A new proof of the Benedetti's inequality and some applications to perturbation to real eigenvalues and singular values (2016)
Using the standard deviation of the real samples μn ≥ … ≥ μ1 and λn ≥ … ≥ λ1, we refine the Chebyshev's inequality (refer to [5]),As a consequence, we obtain a new proof of the Benedetti's inequality (refer to [1], [2] and [4])where Cov[μ, λ], s(μ) and s(λ) denote the covariance, and the standard deviations (≠ 0) of the sample vectors μ = (μ1, …, μn) and λ = (λ1, …, λn), respectively.We can also get very interesting applications to eigenvalues and singular values perturbation theory. For some kinds of matrices, the result that we present improves the well known Homand-Weiland's inequality.