In this paper, we investigate the quadratic approximation methods. After studying the basic idea of simplex methods, we construct several new search directions by combining the local information progressively obtained...
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In this paper, we investigate the quadratic approximation methods. After studying the basic idea of simplex methods, we construct several new search directions by combining the local information progressively obtained during the iterates of the algorithm to form new subspaces. And the quadratic model is solved in the new subspaces. The motivation is to use the information disclosed by the former steps to construct more promising directions. For most tested problems, the number of functions evaluations have been reduced obviously through our algorithms.
For large sparse system of linear equations with a non-Hermitian positive definite coefficient matrix, we review the recently developed Hermitian/skew-Hermitian splitting (HSS) iteration, normal/skew-Hermitian splitti...
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In this paper,based on the multi-symplecticity of concatenating symplectic Runge-Kutta-Nystrom(SRKN)methods and symplectic Runge-Kutta-type methods for numerically solving Hamiltonian PDEs,explicit multi-symplectic sc...
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In this paper,based on the multi-symplecticity of concatenating symplectic Runge-Kutta-Nystrom(SRKN)methods and symplectic Runge-Kutta-type methods for numerically solving Hamiltonian PDEs,explicit multi-symplectic schemes are constructed and investigated,where the nonlinear wave equation is taken as a model *** comparisons are made to illustrate the effectiveness of our newly derived explicit multi-symplectic integrators.
Valuation of image coding relies on not only the efficiency of the coding, but also the quality of the coded image. We present a new objective quality assessment metric for image coding based on matching pursuit. Firs...
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Valuation of image coding relies on not only the efficiency of the coding, but also the quality of the coded image. We present a new objective quality assessment metric for image coding based on matching pursuit. First of all, we get the characteristics of the most important structure by projecting the reference image onto the base functions from a dictionary using matching pursuit. Secondly, we process the reference image and gain the structure information of the images in the order of importance, projecting the images onto the structural characteristics. Finally the objective quality score is given by comparing the differences of structure information between the reference and coded images. Experimental results show that the proposed approach is well consistent with the subjective quality score.
Principal Component Analysis (PCA) has been proven to be an efficient method in dimensionality reduction, feature extraction and pattern *** Principal Component Analysis (KPCA) can be considered as a natural nonlinear...
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Principal Component Analysis (PCA) has been proven to be an efficient method in dimensionality reduction, feature extraction and pattern *** Principal Component Analysis (KPCA) can be considered as a natural nonlinear generalization of PCA, which performs linear PCA in a high dimensional space implicitly by using kernel ***, both conventional PCA and KPCA suffer from the deficiency of being sensitive to *** robust KPCA has to eigen-decompose the Gram matrix directly in each step and is much more computationally infeasible due to the large size of the matrix when the number of training samples is *** extending existing robust PCA algorithm using kernel methods, we present a novel robust adaptive algorithm for calculating the kernel principal *** proposed method not only preserves the characteristic of capturing underlying nonlinear structure of KPCA but also is robust against outliers by restraining the effect of outlying *** with existing robust KPCA methods, our method is performed without having to store the kernel matrix, which can reduce significantly the storage *** addition, our method shows the potential of expansibility to the incremental learning *** results on synthetic data indicate that our improved algorithm is effective and promising.
In this paper Mei symmetry is introduced for a nonconservative system. The necessary and sufficient condition for a Mei symmetry to be also a Lie symmetry is derived. It is proved that the Mei symmetry leads to a non-...
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In this paper Mei symmetry is introduced for a nonconservative system. The necessary and sufficient condition for a Mei symmetry to be also a Lie symmetry is derived. It is proved that the Mei symmetry leads to a non-Noether conservative quantity via a Lie symmetry, and deduces a Lutzky conservative quantity via a Lie point symmetry.
keyword auto-extracting is focused by researchers on information retrieval, data mining, chance discovery and others application. In this paper, new algorithm, CCG(Cognition & Concept Graph, for text chance discov...
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keyword auto-extracting is focused by researchers on information retrieval, data mining, chance discovery and others application. In this paper, new algorithm, CCG(Cognition & Concept Graph, for text chance discovery is presented based on cognition with data depth as measurement. When the keywords in a document are treated as chances in the document, those keywords can be extracted by CGC automatically. In CGC, concepts of a document are represented as maximum connected sub graphs of the basic graph for the document and the cognition of reader/author on a term is weighted with data depth. The correlation for word and concept is defined and the formula for the correlation calculating is given. Experimental results show that keywords extracted by CCG can describe the document and author/reader's cognition much better than keywords extracted by others technologies such as frequency accumulating or key Graph.
This paper presents a discrete vaxiational principle and a method to build first-integrals for finite dimensional Lagrange-Maxwell mechanico-electrical systems with nonconservative forces and a dissipation function. T...
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This paper presents a discrete vaxiational principle and a method to build first-integrals for finite dimensional Lagrange-Maxwell mechanico-electrical systems with nonconservative forces and a dissipation function. The discrete variational principle and the corresponding Euler-Lagrange equations are derived from a discrete action associated to these systems. The first-integrals are obtained by introducing the infinitesimal transformation with respect to the generalized coordinates and electric quantities of the systems. This work also extends discrete Noether symmetries to mechanico-electrical dynamical systems. A practical example is presented to illustrate the results.
Inspired by the success of the projected Barzilai-Borwein (PBB) method for largescale box-constrained quadratic programming, we propose and analyze the monotone projected gradient methods in this paper. We show by exp...
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Inspired by the success of the projected Barzilai-Borwein (PBB) method for largescale box-constrained quadratic programming, we propose and analyze the monotone projected gradient methods in this paper. We show by experiments and analyses that for the new methods,it is generally a bad option to compute steplengths based on the negative gradients. Thus in our algorithms, some continuous or discontinuous projected gradients are used instead to compute the steplengths. Numerical experiments on a wide variety of test problems are presented, indicating that the new methods usually outperform the PBB method.
The (3+1)-dimensional Jimbo-Miwa (JM) equation is solved approximately by using the conformal invariant asymptotic expansion approach presented by Ruan. By solving the new (3+1)-dimensional integrable models, ...
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The (3+1)-dimensional Jimbo-Miwa (JM) equation is solved approximately by using the conformal invariant asymptotic expansion approach presented by Ruan. By solving the new (3+1)-dimensional integrable models, which are conformal invariant and possess Painlevé property, the approximate solutions are obtained for the JM equation, containing not only one-soliton solutions but also periodic solutions and multi-soliton solutions. Some approximate solutions happen to be exact and some approximate solutions can become exact by choosing relations between the parameters properly.
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