Diagnostic checking

This paper presents a short survey of recent research on Krylov subspace methods with emphasis on implementation on vector and parallel computers. Conjugate gradient methods have proven very useful on traditional scalar computers, and their popularity is likely to increase as three dimensional

In pattern recognition, feature vectors are occasionally subject to non-negative constraints. This characteristic can be expressed by a cone in feature vector space. In this paper, we propose cone-restricted subspace methods. The proposed methods admit the scaling and additivity of vectors as well

is compressed to obtain a low-dimensional subspace, called the eigenspace, in which the visual workspace is represented as a continuous appearance manifold. Given an unknown input image, the recognition system first projects the image to eigenspace. The parameters of the vision task are recognized based

In this paper we study “nonstationary consistency” of subspace methods for eigenstructure identification, i.e., the ability of subspace algorithms to converge to the true eigenstructure despite nonstationarities in the excitation and measurement noises. Note that such nonstationarities may result

We show how stability of models can be guaranteed when using the class of identification algorithms which have become known as ‘subspace methods’. In many of these methods the ‘A ’ matrix is obtained (or can be obtained) as the product of a shifted matrix with a pseudo-inverse. We show

. Adaptive mesh refinement (AMR) is one efficient way to reduce the computational cost. We propose a new AMR scheme for topology optimization that results in more robust and efficient solutions. For large sparse symmetric linear systems arising in topology optimization, Krylov subspace methods are required

A novel approach for speech dereverberation via sub-band implementation of subspace methods is presented 1. In recent work we presented a method utilizing the null subspace of the spatial-temporal correlation matrix of the received signals (obtained by the generalized eigenvalue decomposition (GEVD

Summary. Krylov subspace methods are well-known for their nice properties, but they have to be implemented with care. In this article the mathematical conse-quences encountered during implementation of Krylov subspace methods in an ex-isting layout-simulator are discussed. Brie y

We prove that the set of all reflectance functions (the mapping from surface normals to intensities) produced by Lambertian objects under distant, isotropic lighting lies close to a 9D linear subspace. This implies that, in general, the set of images of a convex Lambertian object obtained under a