一类大线性映象的Lshikawa迭代过程

设K是一致光滑Banach空间X的非空界闭凸子集 ,T :K→K是强伪压缩映象 ,本文给出一个Ishikaw迭代序列强收敛到T的唯一不动点 ,并给出一个涉及m -增生映象T的非线性方程x +Tx =f的解的迭代逼近 ,其结果统一和扩展了近期相关结果     

谐波消去法中开关角的求解研究

本文介绍了两种基于谐波消去原理的非线性方程组的求解 ,并对已有的一种解法进行了修正和完善。     

软件过程RUP研究

本文介绍了Rational统一过程(RUP)的主要内容,包括开发阶段、迭代过程和核心工作流等,并简要评述了其在软件项目开发中的优越性及不足。     

基于二维泊松方程六阶紧致格式的多重网格方法

利用六阶紧致差分格式、结合多重网格V循环算法求解了二维泊松方程的Dirichlet边值问题，并用不同的松驰算子与四阶精度格式的多重网格方法进行了比较，计算结果表明，该方法在不明显增加计算量的前提下较四阶精度格式的多重网格方法具有更好的精确度和收敛阶，且ZLGS迭代不论对四阶精度还是对六阶精度格式的多重网格算法，都是一种较其他松弛算子更加有效的“光滑剂”。     

具有熵约束的多阶段均值—半绝对偏差投资组合优化

考虑投资者面临金融市场随机不确定性,将证券收益率视为随机变量,文章运用半绝对偏差度量风险,采用投资组合的熵度量分散化程度,同时,考虑交易成本和借款限制,提出多阶段投资组合模型。由于投资过程存在交易成本,上述模型为路径依赖性的动态优化问题。文章运用离散近似迭代法求解,并证明了该算法是线性收敛的。最后,通过实证研究得出熵的取值越大,投资组合的最终财富越小。     

Lanczos迭代有限终止的条件和性质

在Lanczos迭代过程中,如果出现 βj=0,求解特征值问题会变得十分有利,本文从理论上分析了出现有限终止 βj=0的充分条件,并从两个角度证明了有限终止的性质     

Banach空间中强增生和强伪压缩算子迭代序列的收敛定理

用新的不等式及新的逼近方法证明了Banach空间中强增生和强伪压缩算子Ishikawa和Mann迭代序列的若干收敛定理．我们的结果统一和推广了近期相应的一些结果，并完整地回答了Chidume提出的两个公开问题．     

Partitioned algorithms for maximum likelihood and other non-linear estimation

There are a variety of methods in the literature which seek to make iterative estimation algorithms more manageable by breaking the iterations into a greater number of simpler or faster steps. Those algorithms which deal at each step with a proper subset of the parameters are called in this paper partitioned algorithms. Partitioned algorithms in effect replace the original estimation problem with a series of problems of lower dimension. The purpose of the paper is to characterize some of the circumstances under which this process of dimension reduction leads to significant benefits.Four types of partitioned algorithms are distinguished: reduced objective function methods, nested (partial Gauss-Seidel) iterations, zigzag (full Gauss-Seidel) iterations, and leapfrog (non-simultaneous) iterations. Emphasis is given to Newton-type methods using analytic derivatives, but a nested EM algorithm is also given. Nested Newton methods are shown to be equivalent to applying to same Newton method to the reduced objective function, and are applied to separable regression and generalized linear models. Nesting is shown generally to improve the convergence of Newton-type methods, both by improving the quadratic approximation to the log-likelihood and by improving the accuracy with which the observed information matrix can be approximated. Nesting is recommended whenever a subset of parameters is relatively easily estimated. The zigzag method is shown to produce a stable but generally slow iteration; it is fast and recommended when the parameter subsets have approximately uncorrelated estimates. The leapfrog iteration has less guaranteed properties in general, but is similar to nesting and zigzagging when the parameter subsets are orthogonal.     

大规模并行处理互连结构的分析

大量的数据处理对计算机系统的速度要求越来越高，单机系统已无法满足这种要求。并行处理是提高计算机系统速度的有效途径，采用何种拓扑结构是关健问题之一。本文针对矩阵运算处理，就目前流行的三种拓扑结构进行了效率对比分析，并给出了我们的观点。     

分类问题中的特征选取

针对分类问题提出了一种特征选择的新算法.算法在初始化时首先任意产生几个特征集,然后被迭代多次执行,在每一次迭代过程中,用性能评估函数对所有的特征集进行评价,按照评价结果选取当前性能最优的特征集,其它所有的特征集朝着类似当前最优特征集的方向发生变化,直至完成预定的次数为止.