This paper deals with Vandermonde matrices V whose nodes are the equidistant points in [0,1]. We give an analytic factorization and explicit formula for the entries of their inverse, and explore its computational issues. We also give asymptotic estimates of the Frobenius norm of both V and its inverse and show that a new representation of the floating point number system allows one to build an ...

In this work, an iterative method based on a matrix form of LSQR algorithm is constructed for solving the linear operator equation A(X) = B and the minimum Frobenius norm residual problem ||A(X)−B||F where X ∈ S := {X ∈ Rn×n | X = G(X)}, F is the linear operator from Rn×n onto Rr×s, G is a linear selfconjugate involution operator and B ∈ Rr×s. Numerical examples are given to verify the efficien...

We consider the estimation of the Brain Electrical Sources (BES) matrix from noisy electroencephalographic (EEG) measurements, commonly named as the EEG inverse problem. We propose a new method to induce neurophysiological meaningful solutions, which takes into account the smoothness, structured sparsity, and low rank of the BES matrix. The method is based on the factorization of the BES matrix...

Low rank matrix approximations have many applications in different domains. In system theory it has been used in model reduction schemes, in system identification with outputerror models and in static errors-in-variables problems, for instance. The approximations are mostly performed using the singular value decomposition. This is optimal for all unitarily invariant matrix norms, such as the Fr...

This article proposes a constrained 1 minimization method for estimating a sparse inverse covariance matrix based on a sample of n iid p-variate random variables. The resulting estimator is shown to have a number of desirable properties. In particular, the rate of convergence between the estimator and the true s-sparse precision matrix under the spectral norm is s √ log p/n when the population ...

This paper deals with Vandermonde matrices Vn whose nodes are the Gauss–Lobatto Chebyshev nodes, also called extrema Chebyshev nodes. We give an analytic factorization and explicit formula for the entries of their inverse, and explore its computational issues. We also give asymptotic estimates of the Frobenius norm of both Vn and its inverse and present an explicit formula for the determinant o...

the best rank-r approximation of X with respect to the Frobenius norm. We write ∆r = X − Tr(X) for the ’residual’. In general, Tr(M) wil be used to denote the best rank-r approximation of a matrix M . Further, PM denotes the orthogonal projection on the subspace spanned by the columns of M , and we write M− for the Moore-Penrose pseudoinverse of a matrix M . The i-th column of M is denoted by M...