Orthogonal projection methods have been widely used to solve linear systems. Little attention has been given to oblique projection methods, but the class of oblique projection methods is particularly attractive for la...Orthogonal projection methods have been widely used to solve linear systems. Little attention has been given to oblique projection methods, but the class of oblique projection methods is particularly attractive for large nonsymmetric systems. The purpose of this paper is to consider a criterion for judging whether a given appro ximation is acceptable and present an algorithm which computes an approximate solution to the linear systems Ax=b such that the normwise backward error meets some optimality condition.展开更多
This paper extendes the results by E.M. Kasenally([7]) on a Generalized Minimum Backward Error Algorithm for nonsymmetric linear systems Ax = b to the problem in which pertubations are simultaneously permitted on A an...This paper extendes the results by E.M. Kasenally([7]) on a Generalized Minimum Backward Error Algorithm for nonsymmetric linear systems Ax = b to the problem in which pertubations are simultaneously permitted on A and b. The approach adopted by Kasenally has been to view the approximate solution as the exact solution to a perturbed linear system in which changes are permitted to the matrix A only. The new method introduced in this paper is a Krylov subspace iterative method which minimizes the norm of the perturbations to both the observation vector b and the data matrix A and has better performance than the Kasenally's method and the restarted GMRES method([12]). The minimization problem amounts to computing the smallest singular value and the corresponding right singular vector of a low-order upper-Hessenberg matrix. Theoratical properties of the algorithm are discussed and practical implementation issues are considered. The numerical examples are also given.展开更多
In this note, we consider the backward errors for more general inverse eigenvalue problems by extending Sun's approach. The optimal backward errors are defined for diagonal-ization matrix inverse eigenvalue proble...In this note, we consider the backward errors for more general inverse eigenvalue problems by extending Sun's approach. The optimal backward errors are defined for diagonal-ization matrix inverse eigenvalue problem with respect to an approximate solution, and the upper and lower bounds are derived for the optimal backward errors. The results may be useful for testing the stability of practical algorithms.展开更多
The problem of adapting backward error recovery to parallel real time systems is discussed in this paper. Because of error propagation among different cooperating processes, an error occurring in one process may influ...The problem of adapting backward error recovery to parallel real time systems is discussed in this paper. Because of error propagation among different cooperating processes, an error occurring in one process may influence some important outputs in other processes. Therefore, a local output has to be delayed until its validity is confirmed globally. Since backward error recovery adopts redundancy of computing time instead of processing equipment, the variation of the actual execution time of a cooperating process may be very large if it works in an unreliable environment. These problems are the primary obstacles to be removed. Previous studies focus their attentions on how to eliminate domino-effect dynamically. But backward error recovery cannot be applied directly in parallel real time systems even under the condition that no domino-effect exists. How to reduce output delays efficiently if no domino-effect remains? How to estimate this delay time? How to calculate the actual execution time of every process and how to schedule these processes under an unstable condition? These problems were omitted in literature unfortunately. The interest of this paper is to provide satisfactory solutions to these problems to make it possible to adopt backward error recovery efficiently in parallel real time systems.展开更多
Presents a study that analyzed the symplecticness, stability and asymptotic of Runge-Kutta, partitioned Runge-Kutta, and Runge-Kutta-Nystr ? m methods applied to linear Hamiltonian systems. Numerical representation of...Presents a study that analyzed the symplecticness, stability and asymptotic of Runge-Kutta, partitioned Runge-Kutta, and Runge-Kutta-Nystr ? m methods applied to linear Hamiltonian systems. Numerical representation of the problem; Results in connection to P-stability; Details of the application of backward error analysis in the study.展开更多
In this paper, we propose a new second order numerical scheme for solving backward stochastic differential equations with jumps with the generator linearly depending on . And we theoretically prove that the conv...In this paper, we propose a new second order numerical scheme for solving backward stochastic differential equations with jumps with the generator linearly depending on . And we theoretically prove that the convergence rates of them are of second order for solving and of first order for solving and in norm.展开更多
In this paper, under weak conditions, we theoretically prove the second-order convergence rate of the Crank-Nicolson scheme for solving a kind of decoupled forward-backward stochastic differential equations.
