Matrix Ostrowski inequality via the matrix geometric mean

The matrix arithmetic-geometric mean inequality revisited

The matrix geometric mean

An attractive candidate for the geometric mean of m positive definite matrices A1, . . . , Am is their Riemannian barycentre G. One of its important properties, monotonicity in the m arguments, has been established recently by J. Lawson and Y. Lim. We give a much simpler proof of this result, and prove some other inequalities. One of these says that, for every unitarily invariant norm, |||G||| ...

An Inequality of Ostrowski Type via Pompeiu’s Mean Value Theorem

 (b− a)M, for all x ∈ [a, b] . The constant 14 is best possible in the sense that it cannot be replaced by a smaller constant. In [2], the author has proved the following Ostrowski type inequality. Theorem 2. Let f : [a, b] → R be continuous on [a, b] with a > 0 and differentiable on (a, b) . Let p ∈ R\ {0} and assume that Kp (f ) := sup u∈(a,b) { u |f ′ (u)| } < ∞. Then we have the inequality...

Computing the Matrix Geometric Mean of Two HPD Matrices: A Stable Iterative Method

A new iteration scheme for computing the sign of a matrix which has no pure imaginary eigenvalues is presented. Then, by applying a well-known identity in matrix functions theory, an algorithm for computing the geometric mean of two Hermitian positive definite matrices is constructed. Moreover, another efficient algorithm for this purpose is derived free from the computation of principal matrix...

Matrix sparsification via the Khintchine inequality

Given a matrix A ∈ Rn×n, we present a simple, element-wise sparsification algorithm that zeroes out all sufficiently small elements of A, keeps all sufficiently large elements of A, and retains some of the remaining elements with probabilities proportional to the square of their magnitudes. We analyze the approximation accuracy of the proposed algorithm using a powerful inequality bounding the ...