可压缩旋转恒星的存在性与稳定性理论

旋转是宇宙中的一个普遍现象.从Newton时代开始,许多著名的数学家和物理学家如:Maclaurin,Clairaut,Liouville,Lyapunov,Poincare等人都对旋转星理论作出过非常重要的贡献.旋转星理论中一个基本的问题就是研究自重力旋转流体相对平衡结构的存在性与稳定性,这个问题至今没有完全解决.本文将尝试对其研究历史作一些综述,给出近年来的一些最新进展,并介绍一些未解决的问题.     

p-距离空间中的一类压缩映象的不动点定理

仿照距离空间,2-距离空间中的一些概念,引入了p-距离空间及p-距离空间中的一些基本概念.根据研究2-距离空间中不动点理论的思想和方法,利用泛函分析的理论,对p-距离空间中的不动点问题进行了研究.把2.距离空间中压缩型映像的不动点理论移植到了p-距离空间中,形成了p-距离空间中压缩型映像的一些基本不动点理论.其距.离空...     

一些循环代数函数域的不分明理想类（英文）

本文研究了一些l次循环函数域的理想类群的不分明理想类的结构问题.利用函数域的素理想分解理论和理想的一阶上同调理论,得到了这几类循环函数域的理想类群的l-秩的下界.进一步,我们还得了一些不分明理想类中不含不分明理想的域的充分条件.     

利用远场模式的不完全数据反演声波阻尼系数

1.引言 对声波反散射理论的研究,已经有大量的研究[1.5].[7]利用散射波的远场模式反演边界条件中的阻尼系数.但是在实际问题中,要在物体的一周测量到远场模式的值是不现实的.因此,利用远场模式的不完全数据来进行反演有明显的物理和实际意义.一些文献将此类问题称为声波反散射理论的“limited aperture problem”.本文利用远场模式的不完全数据,反演边界条件中的声波阻尼系数.     

DSS的模型自动生成器研究

本文主要研究了 DSS的模型自动生成问题 ,90年代以来 ,计算机在软件编程等各个方面出现了许多新的技术 ,自动编程技术是其中最引人注目的技术之一 .自动编程技术属于软件自动化的一部分 .市场上对一些简单的软件系统已经有了一些成功的产品 .但这种技术在 DSS的软件领域中尚不多见 .将这一技术应用到 DSS的开发之中 ,对 DSS从理论到实践的转化有着重要的意义 .     

期货价格预测——反向传播神经网络模型

利用神经网络的 BP算法 ,对较难解决的期货价格问题做了一些研究 ,获得了一定的成功 .     

选控图的应用条件及诊断问题

1979年起,张公绪付教授提出并推广选控图的理论和方法,对活跃质量管理界的学术研究气氛起了积极的作用.本市亦有个别企业作过尝试,遇到了一些困难.这也促使我们对选控图进行了断续的不系统的研究.本文对选控图的应用条件及诊断问题作一初步探讨,对选控图的一些理论和方法及应用     

一些循环代数函数域的不分明理想类

本文研究了一些l次循环函数域的理想类群的不分明理想类的结构问题.利用函数域的素理想分解理论和理想的一阶上同调理论,得到了这几类循环函数域的理想类群的l-秩的下界.进一步,我们还得了一些不分明理想类中不含不分明理想的域的充分条件.     

对偶锥映像上的问题

本文研究了对偶锥映像上的一些非线性问题.利用拓扑度理论和半序的方法,获得了对偶锥映像的锐角原理,Debrunner-Flor不等式和不动点定理的结果.推广了一般单调映像的锐角原理和Debrunner-Flor不等式的一些结果.     

以中心- 迎风数值格式研究具有外力项的p 系统

本文以半离散中心- 迎风数值格式研究具有外力项的p 系统. 中心型数值格式用来处理双曲型守恒律或系统的优势是快速且简单, 因为不需要使用近似Riemann 解, 也不需要做特征分解. 我们的数值模拟验证了理论研究结果: 具有外力项的p 系统的解的收敛及爆破行为, 同时也指出一些尚待理论研究的问题.     

