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  2. Row and column spaces - Wikipedia

    en.wikipedia.org/wiki/Row_and_column_spaces

    The row space is defined similarly. The row space and the column space of a matrix A are sometimes denoted as C(A T) and C(A) respectively. [2] This article considers matrices of real numbers. The row and column spaces are subspaces of the real spaces and respectively. [3]

  3. Kernel (linear algebra) - Wikipedia

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

    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 associated linear transformation. The kernel, the row space, the column space, and the left null space of A are the four fundamental subspaces associated with the matrix A.

  4. Row and column vectors - Wikipedia

    en.wikipedia.org/wiki/Row_and_column_vectors

    The transpose (indicated by T) of any row vector is a column vector, and the transpose of any column vector is a row vector: […] = [] and [] = […]. The set of all row vectors with n entries in a given field (such as the real numbers ) forms an n -dimensional vector space ; similarly, the set of all column vectors with m entries forms an m ...

  5. Matrix (mathematics) - Wikipedia

    en.wikipedia.org/wiki/Matrix_(mathematics)

    Matrices can be used to compactly write and work with multiple linear equations, that is, systems of linear equations. For example, if A is an m × n matrix, x designates a column vector (that is, n ×1 -matrix) of n variables x 1 , x 2 , ..., x n , and b is an m ×1 -column vector, then the matrix equation

  6. Vectorization (mathematics) - Wikipedia

    en.wikipedia.org/wiki/Vectorization_(mathematics)

    Vectorization is a unitary transformation from the space of n×n matrices with the Frobenius (or Hilbert–Schmidt) inner product to C n 2: ⁡ (†) = ⁡ † ⁡ (), where the superscript † denotes the conjugate transpose.

  7. Row equivalence - Wikipedia

    en.wikipedia.org/wiki/Row_equivalence

    The row space of a matrix is the set of all possible linear combinations of its row vectors. If the rows of the matrix represent a system of linear equations , then the row space consists of all linear equations that can be deduced algebraically from those in the system.

  8. Hermite normal form - Wikipedia

    en.wikipedia.org/wiki/Hermite_normal_form

    In linear algebra, the Hermite normal form is an analogue of reduced echelon form for matrices over the integers Z.Just as reduced echelon form can be used to solve problems about the solution to the linear system Ax=b where x is in R n, the Hermite normal form can solve problems about the solution to the linear system Ax=b where this time x is restricted to have integer coordinates only.

  9. Linear subspace - Wikipedia

    en.wikipedia.org/wiki/Linear_subspace

    If V is a vector space over a field K, a subset W of V is a linear subspace of V if it is a vector space over K for the operations of V.Equivalently, a linear subspace of V is a nonempty subset W such that, whenever w 1, w 2 are elements of W and α, β are elements of K, it follows that αw 1 + βw 2 is in W.