求解二阶锥规划问题的VU分解方法

给出解决二阶锥规划(SOCP)问题的VU-分解方法.问题首先被转化为非线性规划,并给出相应的精确罚函数的Clarke次微分结构及VU-空间分解．在某种条件下,可以计算出一个二阶连续可微的轨道,进而得到目标函数f在其上的二阶展开．最后给出一个具有超线性收敛速度的概念型算法．     

重构高阶导数的磨光方法

考虑由扰动数据重构原函数的导数问题．基于L-广义解正则化理论,提出了一个新的磨光方法的框架．给出一个具体的求解前3阶导数的算法,其中正则化策略选择了一种改进的TSVD(truncated singular value decomposition)方法(典则TSVD方法)．数值结果进一步验证了理论结果及新方法的有效性．     

基于LMI方法的MIMO系统鲁棒D-稳定的频域判定

研究了具有结构不确定性的多输入多输出(MIMO)系统的鲁棒D-稳定性判定问题．首先提出了系统的3种数学模型,包括凸多面体型多项式矩阵模型、多线性型多项式矩阵模型和反馈型多项式矩阵模型．然后分析了各模型在参数空间中的凸性,从而将系统的稳定性检验问题转化为线性矩阵不等式(LMI)的可行性问题,并由此给出了D-稳定性的判定方法．     

周期裂纹削弱的无限长板条的应力分析

作出了周期裂纹削弱的无限长板条的应力分析．假设这些裂纹均在水平位置,又板条承受 y方向的拉伸力p．此时边值问题归结为一个复杂混合边值问题．发现,对此问题言,特征展开变分原理方法 ( eigenfunction expansion variational method,简称为EEVM)是非常有效的．研究了裂纹端的应力强度因子和T-应力．从拉伸力作用下的弹性变形考虑,开裂板条可等价于一不开裂的正交异性板条．还分析了等价正交异性板条的弹性性质．最后给出了算例和数值结果．     

Banach空间中的多值拟变分包含

引入和研究了Banach空间中的多值拟变分包含问题．借助预解算子技巧,建立了某些解的存在性定理及解的迭代逼近．文中所给出的结果改进和推广了许多人的最新结果．     

局部FC-一致空间内凝聚映象的极大元和广义对策及应用(Ⅱ)

在局部FC-一致空间内,引入和研究了某些新的涉及凝聚集值映象的广义矢量拟平衡问题组．应用作者在文(Ⅰ)中对局部FC-一致空间内的凝聚集值映象得到的极大元存在性定理,在局部FC-一致空间内,对这些广义矢量拟平衡问题组的解,证明了某些新的存在性定理．这些结果改进和推广了文献中某些已知结果到局部FC-一致空间．     

碳纤维增强复合材料板中Ⅰ型裂纹扩展方向预测理论研究

对含裂纹碳纤维增强的复合材料层合板进行了裂纹扩展方向预测理论的研究．介绍了一种新的复合材料Z断裂理论,在裂纹尖端Z因子的定义中引入了新概念:面内平均应变,面内膨胀应变能密度因子和交互特征函数．并通过理论分析和实验测试证明了Z断裂理论的正确性．     

均衡约束数学规划问题的一种新的约束规格

本文研究了均衡约束数学规划(MPEC)问题.利用其弱稳定点,获得了一种新的约束规格–MPEC的伪正规约束规格.用一种简单的方式,证明了该约束规格是介于MPEC-MFCQ(即MPEC,Mangasarian-Fromowitz约束规格)与MPEC-ACQ(即MPEC,Abadie约束规格)之间的约束规格,因此该约束规格也可以导出MPEC问题的M-稳定点.最后通过两个例子,说明了该约束规格与MPEC-MFCQ以及与MPEC-ACQ之间是严格的强弱关系.     

Banach空间中含m-增生算子紧扰动的映象定理的若干注记

借助于某些新的逼近技巧得到了几个含ｍ－增生算子紧扰动的映象定理．这些结果改进并扩展了由Ｋａｒｔｓａｔｏｓ，Ｚｈｕ和Ｋａｒｔｓａｔｏｓ ａｎｄ Ｍａｂｒｙ所建立的相应结果．     

uv-理论在一类半无限最小化问题的应用

Lemarechal,Oustry和Sagastizabal(2000)提出的uv分解理论为解决非光滑函数的高阶展开提供了一种新的途径，并将此理论应用于研究具有有限个约束的非线性规划的精确罚函数．本文将这一研究推广到具有无限约束的一类半无限规划的问题上，并给出了与这类最小化问题的精确罚函数的U-Lagrange函数有关的某些结果．     

Identifying Fourth Graders' Understanding of Rational Number Representations: A Mixed Methods Approach

This study examined average‐, high‐ and top‐performing US fourth graders' rational number problem solving and their understanding of rational number representations. In phase one, all students completed a written test designed to tap their skills for multiplication, division and rational number word‐problem solving. In phase two, a subset of students sorted cards that showed part‐whole, ratio, quotient, measure, and operator perspectives of rational number representations. Each perspective was shown in numerical notational, word‐problem, and visual formats. The results indicated that top‐performing students scored significantly higher in problem solving and showed more effectively linked rational number representations than the other groups. The results imply that successful rational number problem solving is intertwined with representational knowledge for a wide range of rational numbers and that the bulk of US students do not possess effective skills for working with rational number representations.     

