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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
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:
In linear algebra, the transpose of a linear map between two vector spaces, defined over the same field, is an induced map between the dual spaces of the two vector spaces. The transpose or algebraic adjoint of a linear map is often used to study the original linear map.
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]
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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^{*}} .
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.
The fact that two matrices are row equivalent if and only if they have the same row space is an important theorem in linear algebra. The proof is based on the following observations: Elementary row operations do not affect the row space of a matrix. In particular, any two row equivalent matrices have the same row space.