Improved scatter search for the global optimization of computationally expensive dynamic models

A new algorithm for global optimization of costly nonlinear continuous problems is presented in this paper. The algorithm is based on the scatter search metaheuristic, which has recently proved to be efficient for solving combinatorial and nonlinear optimization problems. A kriging-based prediction method has been coupled to the main optimization routine in order to discard the evaluation of solutions that are not likely to provide high quality function values. This makes the algorithm suitable for the optimization of computationally costly problems, as is illustrated in its application to two benchmark problems and its comparison with other algorithms.     

Constraint programming-based column generation

This paper surveys recent applications and advances of the constraint programming-based column generation framework, where the master subproblem is solved by traditional OR techniques, while the pricing subproblem is solved by constraint programming (CP). This framework has been introduced to solve crew assignment problems, where complex regulations make the pricing subproblem demanding for traditional techniques, and then it has been applied to other contexts. The main benefits of using CP are the expressiveness of its modeling language and the flexibility of its solvers. Recently, the CP-based column generation framework has been applied to many other problems, ranging from classical combinatorial problems such as graph coloring and two dimensional bin packing, to application oriented problems, such as airline planning and resource allocation in wireless ad hoc networks.      

Constraint Programming-based Column Generation

This paper surveys recent applications and advances of the Constraint Programming-based Column Generation framework, where the master subproblem is solved by traditional OR techniques, while the pricing subproblem is solved by Constraint Programming. This framework has been introduced to solve crew assignment problems, where complex regulations make the pricing subproblem demanding for traditional techniques, and then it has been applied to other contexts. The main benefits of using Constraint Programming are the expressiveness of its modeling language and the flexibility of its solvers. Recently, the Constraint Programming-based Column Generation framework has been applied to many other problems, ranging from classical combinatorial problems such as graph coloring and two dimensional bin packing, to application oriented problems, such as airline planning and resource allocation in wireless ad-hoc networks.     

Multilevel Refinement for Combinatorial Optimisation Problems

We consider the multilevel paradigm and its potential to aid the solution of combinatorial optimisation problems. The multilevel paradigm is a simple one, which involves recursive coarsening to create a hierarchy of approximations to the original problem. An initial solution is found (sometimes for the original problem, sometimes the coarsest) and then iteratively refined at each level. As a general solution strategy, the multilevel paradigm has been in use for many years and has been applied to many problem areas (most notably in the form of multigrid techniques). However, with the exception of the graph partitioning problem, multilevel techniques have not been widely applied to combinatorial optimisation problems. In this paper we address the issue of multilevel refinement for such problems and, with the aid of examples and results in graph partitioning, graph colouring and the travelling salesman problem, make a case for its use as a metaheuristic. The results provide compelling evidence that, although the multilevel framework cannot be considered as a panacea for combinatorial problems, it can provide an extremely useful addition to the combinatorial optimisation toolkit. We also give a possible explanation for the underlying process and extract some generic guidelines for its future use on other combinatorial problems.     

A Convergent Variant of the Nelder–Mead Algorithm

The Nelder–Mead algorithm (1965) for unconstrained optimization has been used extensively to solve parameter estimation and other problems. Despite its age, it is still the method of choice for many practitioners in the fields of statistics, engineering, and the physical and medical sciences because it is easy to code and very easy to use. It belongs to a class of methods which do not require derivatives and which are often claimed to be robust for problems with discontinuities or where the function values are noisy. Recently (1998), it has been shown that the method can fail to converge or converge to nonsolutions on certain classes of problems. Only very limited convergence results exist for a restricted class of problems in one or two dimensions. In this paper, a provably convergent variant of the Nelder–Mead simplex method is presented and analyzed. Numerical results are included to show that the modified algorithm is effective in practice.     

