对F.& M.Riesz定理和圆盘代数的极大理想定理的推广

本文将F.& M.Riesz定理和圆盘代数的极大理想定理分别推广到向量测度和向量值函数。     

多维下标变量相关性的矢量识别法

本文利用矢量方程的概念,给出了两种多维下标变量相关性识别的有效方法:VGCD方法和值界收缩法.与现有的各种判定方法相比,本文方法具有更高的识别精度,且更易于并行实现,以提高识别效率.     

空气－水在壳侧垂直向上、向下横掠水平管束的两相流动特性

对空气－水混合物在壳侧垂直向上、向下横掠水平管束流动时的平均截面含气率及两相摩擦压降进行了实验研究。管束为正方形顺排，管间距离与管径之比１．２８．试验表明，质量流率对平均截面含气率及两相摩擦压降有显著的影响；截面含气率的测量值明显低于均相模型的预测值。提出了壳侧垂直向上、向下横掠水平管束时预测截面含气率及两相压降的新的关联式。     

位似与纯矩阵

本文研究了向量空间V的一个线性变换是位似的一些条件和一个n阶矩阵是纯矩阵的一些条件以及位似与纯矩阵之间的一些关系。     

线性代数中几个定理的新证法

对线性代数中关于矩阵秩的几个公式与特征多项式的性质定理给出了新的证明方法，用齐次线性方程组解空间的理论证明了矩阵秩的６个定理，利用矩阵和的行列式定理给出了矩阵Ａ的特征多项式系数及Ａ的主子式关系定理的新证法。     

共形平坦黎曼流形中具有平行第二基本形式的子流形

设Ｍ＾ｎ＋ｐ是ｎ＋ｐ维共形平坦黎曼流形，本文对具有平行第二基本形式的子流形作了讨论，将我们现有结论推广到了更一般的情形。     

New Bundle Methods for Solving Lagrangian Relaxation Dual Problems

Bundle methods have been used frequently to solve nonsmooth optimization problems. In these methods, subgradient directions from past iterations are accumulated in a bundle, and a trial direction is obtained by performing quadratic programming based on the information contained in the bundle. A line search is then performed along the trial direction, generating a serious step if the function value is improved by or a null step otherwise. Bundle methods have been used to maximize the nonsmooth dual function in Lagrangian relaxation for integer optimization problems, where the subgradients are obtained by minimizing the performance index of the relaxed problem. This paper improves bundle methods by making good use of near-minimum solutions that are obtained while solving the relaxed problem. The bundle information is thus enriched, leading to better search directions and less number of null steps. Furthermore, a simplified bundle method is developed, where a fuzzy rule is used to combine linearly directions from near-minimum solutions, replacing quadratic programming and line search. When the simplified bundle method is specialized to an important class of problems where the relaxed problem can be solved by using dynamic programming, fuzzy dynamic programming is developed to obtain efficiently near-optimal solutions and their weights for the linear combination. This method is then applied to job shop scheduling problems, leading to better performance than previously reported in the literature.