The Limit Behaviour of Imprecise Continuous-Time Markov Chains

We study the limit behaviour of a nonlinear differential equation whose solution is a superadditive generalisation of a stochastic matrix, prove convergence, and provide necessary and sufficient conditions for ergodicity. In the linear case, the solution of our differential equation is equal to the matrix exponential of an intensity matrix and can then be interpreted as the transition operator of a homogeneous continuous-time Markov chain. Similarly, in the generalised nonlinear case that we consider, the solution can be interpreted as the lower transition operator of a specific set of non-homogeneous continuous-time Markov chains, called an imprecise continuous-time Markov chain. In this context, our convergence result shows that for a fixed initial state, an imprecise continuous-time Markov chain always converges to a limiting distribution, and our ergodicity result provides a necessary and sufficient condition for this limiting distribution to be independent of the initial state.     

Convergence and quotient convergence of iterative methods for solving singular linear equations with index one

Singular systems with index one arise in many applications, such as Markov chain modelling. In this paper, we use the group inverse to characterize the convergence and quotient convergence properties of stationary iterative schemes for solving consistent singular linear systems when the index of the coefficient matrix equals one. We give necessary and sufficient conditions for the convergence of stationary iterative methods for such problems. Next we show that for the stationary iterative method, the convergence and the quotient convergence are equivalent.     

Convergence of a Penalty Method for Mathematical Programming with Complementarity Constraints

We adapt the convergence analysis of the smoothing (Ref. 1) and regularization (Ref. 2) methods to a penalty framework for mathematical programs with complementarity constraints (MPCC); we show that the penalty framework shares convergence properties similar to those of these methods. Moreover, we give sufficient conditions for a sequence generated by the penalty framework to be attracted to a B-stationary point of the MPCC.     

契比雪夫半迭代法的收敛性

其中,G是一个依赖于A的N阶矩阵,h是一个依赖于A和b的N阶向量。 方法(2)收敛的充要条件是迭代矩阵G的谱半径小于1。这个结论适合于任一线性定常迭代方法。但对非定常迭代方法,收敛性问题比较复杂,一般很难运用谱半径进行收敛性分析,Young给出的一个例子(见[1pp.298])便说明了这一点。 然而,对一种特殊的非定常迭代方法——契比雪夫半迭代法(下文简称CSI方法),却可以提供基于谱半径的收敛性条件。这正是本文的核心内容。     

Extended convergence regions for the AOR method

In this paper, we give sufficient conditions for the convergence of the (AOR) method, when the matrix A for Ax = b is a strictly diagonally dominant matrix. These results improve the conclusions obtained in the Theorem 4 [10].With the notion of generalized diagonal dominant matrix, we enlarge the convergence regions given in Theorem 9 [10], when A is a nonsingular H-matrix.In the last section we generalize theorem 6 of Robert [11] and we present some results which extend the convergence regions for the (AOR) method.     

A study on the convergence of homotopy analysis method

In this study, a reliable approach for convergence of the homotopy analysis method when applied to nonlinear problems is discussed. First, we present an alternative framework of the method which can be used simply and effectively to handle nonlinear problems. Then, mainly, we address the sufficient condition for convergence of the method. The convergence analysis is reliable enough to estimate the maximum absolute truncated error of the homotopy series solution. The analysis is illustrated by investigating the convergence results for some nonlinear differential equations. The study highlights the power of the method.     

A necessary and sufficient convergence condition of orthomin(k) methods for least squares problem with weight

A class of Orthomin-type methods for linear systems based on conjugate residuals is extended to a form suitable for solving a least squares problem with weight. In these algorithms a mapping matrix as preconditioner is brought into use. We also give a necessary and sufficient condition for the convergence of the algorithm. Furthermore, we also study the construction of the mapping matrix for which the necessary and sufficient condition holds.     

Convergence of matrix continued fractions

The aim of this work is to give some criteria on the convergence of matrix continued fractions. We begin by presenting some new results which generalize the links between the convergent elements of real continued fractions. Secondly, we give necessary and sufficient conditions for the convergence of continued fractions of matrix arguments. This paper will be completed by illustrating the theoretical results with some examples.     

正项矩阵级数的敛散性研究

本文研究了正项矩阵级数收敛的充要条件 ,从而把正项级数的收敛原理推广到了正项矩阵级数的情形 .     

Convergence of the parallel chaotic waveform relaxation method for stiff systems

In this paper, we establish several algorithms for parallel chaotic waveform relaxation methods for solving linear ordinary differential systems based on some given models. Under some different assumptions on the coefficient matrix A and its multisplittings we obtain corresponding sufficient conditions of convergence of the algorithms. Also a discussion on convergence speed comparison of synchronous and asynchronous algorithms is given.     

Positivity of linear transformations of mean-starshaped sequences

In this paper, we give the necessary and sufficient conditions for a linear transformation of a mean-starshaped sequence to be positive. Using this result, we obtain the necessary and sufficient conditions for a lower triangular matrix to preserve the mean-starshape of a sequence and we discuss some special cases of linear transformations. Our next result deals with the convergence of a sequence of mean-starshaped sequences to any given mean-starshaped sequence and the positivity of a linear operator on the set of mean-starshaped sequences.     

