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Although an explicit inverse is not necessary to estimate the vector of unknowns, it is the easiest way to estimate their accuracy, found in the diagonal of a matrix inverse (the posterior covariance matrix of the vector of unknowns). However, faster algorithms to compute only the diagonal entries of a matrix inverse are known in many cases. [19]
A square root of a 2×2 matrix M is another 2×2 matrix R such that M = R 2, where R 2 stands for the matrix product of R with itself. In general, there can be zero, two, four, or even an infinitude of square-root matrices. In many cases, such a matrix R can be obtained by an explicit formula.
An involutory matrix which is also symmetric is an orthogonal matrix, and thus represents an isometry (a linear transformation which preserves Euclidean distance). Conversely every orthogonal involutory matrix is symmetric. [3] As a special case of this, every reflection and 180° rotation matrix is involutory.
An orthogonal matrix A is necessarily invertible (with inverse A −1 = A T), unitary (A −1 = A*), and normal (A*A = AA*). The determinant of any orthogonal matrix is either +1 or −1. The special orthogonal group SO ( n ) {\displaystyle \operatorname {SO} (n)} consists of the n × n orthogonal matrices with determinant +1.
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 (+)).
Calculating the inverse matrix once, and storing it to apply at each iteration is of complexity O(n 3) + k O(n 2). Storing an LU decomposition of ( A − μ I ) {\displaystyle (A-\mu I)} and using forward and back substitution to solve the system of equations at each iteration is also of complexity O ( n 3 ) + k O ( n 2 ).
In mathematics, and in particular, algebra, a generalized inverse (or, g-inverse) of an element x is an element y that has some properties of an inverse element but not necessarily all of them. The purpose of constructing a generalized inverse of a matrix is to obtain a matrix that can serve as an inverse in some sense for a wider class of ...
In this case, the determinant may not be 1, but will have absolute value 1. In the 2×2 case (n=1), M will be the product of a real symplectic matrix and a complex number of absolute value 1. Other authors [7] retain the definition for complex matrices and call matrices satisfying conjugate symplectic.