LIMITED MEMORY BFGS METHOD FOR NONLINEAR MONOTONE EQUATIONS

In this paper, we propose an algorithm for solving nonlinear monotone equations by combining the limited memory BFGS method （L-BFGS） with a projection method. We show that the method is globally convergent if the equation involves a Lipschitz continuous monotone function. We also present some preliminary numerical results.     

Banach空间中微分方程的单调迭代技巧与上、下解方法

本文研究了 Banach 空间 E 中一阶微分方程的初值问题,建立了两条定理,即文中的定理2.1与定理3.1。定理2.1给出了一个比较结果。定理3.1讨论了如何用单调技巧构造单调一致收敛到方程的最小与最大解序列。参考文献[1]中的结果是本定理的一个特例.     

连续时间马尔可夫决策过程的折扣模型

本文考虑的是转移速率族任意且费用率函数可能无界的连续时间马尔可夫决策过程的折扣模型．放弃了传统的要求相应于每个策略的　Ｑ　－过程唯一等条件,而首次考虑相应每个策略的　Ｑ　－过程不一定唯一,　转移速率族也不一定保守,　费用率函数可能无界,　且允许行动空间非空任意的情形．　本文首次用"α－折扣费用最优不等式"更新了传统的α－折扣费用最优方程,并用"最优不等式"和新的方法,不仅证明了传统的主要结果即最优平稳策略的存在性,　而且还进一步探讨了（　∈＞０　　）－最优平稳策略,具有单调性质的最优平稳策略,　以及（∈≥０）　－最优决策过程的存在性,　得到了一些有意义的新结果．　最后,　提供了一个迁移率受控的生灭系统例子,　它满足本文的所有条件,　而传统的假设（见文献［１－１４］）均不成立．     

The monotone convergence theorem for multidimensional abstract Kurzweil vector integrals

We prove two versions of the Monotone Convergence Theorem for the vector integral of Kurzweil, , where R is a compact interval of , and f are functions with values on L(Z,W) and Z respectively, and Z and W are monotone ordered normed spaces. Analogous results can be obtained for the Kurzweil vector integral, , as well as to unbounded intervals R.     

On the choice of the initial value of a quadratic spline in positive interpolation

Any quadratic spline with coinciding interpolating points and breakpoints can be uniquely determined by one initial condition. We examine how to choose the initial value in positive interpolation by using a norm of the spline. Monotone interpolation is also considered. The results are illuminated by numerical examples and by an application.     

Predictor-Corrector Smoothing Methods for Monotone LCP

In this paper, we analyze the global and local convergence properties of two predictor-corrector smoothing methods, which are based on the framework of the method in [1], for monotone linear complementarity problems (LCPs). The difference between the algorithm in [1] and our algorithms is that the neighborhood of smoothing central path in our paper is different to that in [1]. In addition, the difference between Algorithm 2.1 and the algorithm in [1] exists in the calculation of the predictor step. Comparing with the results in [1],the global and local convergence of the two methods can be obtained under very mild conditions. The global convergence of the two methods do not need the boundness of the inverse of the Jacobian. The superlinear convergence of Algorithm 2.1‘ is obtained under the assumption of nonsingularity of generalized Jacobian of Φ(x,y) at the limit point and Algorithm 2.1 obtains superlinear convergence under the assumption of strict complementarity at the solution. The efficiency of the two methods is tested by numerical experiments.