General Information

In this paper, we propose and analyze an SQP-type method for solving linearly constrained convex minimization problems where the objective functions are too complex to be evaluated exactly. Some basic results for global convergence and local superlinear convergence are obtained according to the properties of the approximation sequence. We illustrate the applicability of our approach by proposing a new method for solving two-stage stochastic programs with fixed recourse.     

动态随机弹性在期权定价中的应用

给出动态随机弹性的概念及运算性质,讨论了动态随机弹性在期权定价模型中的应用.主要结果有:(1)在波动率为常数时,期权价格对的弹性,得到了动态随机弹性服从运动,并给出了相应的经济解释;(2)由于波动率一般不是常数,也是随机过程,因此本文进一步研究了期权价格对波动率的弹性,就股票价格的波动情况给出了数学描述和金融意义上的解释.     

逆序数的应用

本文通过“1 4-1 5”智力玩具和最优戴断切割问题两个实例 ,讨论了逆序数在解决实际问题中的应用     

关于Erdos-Jacobson-Lehel 问题的门槛

设σ(k,n）表示最小的正整数m，使得对于每个n项正可图序列，当其项和至少为m时，有一个实现含k 1个顶点的团作为其子图。Erdos等人猜想：σ(k,n）＝（k-1)(2n-k) 2.Li等人证明了这个猜想对于k≥5，n≥（^k2））＋3是对的，并且提出如下问题：确定最小的整数N（k），使得这个猜想对于n≥N(k）成立。他们同时指出：当k≥5时，[5k-1/2]≤N(k）≤（^k2) 3.Mubayi猜想：当k≥5时，N(k）＝[5k-1/2]。在本文中，我们证明了N（8）＝20，即Mubayi猜想对于k=8是成立的。     

Runge–Kutta Methods for Itô Stochastic Differential Equations with Scalar Noise

A general class of stochastic Runge–Kutta methods for Itô stochastic differential equation systems w.r.t. a one-dimensional Wiener process is introduced. The colored rooted tree analysis is applied to derive conditions for the coefficients of the stochastic Runge–Kutta method assuring convergence in the weak sense with a prescribed order. Some coefficients for new stochastic Runge–Kutta schemes of order two are calculated explicitly and a simulation study reveals their good performance.     

弱混强平稳序列随机容量、随即秩次序统计量的极限分布律

设W(1n)W2(n)…W(nn)是严平稳随即序列{Xn,n 1}的前几个变量的顺序统计量,Nn Mn是两正整值r.v,n 1.在较弱一些的混合条件下,本文给出了W(NMn)n标准化后的极限分布,从而使一些提法更趋一般化,主要结果推进了Rao(1984)和张国胜(1993)的工作.由于混合条件的减弱,使得对m-相关和正态平稳过程的具体应用讨论更加充分.     

线性递归序列模q

设{u_k}_k≥0为一个线性递归序列．序列{u_k(mod q)}_(k≥0)是周期的,很多人都对其周期有过研究．本文应用二次数域中理想的理论,较完全地刻面了二次线性递归序列模q的周期长度,所获结果加强并推广了Engstrom及Wall的结论．     

Second order Runge–Kutta methods for Stratonovich stochastic differential equations

The weak approximation of the solution of a system of Stratonovich stochastic differential equations with a m–dimensional Wiener process is studied. Therefore, a new class of stochastic Runge–Kutta methods is introduced. As the main novelty, the number of stages does not depend on the dimension m of the driving Wiener process which reduces the computational effort significantly. The colored rooted tree analysis due to the author is applied to determine order conditions for the new stochastic Runge–Kutta methods assuring convergence with order two in the weak sense. Further, some coefficients for second order stochastic Runge–Kutta schemes are calculated explicitly. AMS subject classification (2000)  65C30, 65L06, 60H35, 60H10     

An improved algorithm for computing topological entropy

A new algorithm is presented for computing the topological entropy of a unimodal map of the interval. The accuracy of the algorithm is discussed and some graphs of the topological entropy which are obtained using the algorithm are displayed.