线性优化

A flexible semantic distance function called X_Dist is proposed, which can utilize the semantic information to measure the difference between two objects based on a solution to the transportation problem from linear optimization.

提出了一个量化对象间语义差别的距离函数X_Dist，它基于线性优化中的运输问题模型和相关的语义信息量化两个对象之间的差别。

Quantifying similarity/difference between two objects plays an important role in many contexts The quality of the similarity/difference scores can be improved by considering the semantic information related to the features of objects A flexible semantic distance function called X Dist is proposed, which can utilize the semantic information to measure the difference between two objects based on a solution to the transportation problem from linear optimization With a ground distance function for single features being a metric, X Dist is also a metric This property is very useful for making searching efficient, but is not investigated in the previous research Moreover, the experimental results show X Dist can be as good as the previously studied similarity measures in nearest neighbor searching, discriminative power and computing speed

量化对象间相似性/差别的方法具有广泛的用途，利用相关的语义信息能够得到更好的量化结果提出了一个量化对象间语义差别的距离函数XDist，它基于线性优化中的运输问题模型和相关的语义信息量化两个对象之间的差别在量化特征的差别函数是度量的情况下，XDist是一个度量，在提高搜索的效率方面具有优势，弥补了以往研究的不足，而且实验初步表明，此函数在最近邻查询效果、差别分辨力和计算速度方面能与已有函数相媲美1 引言量化对象间相似性/差别的方法具有广泛的用途，例如数据挖掘中的聚类；信息检索中的相似文档查询；协同过滤中的相似用户查询；CBR中的相似案例查询集合是对象特征表示的常用结构，集合的元素是与对象特征相关的项，它们可以

For uncertain linear optimization, the choice of uncertain set and transformation of robust counterpart were investigated.

对于不确定线性优化问题，研究了不确定集的选择以及模型鲁棒对等式转化方法。

Combining with the feature of the proposed model, a linear multi-objective optimization algorithm based on the membership function of fuzzy theory is put forward.

文章结合模型的特点提出了一种基于模糊理论隶属度的多目标线性优化算法。

The linear optimization model of traffic environment capacity is developed on the basis of the air pollutants diffusion and transmission theories.

基于环境科学的空气污染物扩散、传递理论，建立了交通环境容量的线性优化模型。

Based on the best-first principle, a heuristic algorithm of sequential linear programming is proposed.

针对这类问题的特点，本文提出启发式序列线性优化算法。

There are strong convergence and reliability properties for these algorithms, so they are widely used in many fields.

中文摘要：信赖域方法是非线性优化中一类重要的数值计算方法，它具有良好的收敛性和稳定性，因此在许多领域都有广泛的应用。

Trust region method is a class of important numerical algorithms for nonlinear optimization. There are strong convergence and reliability properties for these algorithms, so they are widely used in many fields.

信赖域方法是非线性优化中一类重要的数值计算方法，它具有良好的收敛性和稳定性，因此在许多领域都有广泛的应用。

Relative key concept and technique of this method was described in detail in this paper, such as the decomposition of balance matrix, the modes of self-stress, the modes of inextensional mechanisms, the analysis model of optimization under linear constrained condition, the integration of constrained matrix.

本文从桁架理论的奇异值分解出发，结合有约束线性优化方法，提出了解决这一问题的另一途径，编制了相应的分析程序，经过算例分析与验证，证明了该方法的正确性。

Relative key concept and technique of this method was described in detail in this paper, such as the decomposition of balance matrix, the modes of self-stress, the modes of inextensional mechanisms, the analysis model of optimization under linear constrained condition, the integration of constrained matrix.

文中对桁架理论的奇异值分解法、自应力模态、机构位移模态、有约束线性优化数学模型、约束矩阵集成等关键概念和技术进行了详细的推导说明。

We have no common name for a mime of Sophron or Xenarchus and a Socratic Conversation; and we should still be without one even if the imitation in the two instances were in trimeters or elegiacs or some other kind of verse--though it is the way with people to tack on 'poet' to the name of a metre, and talk of elegiac-poets and epic-poets, thinking that they call them poets not by reason of the imitative nature of their work, but indiscriminately by reason of the metre they write in.

索夫农 、森那库斯和苏格拉底式的对话采用的模仿没有一个公共的名称；三音步诗、挽歌体或其他类型的诗的模仿也没有——人们把&诗人&这一名词和格律名称结合到一起，称之为挽歌体诗人或者史诗诗人，他们被称为诗人，似乎只是因为遵守格律写作，而非他们作品的模仿本质。

The relationship between communicative competence and grammar teaching should be that of the ends and the means.

交际能力和语法的关系应该是目标与途径的关系。