Journey toward Teaching Mathematics through Problem Solving

Teaching mathematics through problem solving is a challenge for teachers who learned mathematics by doing exercises. How do teachers develop their own problem solving abilities as well as their abilities to teach mathematics through problem solving? A group of teachers began the journey of learning to teach through problem solving while taking a Teaching Elementary School Mathematics graduate course. This course was designed to engage teachers in problem solving during class meetings and required them to do problem solving action research in their classrooms. Although challenged by the course problem solving work, teachers became more comfortable with the mathematics and recognized the importance of group work while problem solving. As they worked with their students, teachers were more confident in their students' abilities to be successful problem solvers. For some teachers, a strong problem solving foundation was established. For others, the foundation was more tentative.     

Hybrid co-evolutionary particle swarm optimization and noising metaheuristics for the delay constrained least cost path problem

This paper presents a co-evolutionary particle swarm optimization (PSO) algorithm, hybridized with noising metaheuristics, for solving the delay constrained least cost (DCLC) path problem, i.e., shortest-path problem with a delay constraint on the total “cost” of the optimal path. The proposed algorithm uses the principle of Lagrange relaxation based aggregated cost. It essentially consists of two concurrent PSOs for solving the resulting minimization-maximization problem. The main PSO is designed as a hybrid PSO-noising metaheuristics algorithm for efficient global search to solve the minimization part of the DCLC-Lagrangian relaxation by finding multiple shortest paths between a source-destination pair. The auxiliary/second PSO is a co-evolutionary PSO to obtain the optimal Lagrangian multiplier for solving the maximization part of the Lagrangian relaxation problem. For the main PSO, a novel heuristics-based path encoding/decoding scheme has been devised for representation of network paths as particles. The simulation results on several networks with random topologies illustrate the efficiency of the proposed hybrid algorithm for the constrained shortest path computation problems.     

非线性0-1规划问题的蜂群算法

针对非线性0-1规划,提出采用一种智能优化算法——蜂群算法进行求解.描述了蜂群算法的实现过程,并在计算机上编程予以实现.经大量实例测试,并与其它算法进行比较,获得了满意的结果.说明了蜂群算法在解决非线性0-1规划问题上的可行性与有效性,同时具有良好的优化能力..     

Problem solving as a challenge for mathematics education in The Netherlands

This paper deals with the challenge to establish problem solving as a living domain in mathematics education in The Netherlands. While serious attempts are made to implement a problem-oriented curriculum based on principles of realistic mathematics education with room for modelling and with integrated use of technology, the PISA 2003 results suggest that this has been successful in educational practice only to a limited extent. The main difficulties encountered include institutional factors such as national examinations and textbooks, and issues concerning design and training. One of the main challenges is the design of good problem solving tasks that are original, non-routine and new to the students. It is recommended to pay attention to problem solving in primary education and in textbook series, to exploit the benefits of technology for problem solving activities and to use the schools’ freedom to organize school-based examinations for types of assessment that are more appropriate for problem solving.     

A Rollout Metaheuristic for Job Shop Scheduling Problems

In this paper we deal with solution algorithms for a general formulation of the job shop problem, called alternative graph. We study in particular the job shop scheduling problem with blocking and/or no-wait constraints. Most of the key properties developed for solving the job shop problem with infinite capacity buffer do not hold in the more general alternative graph model. In this paper we report on an extensive study on the applicability of a metaheuristic approach, called rollout or pilot method. Its basic idea is a look-ahead strategy, guided by one or more subheuristics, called pilot heuristics. Our results indicate that this method is competitive and very promising for solving complex scheduling problems.     

A new extension principle algorithm for the traveling salesman problem

In this paper, an algorithm for solving the asymmetric traveling salesman problem is developed and tested by computation. This algorithm is based on the extension principle by Schoch and uses the assignment problem relaxation of the traveling salesman problem for computing lower bounds. Computational experience with randomly generated test problems indicate that the present algorithm yields good results in runtime which are comparable with the results of Smith/Srinivasan/Thompson. Computational experience are reported for up to 120-node problems with uniformly distributed and approximately normally distributed cost.     

Using concave envelopes to globally solve the nonlinear sum of ratios problem

This article presents a branch and bound algorithm for globally solving the nonlinear sum of ratios problem (P). The algorithm works by globally solving a sum of ratios problem that is equivalent to problem (P). In the algorithm, upper bounds are computed by maximizing concave envelopes of a sum of ratios function over intersections of the feasible region of the equivalent problem with rectangular sets. The rectangular sets are systematically subdivided as the branch and bound search proceeds. Two versions of the algorithm, with convergence results, are presented. Computational advantages of these algorithms are indicated, and some computational results are given that were obtained by globally solving some sample problems with one of these algorithms.     

Reconstruction of boundary regimes in the inverse problem of thermal convection of a high-viscosity fluid

A problem of reconstruction of boundary regimes in a model for free convection of a high-viscosity fluid is considered. A variational method and a quasi-inversion method are suggested for solving the problem in question. The variational method is based on the reduction of the original inverse problem to some equivalent variational minimum problem for an appropriate objective functional and solving this problem by a gradient method. When realizing the gradient method for finding a minimizing element of the objective functional, an iterative process actually reducing the original problem to a series of direct well-posed problems is organized. For the quasi-inversion method, the original differential model is modified by means of introducing special additional differential terms of higher order with small parameters as coefficients. The new perturbed problem is well-posed; this allows one to solve this problem by standard methods. An appropriate choice of small parameters gives an opportunity to obtain acceptable qualitative and quantitative results in solving the inverse problem. A comparison of the methods suggested for solving the inverse problem is made with the use of model examples.