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A matrix that is its own inverse (i.e., a matrix A such that A = A −1, and consequently A 2 = I), is called an involutory matrix. In relation to its adjugate. The adjugate of a matrix A can be used to find the inverse of A as follows: If A is an invertible matrix, then
In linear algebra, the Sherman–Morrison formula, named after Jack Sherman and Winifred J. Morrison, computes the inverse of a " rank -1 update" to a matrix whose inverse has previously been computed. [1] [2] [3] That is, given an invertible matrix and the outer product of vectors and the formula cheaply computes an updated matrix inverse.
In mathematics, a matrix ( pl.: matrices) is a rectangular array or table of numbers, symbols, or expressions, arranged in rows and columns, which is used to represent a mathematical object or property of such an object. For example, is a matrix with two rows and three columns. This is often referred to as a "two by three matrix", a " matrix ...
Inverse iteration. In numerical analysis, inverse iteration (also known as the inverse power method) is an iterative eigenvalue algorithm. It allows one to find an approximate eigenvector when an approximation to a corresponding eigenvalue is already known. The method is conceptually similar to the power method .
Moore–Penrose inverse. In mathematics, and in particular linear algebra, the Moore–Penrose inverse of a matrix , often called the pseudoinverse, is the most widely known generalization of the inverse matrix. [1] It was independently described by E. H. Moore in 1920, [2] Arne Bjerhammar in 1951, [3] and Roger Penrose in 1955. [4]
Vandermonde matrix. In linear algebra, a Vandermonde matrix, named after Alexandre-Théophile Vandermonde, is a matrix with the terms of a geometric progression in each row: an matrix. with entries , the jth power of the number , for all zero-based indices and . [1] Some authors define the Vandermonde matrix as the transpose of the above matrix.
The linear map h → J(x) ⋅ h is known as the derivative or the differential of f at x . When m = n, the Jacobian matrix is square, so its determinant is a well-defined function of x, known as the Jacobian determinant of f. It carries important information about the local behavior of f.
An orthogonal matrix Q is necessarily invertible (with inverse Q−1 = QT ), unitary ( Q−1 = Q∗ ), where Q∗ is the Hermitian adjoint ( conjugate transpose) of Q, and therefore normal ( Q∗Q = QQ∗) over the real numbers. The determinant of any orthogonal matrix is either +1 or −1. As a linear transformation, an orthogonal matrix ...