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Krylov subspace methods on supercomputers
- SIAM J. SCI. STAT. COMPUT
, 1989
"... 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 model ..."
Abstract
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Cited by 79 (4 self)
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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
Subspace Methods for Robot Vision
, 1995
"... In contrast to the traditional approach, visual recognition is formulated as one of matching appearance rather than shape. For any given robot vision task, all possible appearance variations define its visual workspace. A set of images is obtained by coarsely sampling the workspace. The image set is ..."
Abstract
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Cited by 90 (2 self)
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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
Nonstationary consistency of subspace methods
, 2006
"... 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 i ..."
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Cited by 13 (7 self)
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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
Cone-Restricted Subspace Methods
"... 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 a ..."
Abstract
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Cited by 3 (3 self)
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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
Krylov Subspace Methods for . . .
, 2007
"... Topology optimization is a powerful tool for global and multiscale design of structures, microstructures, and materials. The computational bottleneck of topology optimization is the solution of a large number of extremely ill-conditioned linear systems arising in the finite element analysis. Adaptiv ..."
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. 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
Guaranteed Stability with Subspace Methods
, 1995
"... 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 that whenever ..."
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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
IMPLEMENTATION OF SUBSPACE METHODS
"... 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) ..."
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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
Lambertian Reflectance and Linear Subspaces
, 2000
"... 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 wi ..."
Abstract
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Cited by 526 (20 self)
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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
Krylov subspace methods in the electronic industry
, 2004
"... Summary. Krylov subspace methods are well-known for their nice properties, but they have to be implemented with care. In this article the mathematical consequences encountered during implementation of Krylov subspace methods in an existing layout-simulator are discussed. Briefly, the representation ..."
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Summary. Krylov subspace methods are well-known for their nice properties, but they have to be implemented with care. In this article the mathematical consequences encountered during implementation of Krylov subspace methods in an existing layout-simulator are discussed. Briefly
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