Experiments in integer programming

A hybrid algorithm to solve large scale zero–one integer programming problems has been developed. The algorithm combines branch-and-bound, enumeration and cutting plane techniques. Mixed-integer cuts are generated in the initial phase of the algorithm and added to the L.P. Benders cuts are derived and used implicitly but, except for the cut from the initial LP, are not stored. The algorithm has been implemented on an experimental basis in MPSX/370 using its Extended Control Language and Algorithmic Tools. A computational study based on five well-known difficult test problems and on three practical problems with up to 2000 zer–one variables shows that the hybrid code compares favorably with MIP/370 and with results published for other algorithms.     

On the modified Taylor's approximation for the solution of linear and nonlinear equations

Fourier series and Taylor's expansions are commonly used in the fields of science and engineering. A whole range of interesting physical problems can be solved using one or the other of these standard techniques. Yet, there is a class of problems whose solutions exhibit near sinusoidal or repetitive behavior that cannot be solved using the above expansions. It is for these types of problems that the modified Taylor expansion has been developed. It is a method that combines the advantages of the repetitive behavior of sinusoidal functions and polynomial series. This technique has not been employed for tackling equations whose solutions are near sinusoidal. We discuss the method and apply it to a number of interesting problems that show its utility. Examples include the solution of differential, integral, integro-differential and functional equations. In addition, we propose an algorithm for the numerical integration of a function which exhibits near periodic behavior, namely the Bessel function. Most of the symbolic and numerical computations have been performed using the Computer Algebra System––Maple.     

Recent developments in dynamic advertising research

A variety of continuous-time differential functions have been developed to investigate dynamic advertising problems in business and economics fields. Since major dynamic models appearing before 1995 have been reviewed by a few survey papers, we provide a comprehensive review of the dynamic advertising models published after 1995, which are classified into six categories: (i) Nerlove–Arrow model and its extensions, (ii) Vidale–Wolfe model and its extensions, (iii) Lanchester model and its extensions, (iv) the diffusion models, (v) dynamic advertising-competition models with other attributes, and (vi) empirical studies for dynamic advertising problems. For each category, we first briefly summarize major relevant before-1995 models, and then discuss major after-1995 models in details. We find that the dynamic models reviewed in this paper have been extensively used to analyze various advertising problems in the monopoly, duopoly, oligopoly, and supply chain systems. Our review reveals that the diffusion models have not been used to analyze advertising problems in supply chain operations, which may be a research direction in the future. Moreover, we learn from our review that very few publications regarding dynamic advertising problems have considered the supply chain competition. We also find that very few researchers have used the diffusion model to investigate the dynamic advertising problems with product quality as a decision variable; and, the pricing decision has not been incorporated into any extant Lanchester model. The paper ends with a summary of our review and suggestions on possible research directions in the future.     

A survey of school timetabling research

Although there has been a fair amount of research in the area of school timetabling, this domain has not developed as well as other fields of educational timetabling such as university course and examination timetabling. This can possibly be attributed to the fact that the studies in this domain have generally been conducted in isolation of each other and have addressed different school timetabling problems. Furthermore, there have been no comparative studies on the success of different methodologies on a variety of school timetabling problems. As a way forward this paper provides an overview of the research conducted in this domain, details of problem sets which are publicly available and proposes areas for further research in school timetabling.     

A Controlled Random Search Technique Incorporating the Simulated Annealing Concept for Solving Integer and Mixed Integer Global Optimization Problems

In this paper, a computational algorithm, named RST2ANU algorithm, has been developed for solving integer and mixed integer global optimization problems. This algorithm, which primarily is based on the original controlled random search approach of Price [22i], incorporates a simulated annealing type acceptance criterion in its working so that not only downhill moves but also occasional uphill moves can be accepted. In its working it employs a special truncation procedure which not only ensures that the integer restrictions imposed on the decision variables are satisfied, but also creates greater possibilities for the search leading to a global optimal solution. The reliability and efficiency of the proposed RST2ANU algorithm has been demonstrated on thirty integer and mixed integer optimization problems taken from the literature. The performance of the algorithm has been compared with the performance of the corresponding purely controlled random search based algorithm as well as the standard simulated annealing algorithm. The performance of the method on mathematical models of three realistic problems has also been demonstrated.