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443,504
GMRES: A generalized minimal residual algorithm for solving nonsymmetric linear systems
 SIAM J. SCI. STAT. COMPUT
, 1986
"... We present an iterative method for solving linear systems, which has the property ofminimizing at every step the norm of the residual vector over a Krylov subspace. The algorithm is derived from the Arnoldi process for constructing an l2orthogonal basis of Krylov subspaces. It can be considered a ..."
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Cited by 2041 (40 self)
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We present an iterative method for solving linear systems, which has the property ofminimizing at every step the norm of the residual vector over a Krylov subspace. The algorithm is derived from the Arnoldi process for constructing an l2orthogonal basis of Krylov subspaces. It can be considered
ENTROPYCONSTRAINED RESIDUAL VECTOR QUANTIZATION
"... This paper introduces a new variable rate residual vector quantizer (RVQ) where the source entropy is exploited. Necessary conditions for the optimality of this RVQ are presented as well as a new entropyconstrained RVQ (ECRVQ) design algorithm. ECRVQ is shown to simultaneously outperform entropy ..."
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This paper introduces a new variable rate residual vector quantizer (RVQ) where the source entropy is exploited. Necessary conditions for the optimality of this RVQ are presented as well as a new entropyconstrained RVQ (ECRVQ) design algorithm. ECRVQ is shown to simultaneously outperform entropy
The Dantzig selector: statistical estimation when p is much larger than n
, 2005
"... In many important statistical applications, the number of variables or parameters p is much larger than the number of observations n. Suppose then that we have observations y = Ax + z, where x ∈ R p is a parameter vector of interest, A is a data matrix with possibly far fewer rows than columns, n ≪ ..."
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Cited by 860 (14 self)
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, where r is the residual vector y − A˜x and t is a positive scalar. We show that if A obeys a uniform uncertainty principle (with unitnormed columns) and if the true parameter vector x is sufficiently sparse (which here roughly guarantees that the model is identifiable), then with very large probability
An AdaptiveSearch Residual Vector Quantizer For Airborne Reconnaissance
"... A lossy image compression algorithm designed for highspeed, high quality data applications is described. The algorithm consists of a vector quantizer followed by a modified Huffman entropy encoder. The quantizer is a meanremoved, adaptivesearch, residual vector quantizer. A few details of a highsp ..."
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A lossy image compression algorithm designed for highspeed, high quality data applications is described. The algorithm consists of a vector quantizer followed by a modified Huffman entropy encoder. The quantizer is a meanremoved, adaptivesearch, residual vector quantizer. A few details of a high
AdaptiveSearch TreeStructured Residual Vector Quantization
"... Fullsearch vector quantization (VQ) provides optimal results only with high memory and computational cost. We describe the computational and memory requirements of treestructured VQ, residual VQ (RVQ), and treestructured RVQ. We present multiplerate, adaptivesearch implementations of these VQ ..."
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Fullsearch vector quantization (VQ) provides optimal results only with high memory and computational cost. We describe the computational and memory requirements of treestructured VQ, residual VQ (RVQ), and treestructured RVQ. We present multiplerate, adaptivesearch implementations of these VQ
Necessary conditions for the optimality of variable rate residual vector quantizers
 IEEE Trans. Inform. Theory
, 1995
"... AbstractResidual vector quantizatlon (RVQ), or multistage VQ, as it is also called, has recently been shown to be a competitive technique for data compression [1]. The competitive performance of RVQ reported in [1] results from the joint optimization of variable rate encoding and RVQ directsum c ..."
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Cited by 1 (0 self)
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AbstractResidual vector quantizatlon (RVQ), or multistage VQ, as it is also called, has recently been shown to be a competitive technique for data compression [1]. The competitive performance of RVQ reported in [1] results from the joint optimization of variable rate encoding and RVQ direct
Article Approximate Nearest Neighbor Search by Residual Vector Quantization
, 2010
"... sensors ..."
Residual Vectors & Error Estimation in Substructure based Model Reduction
"... ii To my father iv Alternative energy sources, including wind energy, are often subject to scepticism. In general, society has a dual feeling about it, on the one hand the need is realised on the other hand a ‘not in my backyard ‘ feeling is experienced. This combined with the ever changing attitude ..."
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ii To my father iv Alternative energy sources, including wind energy, are often subject to scepticism. In general, society has a dual feeling about it, on the one hand the need is realised on the other hand a ‘not in my backyard ‘ feeling is experienced. This combined with the ever changing attitude and approach of national and international politics, the renewable energy industry has difficulties to find its place in society. On a long term vision however the necessity of renewable energy sources is very clear since the supply of fossil fuels will, whether it is a sudden drop or a slow decline, eventually run out. The debate of which renewable energy source will eventually dominate in the long run is still ongoing. Looking back on the developments over the last decades wind energy has seen a dramatic increase in both the number of installed wind turbines and the amount of power produced by each. Wind turbine design has seen major changes developed by wind
Hybrid Image Compression Based on SetPartitioning Embedded Block Coder and Residual Vector Quantization
"... A hybrid image coding scheme based on the setpartitioning embedded block coder (SPECK) and residual vector quantization (RVQ) is proposed for image compression. In which, the scaling and wavelet coefficients of an image are coded by using the original SPECK algorithm and the SPECK with RVQ, respect ..."
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A hybrid image coding scheme based on the setpartitioning embedded block coder (SPECK) and residual vector quantization (RVQ) is proposed for image compression. In which, the scaling and wavelet coefficients of an image are coded by using the original SPECK algorithm and the SPECK with RVQ
Pyramidal implementation of the Lucas Kanade feature tracker
 Intel Corporation, Microprocessor Research Labs
, 2000
"... grayscale value of the two images are the location x = [x y] T, where x and y are the two pixel coordinates of a generic image point x. The image I will sometimes be referenced as the first image, and the image J as the second image. For practical issues, the images I and J are discret function (or ..."
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Cited by 304 (0 self)
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ωx and ωy two integers. We define the image velocity d as being the vector that minimizes the residual function ɛ defined as follows: ɛ(d) = ɛ(dx, dy) = ux+ωx ∑ uy+ωy x=ux−ωx y=uy−ωy (I(x, y) − J(x + dx, y + dy)) 2. (1)
Results 1  10
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443,504