In order to enhance the accuracy and reliability of wireless location under non-line-of-sight （NLOS） environments,a novel neural network （NN） location approach using the digital broadcasting signals is presented. ...In order to enhance the accuracy and reliability of wireless location under non-line-of-sight （NLOS） environments,a novel neural network （NN） location approach using the digital broadcasting signals is presented. By the learning ability of the NN and the closely approximate unknown function to any degree of desired accuracy,the input-output mapping relationship between coordinates and the measurement data of time of arrival （TOA） and time difference of arrival （TDOA） is established. A real-time learning algorithm based on the extended Kalman filter （EKF） is used to train the multilayer perceptron （MLP） network by treating the linkweights of a network as the states of the nonlinear dynamic system. Since the EKF-based learning algorithm approximately gives the minimum variance estimate of the linkweights,the convergence is improved in comparison with the backwards error propagation （BP） algorithm. Numerical results illustrate thatthe proposedalgorithmcanachieve enhanced accuracy,and the performance ofthe algorithmis betterthanthat of the BP-based NN algorithm and the least squares （LS） algorithm in the NLOS environments. Moreover,this location method does not depend on a particular distribution of the NLOS error and does not need line-of-sight （ LOS ） or NLOS identification.展开更多
文摘Orthogonal projection methods have been widely used to solve linear systems. Little attention has been given to oblique projection methods, but the class of oblique projection methods is particularly attractive for large nonsymmetric systems. The purpose of this paper is to consider a criterion for judging whether a given appro ximation is acceptable and present an algorithm which computes an approximate solution to the linear systems Ax=b such that the normwise backward error meets some optimality condition.
文摘This paper extendes the results by E.M. Kasenally([7]) on a Generalized Minimum Backward Error Algorithm for nonsymmetric linear systems Ax = b to the problem in which pertubations are simultaneously permitted on A and b. The approach adopted by Kasenally has been to view the approximate solution as the exact solution to a perturbed linear system in which changes are permitted to the matrix A only. The new method introduced in this paper is a Krylov subspace iterative method which minimizes the norm of the perturbations to both the observation vector b and the data matrix A and has better performance than the Kasenally's method and the restarted GMRES method([12]). The minimization problem amounts to computing the smallest singular value and the corresponding right singular vector of a low-order upper-Hessenberg matrix. Theoratical properties of the algorithm are discussed and practical implementation issues are considered. The numerical examples are also given.
文摘In this note, we consider the backward errors for more general inverse eigenvalue problems by extending Sun's approach. The optimal backward errors are defined for diagonal-ization matrix inverse eigenvalue problem with respect to an approximate solution, and the upper and lower bounds are derived for the optimal backward errors. The results may be useful for testing the stability of practical algorithms.
文摘The problem of adapting backward error recovery to parallel real time systems is discussed in this paper. Because of error propagation among different cooperating processes, an error occurring in one process may influence some important outputs in other processes. Therefore, a local output has to be delayed until its validity is confirmed globally. Since backward error recovery adopts redundancy of computing time instead of processing equipment, the variation of the actual execution time of a cooperating process may be very large if it works in an unreliable environment. These problems are the primary obstacles to be removed. Previous studies focus their attentions on how to eliminate domino-effect dynamically. But backward error recovery cannot be applied directly in parallel real time systems even under the condition that no domino-effect exists. How to reduce output delays efficiently if no domino-effect remains? How to estimate this delay time? How to calculate the actual execution time of every process and how to schedule these processes under an unstable condition? These problems were omitted in literature unfortunately. The interest of this paper is to provide satisfactory solutions to these problems to make it possible to adopt backward error recovery efficiently in parallel real time systems.
文摘Presents a study that analyzed the symplecticness, stability and asymptotic of Runge-Kutta, partitioned Runge-Kutta, and Runge-Kutta-Nystr ? m methods applied to linear Hamiltonian systems. Numerical representation of the problem; Results in connection to P-stability; Details of the application of backward error analysis in the study.
文摘In this paper, we propose a new second order numerical scheme for solving backward stochastic differential equations with jumps with the generator linearly depending on . And we theoretically prove that the convergence rates of them are of second order for solving and of first order for solving and in norm.
文摘In this paper, under weak conditions, we theoretically prove the second-order convergence rate of the Crank-Nicolson scheme for solving a kind of decoupled forward-backward stochastic differential equations.
基金The National High Technology Research and Development Program of China (863 Program) (No.2008AA01Z227)the Cultivatable Fund of the Key Scientific and Technical Innovation Project of Ministry of Education of China (No.706028)
文摘In order to enhance the accuracy and reliability of wireless location under non-line-of-sight （NLOS） environments,a novel neural network （NN） location approach using the digital broadcasting signals is presented. By the learning ability of the NN and the closely approximate unknown function to any degree of desired accuracy,the input-output mapping relationship between coordinates and the measurement data of time of arrival （TOA） and time difference of arrival （TDOA） is established. A real-time learning algorithm based on the extended Kalman filter （EKF） is used to train the multilayer perceptron （MLP） network by treating the linkweights of a network as the states of the nonlinear dynamic system. Since the EKF-based learning algorithm approximately gives the minimum variance estimate of the linkweights,the convergence is improved in comparison with the backwards error propagation （BP） algorithm. Numerical results illustrate thatthe proposedalgorithmcanachieve enhanced accuracy,and the performance ofthe algorithmis betterthanthat of the BP-based NN algorithm and the least squares （LS） algorithm in the NLOS environments. Moreover,this location method does not depend on a particular distribution of the NLOS error and does not need line-of-sight （ LOS ） or NLOS identification.
