多步块格式求解微分-代数方程

多步块格式是一类新的一般线性方法,在求解微分-代数方程的过程中不会出现精度降低现象。研究了多步块格式的构造方法,精度条件及具有Runge-Kutta稳定性的多步块格式,多步块格式具有刚性精确的优点,且级精度与格式精度相等。构造了具有Runge-Kutta稳定性的2级和3级多步块格式,具有L-稳定性。数值算例证实多步块格式在求解微分-代数方程不会精度降低。

Abstract：

The multistep block methods are a new class of general linear methods,and the methods solve the differential-algebraic equations with no order reduction.The construction of the multistep block methods was described,and order condition and stability was studied.The multistep block methods with Runge-Kutta stability were also constructed.The multistep block methods have many nice properties,for example,stiffly accurate,and stage order is equal to order of method.At last the methods of 2-stage and 3-stage with Runge-Kutta stability were constructed,and they have the property of L-stability.The numerical example shows that the multistep block methods can solve the differential-algebraic equations without the order reduction.     

A Class of Parallel Implicit Runge-Kutta Formulas

A class of parallel implicit Runge-Kutta formulas is constructed for multiprocessor system. A family of parallel implicit two-stage fourth order Runge-Kutta formulas is given. For these formulas, the convergence is proved and the stability analysis is given. The numerical examples demonstrate that these formulas can solve an extensive class of initial value problems for the ordinary differential equations.     

A Class of Explicit Parallel Multistep Runge-Kutta Methods

In this paper, a rather general class of explicit parallel multistep Runge-Kutta methods is constructed for solving initial value problem of ordinary differential equations. Also, the corresponding convergence and stability are analysed. Several parallel computational formulae are given. The numerical experiments, including accuracy, speedup, and efficiency tests show that the methods are efficient.     

Almost sure and moment exponential stability of predictor-corrector methods for stochastic differential equations

This paper deals with almost sure and moment exponential stability of a class of predictor-corrector methods applied to the stochastic differential equations of Ito-type.Stability criteria for this type of methods are derived.The methods are shown to maintain almost sure and moment exponential stability for all sufficiently small timesteps under appropriate conditions.A numerical experiment further testifies these theoretical results.     

Adjustable entropy function method for support vector machine

Based on KKT complementary condition in optimization theory, an unconstrained non-differential optimization model for support vector machine is proposed. An adjustable entropy function method is given to deal with the proposed optimization problem and the Newton algorithm is used to figure out the optimal solution. The proposed method can find an optimal solution with a relatively small parameter p, which avoids the numerical overflow in the traditional entropy function methods. It is a new approach to solve support vector machine. The theoretical analysis and experimental results illustrate the feasibility and efficiency of the proposed algorithm.     

A note on stability of the split-step backward Euler method for linear stochastic delay integro-differential equations

In the literature(Tan and Wang,2010),Tan and Wang investigated the convergence of the split-step backward Euler(SSBE) method for linear stochastic delay integro-differential equations (SDIDEs) and proved the mean-square stability of SSBE method under some condition.Unfortunately, the main result of stability derived by the condition is somewhat restrictive to be applied for practical application.This paper improves the corresponding results.The authors not only prove the mean-square stability of the numerical method but also prove the general mean-square stability of the numerical method.Furthermore,an example is given to illustrate the theory.     

Receding horizon H∞ control for discrete-time Markovian jump linear systems

Receding horizon H∞ control scheme which can deal with both the H∞ disturbance attenuation and mean square stability is proposed for a class of discrete-time Markovian jump linear systems when minimizing a given quadratic performance criteria. First, a control law is established for jump systems based on pontryagin’s minimum principle and it can be constructed through numerical solution of iterative equations. The aim of this control strategy is to obtain an optimal control which can minimize the cost function under the worst disturbance at every sampling time. Due to the difficulty of the assurance of stability, then the above mentioned approach is improved by determining terminal weighting matrix which satisfies cost monotonicity condition. The control move which is calculated by using this type of terminal weighting matrix as boundary condition naturally guarantees the mean square stability of the closed-loop system. A sufficient condition for the existence of the terminal weighting matrix is presented in linear matrix inequality (LMI) form which can be solved efficiently by available software toolbox. Finally, a numerical example is given to illustrate the feasibility and effectiveness of the proposed method.     

BFGS quasi-Newton location algorithm using TDOAs and GROAs

With the emergence of location-based applications in various fields,the higher accuracy of positioning is demanded.By utilizing the time differences of arrival(TDOAs) and gain ratios of arrival(GROAs),an efficient algorithm for estimating the position is proposed,which exploits the Broyden-Fletcher-Goldfarb-Shanno(BFGS) quasi-Newton method to solve nonlinear equations at the source location under the additive measurement error.Although the accuracy of two-step weighted-least-square(WLS) method based on TDOAs and GROAs is very high,this method has a high computational complexity.While the proposed approach can achieve the same accuracy and bias with the lower computational complexity when the signal-to-noise ratio(SNR) is high,especially it can achieve better accuracy and smaller bias at a lower SNR.The proposed algorithm can be applied to the actual environment due to its real-time property and good robust performance.Simulation results show that with a good initial guess to begin with,the proposed estimator converges to the true solution and achieves the Cramer-Rao lower bound(CRLB) accuracy for both near-field and far-field sources.     

