The paper addresses methods for parameter sensitivity analysis in a large, nonlinear, mechanistic model which is to be run in an online estimation scheme. The parameter sensitivity has been obtained by numeric approximation. The paper proposes and applies successive orthogonalization of the sensitivity derivative for parameter ranking. The method is easy to implement and the results are easily ...

In this paper we address the orthogonalization of independent component analysis (ICA) to obtain transform-based image coders. We consider several classes of training images, from which we extract the independent components, followed by orthogonalization, obtaining bases for image coding. Experimental tests show the generalization ability of ICA of natural images, and the adaptation ability to ...

This paper reflects results of research related to developing a new methodology for automatic graph drawing based on applying genetic algorithms. The methodology has permitted the elaboration of a hybrid technique that combines the most popular, classical, topology-shape-metric approach to orthogonal drawings on the grid and a genetic algorithm that is directed, in its evolutionary process, at ...

A randomized Gram--Schmidt algorithm is developed for orthonormalization of high-dimensional vectors or QR factorization. The proposed process can be less computationally expensive than the classical while being at least as numerically stable modified process. Our approach based on random sketching, which a dimension reduction technique consisting in estimation inner products by their small eff...

We consider the problem of propagating an ensemble of solutions and its characterization in terms of its mean and covariance matrix. We propose differential equations that lead to a continuous matrix factorization of the ensemble into a generalized singular value decomposition (SVD). The continuous factorization is applied to ensemble propagation under periodic rescaling (ensemble breeding) and...