The paper deals with robust direction of arrival (DOA) estimation based on independent component analysis (ICA) in the presence of sensor gain perturbation and unknown noise fields. It has been shown that the noise subspace based estimator suffers from the drawback of being very sensitivity to orthogonality between the estimated noise subspace and true signal subspace. However, no single subspa...

A new direct constrained optimization algorithm for minimizing the Kohn-Sham (KS) total energy functional is presented in this paper. The key ingredients of this algorithm involve projecting the total energy functional into a sequences of subspaces of small dimensions and seeking the minimizer of total energy functional within each subspace. The minimizer of a subspace energy functional not onl...

Various strategies for remapping hyperspace associated with two iterative genetic algorithms are reviewed, compared and empirically tested on a particular \real-world" problem. Both algorithms reduce the hypercube at each iteration such that each new mapping samples only subpartitions of the search space. Delta coding remaps the search space at each iteration by sampling the subspace numericall...

Convergence results are provided for inexact two-sided inverse and Rayleigh quotient iteration, which extend the previously established results to the generalized eigenproblem, and inexact solves with a decreasing solve tolerance. Moreover, the simultaneous solution of the forward and adjoint problem arising in two-sided methods is considered and the successful tuning strategy for preconditione...

Subspace learning and matrix factorization problems have a great many applications in science and engineering, and efficient algorithms are critical as dataset sizes continue to grow. Many relevant problem formulations are non-convex, and in a variety of contexts it has been observed that solving the non-convex problem directly is not only efficient but reliably accurate. We discuss convergence...

We introduce a new Krylov subspace iteration for large scale eigen-value problems that is able to accelerate the convergence through an inexact (iterative) solution to a shift-invert equation. The method also takes full advantage of an exact solution when it is possible to apply a sparse direct method to solve the shift-invert equations. We call this new iteration the Truncated RQ iteration (TR...

This article discusses a projection method for nonlinear eigenvalue problems. The subspace of approximants is constructed by a Jacobi–Davidson type approach, and the arising eigenproblems of small dimension are solved by safeguarded iteration. The method is applied to a rational eigenvalue problem governing the vibrations of tube bundle immersed in an inviscid compressible fluid.

We derive a Grassmann-Rayleigh Quotient Iteration for the computation of the best rank-(R1, R2, R3) approximation of higher-order tensors. We present some variants that allow for a very efficient estimation of the signal subspace in ICA schemes without prewhitening.

bstract. Spectral separation is the process of obtaining printer ontrol values to reproduce a given spectral reflectance. Given a ultispectral image where each pixel represents a spectral reflecance, separation could be implemented by inverting a physical rinter model on a pixel-by-pixel basis. Such a process would obviusly need to be very fast to handle high-resolution images in a easonable ti...