Blind reconstruction of convolutional code based on segmented Walsh-Hadamard transform

Walsh-Hadamard transform （WriT） can solve linear error equations on Field F2, and the method can be used to recover the parameters of convolutional code. However, solving the equations with many unknowns needs enormous computer memory which limits the application of WriT. In order to solve this problem, a method based on segmented WriT is proposed in this paper. The coefficient vector of high dimension is reshaped and two vectors of lower dimension are obtained. Then the WriT is operated and the requirement for computer memory is much reduced. The code rate and the constraint length of convolutional code are detected from the Walsh spectrum. And the check vector is recovered from the peak position. The validity of the method is verified by the simulation result, and the performance is proved to be optimal.     

INTERPOLATED FINITE ELEMENT METHODS FOR SECOND ORDER HYPERBOLIC EQUATIONS AND THEIR GLOBAL SUPERCONVERGENCE

We develop the interpolated finite element method to solve second-order hy-perbolic equations. The standard linear finite element solution is used to generate a newsolution by quadratic interpolation over adjacent elements. We prove that this interpo-lated finite element solution has superconvergence. This method can easily be applied togenerating more accurate gradient either locally or globally, depending on the applications.This method is also completely vectorizable and parallelizable to take the advantages ofmodern computer structures. Several numerical examples are presented to confirm ourtheoretical analysis.     

一类组合两步连续RK-Rosenbrock方法

对于一个大的刚性延迟微分方程系统,除了延迟分量给予系统影响外,还常常会出现系统的解分量有的变化很快,而有的变化很慢的情况。此时,可以把大的刚性延迟微分方程系统分解成为两个耦合的子系统,一个是描述系统快变部分的刚性延迟子系统,另一个是描述系统慢变部分的非刚性延迟子系统。对于分解的刚性延迟微分方程大系统,构造了一类用于求解刚性延迟微分方程的组合两步连续RK-Rosenbrock方法,讨论了方法的构造,方法的阶条件,证明了方法的收敛性,分析了方法的稳定性,数值试验表明方法是有效的。     

多延迟中立型方程Runge-Kutta方法的NGPG-稳定性

讨论了多延迟中立型微分方程解析解及由隐式Runge-Kutta方法应用于方程得到的数值解的稳定性.给出了方程解析解渐近稳定的一个充分条件.在此基础上将隐式Runge-Kutta方法应用于方程,证明了数值解NGPG-稳定的充分必要条件为隐式Runge-Kutta方法是A-稳定的.     

奇异延迟微分方程数值仿真的两步连续Runge-Kutta方法

提出在当前的积分步内计算级值时，放松延迟对计算的影响的思想，构造了一类奇异延迟微分方程数值仿真的两步连续Runge-Kutta方法(TSCRK)，讨论了方法的构造，方法阶条件，证明了方法的收敛性，分析了方法的稳定性。这类方法具有优良的稳定性和较高的阶级，并保持了显式的求解过程。数值试验表明方法是有效的。     

一类并行隐式Runge-Kutta公式

本文针对多处理机系统构造了一类并行隐式Runge-Kutta公式,对2级Runge-Kutta公式给出具有4阶精度的公式族,并证明了它们的收敛性,进行稳定性分析。数值例子表明,该公式可以有效地数值求解较广泛类型的常微分方程初值问题。     

用于实时仿真的高阶Runge—Kutta方法

本文简要分析低阶实时仿真算法，构造实时的高阶（ｓ＝６，ｐ＝５）实时Ｒｕｎｇｅ—Ｋｕｔｔａ方法，分析了该方法的收敛阶条件和稳定性，并具体给出了三组实时仿真算法公式，数值试验结果表明，构造的实时高阶Ｒｕｎｇｅ—Ｋｕｔｔａ方法是可行的、有效的。     

一类并行隐式Runge-Kutta方法的A稳定性分析

本文针对多处理机系统构造了一类并行隐式Runge—Kutta方法,给出了一个具有三阶精度的并行二级Runge—Kutta公式,并证明了该计算公式具有A稳定性,数值结果表明该计算公式对求解刚性常微分方程是有效的。     

多步隐式Runge—Kutta方法的稳定性分析

本文对多步隐式Runge—Kutta方法进行数值稳定性分析。给出了广义压缩性及弱广义压缩性的概念,并导出了多步隐式Runge—Kutta方法为广义压缩的代数条件,最后还给出了数值例子。