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An exchange matrix is the simplest anti-diagonal matrix.; Any matrix A satisfying the condition AJ = JA is said to be centrosymmetric.; Any matrix A satisfying the condition AJ = JA T is said to be persymmetric.
To use column-major order in a row-major environment, or vice versa, for whatever reason, one workaround is to assign non-conventional roles to the indexes (using the first index for the column and the second index for the row), and another is to bypass language syntax by explicitly computing positions in a one-dimensional array.
The row space of this matrix is the vector space spanned by the row vectors. The column vectors of a matrix. The column space of this matrix is the vector space spanned by the column vectors. In linear algebra, the column space (also called the range or image) of a matrix A is the span (set of all possible linear combinations) of its column ...
COLEX stands for column exchange. Since the beginning of the atomic era , a variety of lithium enrichments methods have been developed (such as chemical exchange, electromagnetic, laser, centrifugal [ 1 ] ) and the COLEX process has been the most extensively implemented method so far.
A pivot position in a matrix, A, is a position in the matrix that corresponds to a row–leading 1 in the reduced row echelon form of A. Since the reduced row echelon form of A is unique, the pivot positions are uniquely determined and do not depend on whether or not row interchanges are performed in the reduction process.
Normally, a matrix represents a linear map, and the product of a matrix and a column vector represents the function application of the corresponding linear map to the vector whose coordinates form the column vector. The change-of-basis formula is a specific case of this general principle, although this is not immediately clear from its ...
The above example of matrices demonstrates that matrix product of top row and leftmost columns of involved matrices plays special role for to succeed. Let us mark consecutive versions of matrices with (), (), … and then let us write matrix product () = () in such way that these rows and columns are separated from the rest.
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 ...