解线性互补问题的区间GAOR方法

1引言设M∈Rn×n,q∈Rn,则线性互补问题LCP(M,q)指的是寻找一个向量x∈Rn,使其满足下面的条件: x≥0 Mx+q≥0 xt(Mx+q)=0由于线性互补问题在工程物理、管理学、经济学、约束最优化等领域的应用非常广泛,所以该问题的研究一直倍受大家的关注,至今已有很多有效的算法.早在20世纪80年代     

An efficient monotone projected Barzilai–Borwein method for nonnegative matrix factorization

In this paper, we present an efficient method for nonnegative matrix factorization based on the alternating nonnegative least squares framework. Our approach adopts a monotone projected Barzilai–Borwein (MPBB) method as an essential subroutine where the step length is determined without line search. The Lipschitz constant of the gradient is exploited to accelerate convergence. Global convergence of the proposed MPBB method is established. Numerical results are reported to demonstrate the efficiency of our algorithm.     

Convergences of splitting iterative methods for symmetric indefinite linear systems

In this paper, we generalize the saddle point problem to general symmetric indefinite systems, we also present a kind of convergent splitting iterative methods for the symmetric indefinite systems. A special divergent splitting is introduced. The sufficient condition is discussed that the eigenvalues of the iteration matrix are real. The spectral radius of the iteration matrix is discussed in detail, the convergence theories of the splitting iterative methods for the symmetric indefinite systems are obtained. Finally, we present a preconditioner and discuss the eigenvalues of preconditioned matrix.     

Convergence rate for the Bayesian inversion theory

Recently, the metrics of Ky Fan and Prokhorov were introduced as a tool for studying convergence of regularization methods for stochastic ill-posed problems. In this work, we examine the Bayesian approach to linear inverse problems in this new framework. We consider the finite-dimensional case where the measurements are disturbed by an additive normal noise and the prior distribution is normal. A convergence rate result for the posterior distribution is obtained when the covariance matrices are proportional to the identity matrix. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Convergence Analysis of the Implicit Euler-discretization and Sufficient Conditions for Optimal Control Problems Subject to Index-one Differential-algebraic Equations

For optimal control problems subject to index-one differential-algebraic equations in semi-explicit form we discuss second order sufficient conditions in form of a coercivity condition taking into account the two-norm discrepancy. Furthermore we introduce a related Riccati-type and Legendre-Clebsch condition which are sufficient for the validity of the coercivity condition. Using the implicit Euler-discretization we approximate the optimal control problem and analyze the convergence of solutions of the local minimum principle for the discretized optimal control problem by applying the general convergence framework of Stetter, which requires the discretization method to be continuous, consistent, and stable.

     

Simplicial zero-point algorithms: A unifying description

In this paper, we consider the limiting paths of simplicial algorithms for finding a zero point. By rewriting the zero-point problem as a problem of finding a stationary point, the problem can be solved by generating a path of stationary points of the function restricted to an expanding convex, compact set. The limiting path of a simplicial algorithm to find a zero point is obtained by choosing this set in an appropriate way. Almost all simplicial algorithms fit in this framework. Using this framework, it can be shown very easily that Merrill's condition is sufficient for convergence of the algorithms.     

计算广义逆A_(T,S)~(2)的迭代法

1引言与引理 文【l}中Ben一Israel与Greville给出了计算矩阵A的Moore一penrose逆的一阶和p阶 迭代法，陈永林图推广了11]的结果，给出了类似的计算矩阵A的具有指定值域T与零 空间s的(z)一逆A级公的一阶迭代法 X* ，=X、 X0(I一AX*)，k=0，1，2，二 刘桂香:计算广义逆A钾:的迭代     

Unified theory of augmented Lagrangian methods for constrained global optimization

We classify in this paper different augmented Lagrangian functions into three unified classes. Based on two unified formulations, we construct, respectively, two convergent augmented Lagrangian methods that do not require the global solvability of the Lagrangian relaxation and whose global convergence properties do not require the boundedness of the multiplier sequence and any constraint qualification. In particular, when the sequence of iteration points does not converge, we give a sufficient and necessary condition for the convergence of the objective value of the iteration points. We further derive two multiplier algorithms which require the same convergence condition and possess the same properties as the proposed convergent augmented Lagrangian methods. The existence of a global saddle point is crucial to guarantee the success of a dual search. We generalize in the second half of this paper the existence theorems for a global saddle point in the literature under the framework of the unified classes of augmented Lagrangian functions.     

Comparison Theorems for Four Dimensional Regular Matrices

A four dimensional summability matrix is stronger than convergence in the Pringsheim sense if it sums a divergent double sequence. Presented in this paper are sufficient conditions on a four dimensional matrix transformation which ensure that the summability matrix is stronger than convergence in the Pringsheim sense. Also other sufficient conditions will be presented to ensure the failure of inclusion between two summability matrices.AMS Subject Classification (2000): Primary 40B05.