theorem - Wikipedia

    en.wikipedia.org/wiki/Rank–nullity_theorem

    The second proof [6] looks at the homogeneous system =, where is a with rank, and shows explicitly that there exists a set of linearly independent solutions that span the null space of . While the theorem requires that the domain of the linear map be finite-dimensional, there is no such assumption on the codomain.

  8. Moore–Penrose inverse - Wikipedia

    en.wikipedia.org/wiki/Moore–Penrose_inverse

    The vector space of ⁠ ⁠ matrices over ⁠ ⁠ is denoted by ⁠ ⁠. For ⁠ A ∈ K m × n {\displaystyle A\in \mathbb {K} ^{m\times n}} ⁠ , the transpose is denoted ⁠ A T {\displaystyle A^{\mathsf {T}}} ⁠ and the Hermitian transpose (also called conjugate transpose ) is denoted ⁠ A ∗ {\displaystyle A^{*}} ⁠ .

  9. 3D rotation group - Wikipedia

    en.wikipedia.org/wiki/3D_rotation_group

    Since u is in the null space of A, if one now rotates to a new basis, through some other orthogonal matrix O, with u as the z axis, the final column and row of the rotation matrix in the new basis will be zero. Thus, we know in advance from the formula for the exponential that exp(OAO T) must leave u fixed.