需求区间型运输问题的求解算法

为了便于建立与需求区间型运输问题有关的决策支持系统,本文给出了一个求解需求区间型运输问题的数值算法,证明了算法的理论依据,并举例说明算法的应用,该算法能求得问题的最优解,并具有易于编程实现、收敛性好等优点,大量数值实验表明该算法有较高的计算效率。

非线性控制系统的控制器型

M.Zeitz引入了非线性系统的控制器型,证明了由控制器型描述的非线性系统是可控的。本文进一步给出了化一般非线性系统为控制器型的条件及其状态变换的具体算法,从而解决了通过化控制器型证明非线性系统可控性的方法。

求解运输问题的一个算法

给出一个求解问题的数值算法 ,证明了算法的理论依据 ,并举例说明算法的应用。

求解排课表问题的一个启发式数值算法

为了便于建立与排课表问题有关的决策支持系统,本文给出一个求解排课表问题的启发式数值算法,证明了算法的正确性,并举例说明算法的应用。该算法具有易于编程实现、收敛性好等优点,大量数值实值表明该算法能求得问题的最优解。

Regularized canonical correlation analysis with unlabeled data

In standard canonical correlation analysis (CCA), the data from definite datasets are used to estimate their canonical correlation. In real applications, for example in bilingual text retrieval, it may have a great portion of data that we do not know which set it belongs to. This part of data is called unlabeled data, while the rest from definite datasets is called labeled data. We propose a novel method called regularized canonical correlation analysis (RCCA), which makes use of both labeled and unlabeled samples. Specifically, we learn to approximate canonical correlation as if all data were labeled. Then, we describe a generalization of RCCA for the multi-set situation. Experiments on four real world datasets, Yeast, Cloud, Iris, and Haberman, demonstrate that, by incorporating the unlabeled data points, the accuracy of correlation coefficients can be improved by over 30%.