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Since matrix multiplication forms the basis for many algorithms, and many operations on matrices even have the same complexity as matrix multiplication (up to a multiplicative constant), the computational complexity of matrix multiplication appears throughout numerical linear algebra and theoretical computer science.
A matrix whose elements are of the form 1/(x i + y j) for (x i), (y j) injective sequences (i.e., taking every value only once). Centrosymmetric matrix: A matrix symmetric about its center; i.e., a ij = a n−i+1,n−j+1. Circulant matrix: A matrix where each row is a circular shift of its predecessor. Conference matrix
The identity matrix is the only idempotent matrix with non-zero determinant. That is, it is the only matrix such that: When multiplied by itself, the result is itself; All of its rows and columns are linearly independent. The principal square root of an identity matrix is itself, and this is its only positive-definite square root. However ...
Here, vec(X) denotes the vectorization of the matrix X, formed by stacking the columns of X into a single column vector. It now follows from the properties of the Kronecker product that the equation AXB = C has a unique solution, if and only if A and B are invertible ( Horn & Johnson 1991 , Lemma 4.3.1).
The above identities concerning the determinant of products and inverses of matrices imply that similar matrices have the same determinant: two matrices A and B are similar, if there exists an invertible matrix X such that A = X −1 BX. Indeed, repeatedly applying the above identities yields
It is known that a Strassen-like algorithm with a 2x2-block matrix step requires at least 7 block matrix multiplications. In 1976 Probert [16] showed that such an algorithm requires at least 15 additions (including subtractions), however, a hidden assumption was that the blocks and the 2x2-block matrix are represented in the same basis ...
The conjugate transpose, therefore, arises very naturally as the result of simply transposing such a matrix—when viewed back again as an matrix made up of complex numbers. For an explanation of the notation used here, we begin by representing complex numbers e i θ {\displaystyle e^{i\theta }} as the rotation matrix, that is,
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 =