高考预效度的混合估计

用传统的预测效度估计法估计高考预测效度，其估计值往往偏低。为此，本文应用回归分析中混合估计理论提出了高考预测效度的混合估计。这种估计是预测效度的无偏估计，并解决了预测效度估计值偏低的问题。     

Differential Conditions for Constrained Nonlinear Programming via Pareto Optimization

We deal with the differential conditions for local optimality. The conditions that we derive for inequality constrained problems do not require constraint qualifications and are the broadest conditions based on only first-order and second-order derivatives. A similar result is proved for equality constrained problems, although the necessary conditions require the regularity of the equality constraints.     

一类非牛顿渗流方程爆破界的估计

本文利用拟线性常微分方程解的非存在性定理得到了一类拟线性反应扩散方程(非牛顿渗流方程)爆破界的估计,从而推广了半线性反应扩散方程(牛顿渗流方程)相应结果.     

Error estimate for the approximation of nonlinear conservation laws on bounded domains by the finite volume method

In this paper we derive a priori and a posteriori error estimates for cell centered finite volume approximations of nonlinear conservation laws on polygonal bounded domains. Numerical experiments show the applicability of the a posteriori result for the derivation of local adaptive solution strategies.

     

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.     

Bayes估计的进一步研究及其Pitman最优性

对线性模型参数,讨论了Bayes估计的Pitman最优性,将已有结果进行了改进,去掉了附加条件,证明了在Pitman准则下,Bayes估计一致优于最小二乘估计(LSE),在此基础上,提出了一种基于先验信息的方差分量估计,通过和基于LSE的方差分量估计作比较,证明了新估计是无偏估计且有更小的均方误差．最后,证明了在Pitman准则下生长曲线模型参数的Bayes估计优于最佳线性无偏估计．     

Analysis of Static Simulated Annealing Algorithms

Generalized hill climbing (GHC) algorithms provide a framework for modeling local search algorithms to address intractable discrete optimization problems. This paper introduces a measure for determining the expected number of iterations to visit a predetermined objective function level, given that an inferior objective function level has been reached in a finite number of iterations. A variation of simulated annealing (SA), termed static simulated annealing (S²A), is analyzed using this measure. S²A uses a fixed cooling schedule during the algorithm execution. Though S²A is probably nonconvergent, its finite-time performance can be assessed using the finite-time performance measure defined in this paper.     

The Cross-Entropy Method for Continuous Multi-Extremal Optimization

In recent years, the cross-entropy method has been successfully applied to a wide range of discrete optimization tasks. In this paper we consider the cross-entropy method in the context of continuous optimization. We demonstrate the effectiveness of the cross-entropy method for solving difficult continuous multi-extremal optimization problems, including those with non-linear constraints.      

椭圆型问题一类广义差分法的L~2模误差估计

1.引 言 广义差分法作为处理偏微分方程的离散技术,能够保持质量,动量,能量等物理量的守恒.广义差分法（有些文献称为box method[3];finite volume element method[4],[5],[6]）利用在对偶剖分体积单元积分原始方程,并将近似解限制于某一有限元空间而得到离散方程.因此,它在局部区域保持了原始方程的物理守恒性和其他重要特性.从而被广泛地应用于数值求解数学物理方程,特别是计算流体力学和热传导问题[11]. 对广义差分法的研究已有许多文献,专著[10]有详细的介绍.早期的工作主要考虑标准的重心对偶剖分.近年来Cai et,al[4],[5],[6],在某些假定下对较一般的对偶剖分给出了能量模误差估计,Huang and Xi[9]去掉了文献[6]中的这些限制.Chou,Li[8]和Li,     

Lagrangean heuristics combined with reoptimization for the 0-1 bidimensional knapsack problem

First, this paper deals with lagrangean heuristics for the 0-1 bidimensional knapsack problem. A projected subgradient algorithm is performed for solving a lagrangean dual of the problem, to improve the convergence of the classical subgradient algorithm. Secondly, a local search is introduced to improve the lower bound on the value of the biknapsack produced by lagrangean heuristics. Thirdly, a variable fixing phase is embedded in the process. Finally, the sequence of 0-1 one-dimensional knapsack instances obtained from the algorithm are solved by using reoptimization techniques in order to reduce the total computational time effort. Computational results are presented.