解约束优化问题的QP-free可行域方法

本文利用一个新的分片线性NCP函数提出一个新的可行的QP-free方法解非线性不等式约束优化问题.不同于其他的QP-free方法,这个方法只考虑在工作集中的约束函数,工作集是积极集的一个估计,因此子问题的维数不是满秩的.这个方法可行的并且不需假定严格互补条件、聚点的孤立性得到算法的全局收敛性,并且积极约束函数的梯度不要求线性独立的,其中由拟牛顿法得到的子矩阵不需要求一致正定性.     

反向驱动的异质结有机薄膜电致发光

成功制备了结构为ITO／PDDOPV／PPQ／Al的异质结聚合物发光二极管。该器件在正反向偏压下均可发光。在正向偏压下的光发射主要来自PDDOPV，但在反向偏压下的光发射则包括来自PPQ的蓝光发射和PDDOPV的黄光发射。蓝光强度与黄光强度的比值随着反向偏压的增加而增加，当反向直流电压分别为22V、24V、26V、28V时，其电致发光光谱中PPQ与PDDOPV的峰高比IPPQ／IPDDOPV分别为1．092、1．329、1．605、2．046。换句话说，该器件的发光颜色是压控可调的，这对实现彩色显示是极为有利的。分析了在反向偏压下的发光机理以及IPPQ／IPDDOPV受电压控制的原因。     

Generalization of a sharp Hölder's inequality and its application

In this paper, we generalize Hu Ke's sharpness of Hölder's inequality. As application, the obtained result is used to improve the well-known Opial-Olech inequality.     

Some inequalities for characteristic functions. II

Recently we established Matysiak and Szablowski's conjecture [V. Matysiak, P.J. Szablowski, Theory Probab. Appl. 45 (2001) 711-713] about a lower bound of real-valued characteristic functions. In this paper, applying an alternative approach we are able to give explicitly the ranges of argument for which the obtained inequalities hold true for general characteristic functions.     

关于Landau不等式

本文给出Landau不等式的一种改进。     

变时滞Hopfield神经网络的全局吸引性和全局指数稳定性

对一类具时滞的Hopfeild型神经网络模型,在非线性神经元激励函数只要求满足Lipschitz连续的条件下,利用推广的Halanay时延微分析不等式、Dini导数以及泛函微分析技术,给出了这类模型的平衡点全局指数稳定性和全局吸引性的充分条件,这些条件易于检验,且改进和推广了前人的结论.此外,此文给出了研究神经网络模型的全局吸引性的微分不等式比较方法.     

Lower bounds for transition probabilities on graphs

The paper presents two results. The first one provides separate conditions for the upper and lower estimates of the distribution of the time of exit from balls of a random walk on a weighted graph. The main result of the paper is that the lower estimate follows from the elliptic Harnack inequality. The second result is an off-diagonal lower bound for the transition probability of the random walk.     

带有奇异项的临界增长p-Laplace方程的非平凡解

本文研究了一种带有奇异项的临界增长p-Laplace方程在N维空间中的有界集上非平凡解的问题,利用山路引理和集中紧性原理,得出方程在非线性项满足一定条件下有非平凡解的结果.     

Concentration inequalities for random fields via coupling

We present a new and simple approach to concentration inequalities in the context of dependent random processes and random fields. Our method is based on coupling and does not use information inequalities. In case one has a uniform control on the coupling, one obtains exponential concentration inequalities. If such a uniform control is no more possible, then one obtains polynomial or stretched-exponential concentration inequalities. Our abstract results apply to Gibbs random fields, both at high and low temperatures and in particular to the low-temperature Ising model which is a concrete example of non-uniformity of the coupling.      

关于两类重要微分不等式

本文研究了两类重要微分不等式有界解的性质。引入了赋范线性空间(f,‖‖M),利用比较定理和黎卡提方程解的性质,给出了有界解的上界估计式,推广和改进了文[1,2]中的有关结果.