拟线性常微分方程组边值问题的奇摄动

本文研究拟线性常微分方程组边值问题x′=f(t,x,y,ε),εy″=g(t,x,y,ε)y′+h(t,x,y,ε), x(0,ε)=A(ε),y(0,ε)=B(ε),y(1,ε)=C(ε)的奇摄动.其中x,f,y,h,A,B和C均属于Rn和g是对角矩阵.在适当的假设下,利用对角化技巧和微分不等式理论获得了解的存在和它的按分量逐个一致有效的估计.     

矩阵方程AV+BW=VF的一种新的解析通解

本文给出了矩阵方程AV＋BW＝VF的一种新的解析通解。该通解中仅含有数值矩阵计算，这为应用计算机计算该通解提供了方便。     

极大-加线性系统的单局部优化及其在任务调度中的应用

研究极大-加线性系统的单局部优化.对于决策变量取实值和非负实值两种情形,证明单局部优化问题存在最优解,特别地,当决策变量取实值时,最优解是唯一的.通过分析具有仿射等式约束的极大-加线性方程组的可解性以及解的结构,给出寻求单局部最优解的多项式算法.同时,还将极大-加线性系统的单局部优化方法用于计算机操作系统任务调度方案设...     

Effect of inertial mass on a linear system driven by dichotomous noise and a periodic signal

A linear system driven by dichotomous noise and a periodic signal is investigated in the underdamped case. The exact expressions of output signal amplitude and signal-to-noise ratio (SNR) of the system are derived. By means of numerical calculation, the results indicate that (i) at some fixed noise intensities, the output signal amplitude with inertial mass exhibits the structure of a single peak and single valley, or even two peaks if the dichotomous noise is asymmetric; (ii) in the case of asymmetric dichotomous noise, the inertial mass can cause non-monotonic behaviour of the output signal amplitude with respect to noise intensity; (iii) the curve of SNR versus inertial mass displays a maximum in the case of asymmetric dichotomous noise, i.e., a resonance-like phenomenon, while it decreases monotonically in the case of symmetric dichotomous noise; (iv) if the noise is symmetric, the inertial mass can induce stochastic resonance in the system.     

Preconditioned iterative methods for solving weighted linear least squares problems

A class of preconditioned iterative methods,i.e.,preconditioned generalized accelerated overrelaxation(GAOR) methods,is proposed to solve linear systems based on a class of weighted linear least squares problems.The convergence and comparison results are obtained.The comparison results show that the convergence rate of the preconditioned iterative methods is better than that of the original methods.Furthermore,the effectiveness of the proposed methods is shown in the numerical experiment.     

New scheme of anticipating synchronization for arbitrary anticipation time and its application to long-term prediction of chaotic states

How to predict the dynamics of nonlinear chaotic systems is still a challenging subject with important real-life applications. The present paper deals with this important yet difficult problem via a new scheme of anticipating synchronization. A global, robust, analytical and delay-independent sufficient condition is obtained to guarantee the existence of anticipating synchronization manifold theoretically in the framework of the Krasovskii-Lyapunov theory. Different from ＇traditional techniques （or regimes）＇ proposed in the previous literature, the present scheme guarantees that the receiver system can synchronize with the future state of a transmitter system for an arbitrarily long anticipation time, which allows one to predict the dynamics of chaotic transmitter at any point of time if necessary. Also it is simple to implement in practice. A classical chaotic system is employed to demonstrate the application of the proposed scheme to the long-term prediction of chaotic states.[第一段]     

Stochastic resonance in linear system driven by multiplicative noise and additive quadratic noise

In this paper the stochastic resonance (SR) is studied in an overdamped linear system driven by multiplicative noise and additive quadratic noise. The exact expressions are obtained for the first two moments and the correlation function by using linear response and the properties of the dichotomous noise. SR phenomenon exhibits in the linear system. There are three different forms of SR: the bona fide SR, the conventional SR and SR in the broad sense. Moreover, the effect of the asymmetry of the multiplicative noise on the signal-to-noise ratio (SNR) is different from that of the additive noise and the effect of multiplicative noise and additive noise on SNR is different.     

ON CONTRACTION AND SEMI-CONTRACTION FACTORS OF GSOR METHOD FOR AUGMENTED LINEAR SYSTEMS

The generalized successive overrelaxation （GSOR） method was presented and studied by Bai, Parlett and Wang [Numer. Math. 102（2005）, pp.1-38] for solving the augmented system of linear equations, and the optimal iteration parameters and the corresponding optimal convergence factor were exactly obtained. In this paper, we further estimate the contraction and the semi-contraction factors of the GSOR method. The motivation of the study is that the convergence speed of an iteration method is actually decided by the contraction factor but not by the spectral radius in finite-step iteration computations. For the nonsingular augmented linear system, under some restrictions we obtain the contraction domain of the parameters involved, which guarantees that the contraction factor of the GSOR method is less than one. For the singular but consistent augmented linear system, we also obtain the semi-contraction domain of the parameters in a similar fashion. Finally, we use two numerical examples to verify the theoretical results and the effectiveness of the GSOR method.     

求解Hubbard线性系统的快速稳定算法

求解Hubbard线性系统是材料物理中DQMC/HQMC模型的核心问题之一,本文讨论了Hubbard矩阵的结构,然后给出了当能量参数U=O的情况下快速稳定求解Hubbard线性系统的算法.数值实验说明了方法的有效性.     

Modified iterative method for augmented system

The successive overrelaxation-like(SOR-like) method with the real parameters ω is considered for solving the augmented system. The new method is called the modified SOR-like(MSOR-like) method. The functional equation between the parameters and the eigenvalues of the iteration matrix of the MSOR-like method is given. Therefore, the necessary and sufficient condition for the convergence of the MSOR-like method is derived. The optimal iteration parameter ω of the MSOR-like method is derived. Finally, the proof of theorem and numerical computation based on a particular linear system are given, which clearly show that the MSOR-like method outperforms the SOR-like(Li, C. J., Li, B. J., and Evans, D. J. Optimum accelerated parameter for the GSOR method. Neural, Parallel Scientific Computations, 7(4), 453–462(1999)) and the modified symmetric SOR-like(MSSOR-like) methods(Wu, S. L., Huang, T. Z., and Zhao, X. L. A modified SSOR iterative method for augmented systems.