A new method for analysing nonlinear and non-stationary data has been developed. The key part of the method is the `empirical mode decomposition' method with which any complicated data set can be decomposed into a nite and often small number of `int
This textbook for students and practitioners presents a practical approach to decomposition techniques in optimization. It provides an appropriate blend of theoretical background and practical applications in engineering and science, which makes the
We discuss a multilinear generalization of the singular value decomposition. There is a strong analogy between several properties of the matrix and the higher-order tensor decomposition; uniqueness, link with the matrix eigenvalue decomposition, fir
powerful set of techniques for dealing with sets of equations or matrices. In many cases where Gaussian elimination and LU decomposition fail to give satisfactory results, this set of techniques, known as singular value decomposition, or SVD, will d
A few examples of how to use these programs are given, with a given dataset “gsta.dat”, which is the annual mean global surface temperature anomaly. In “gsta.dat”, the first column is the time; and the second is the corresponding data value.
