Blind deconvolution of covariance matrix inverses for autoregressive processes

COVARIANCE MATRIX OF MULTIVARIATE REWARD PROCESSES WITH NONLINEAR REWARD FUNCTIONS

Multivariate reward processes with reward functions of constant rates, defined on a semi-Markov process, first were studied by Masuda and Sumita, 1991. Reward processes with nonlinear reward functions were introduced in Soltani, 1996. In this work we study a multivariate process , , where are reward processes with nonlinear reward functions respectively. The Laplace transform of the covar...

Blind image deconvolution using the Sylvester matrix

Blind image deconvolution refers to the process of determining both an exact image and the blurring function from its inexact image. This thesis presents a solution of the blind image deconvolution problem using polynomial computations. The proposed solution does not require prior knowledge of the blurring function or noise level. Blind image deconvolution is needed in many applications, such a...

Empirical Chaos Processes and Blind Deconvolution

This paper investigates conditions under which certain kinds of systems of bilinear equations have a unique structured solution. In particular, we look at when we can recover vectors w, q from observations of the form yl = 〈w, bl〉〈cl, q〉, l = 1, . . . , L, where bl, cl are known. We show that if w ∈ C M1 and q ∈ C2 are sparse, with no more than K and N nonzero entries, respectively, and the bl,...

EIGENVECTORS OF COVARIANCE MATRIX FOR OPTIMAL DESIGN OF STEEL FRAMES

In this paper, the discrete method of eigenvectors of covariance matrix has been used to weight minimization of steel frame structures. Eigenvectors of Covariance Matrix (ECM) algorithm is a robust and iterative method for solving optimization problems and is inspired by the CMA-ES method. Both of these methods use covariance matrix in the optimization process, but the covariance matrix calcula...

Internal multiscale autoregressive processes, stochastic realization, and covariance extension