分区函数法在加权残数法中的应用

本文提出了一个分区函数法的概念.即根据边界形状或刚度、荷载的变化,将原受力体分成若干个分区.在每个分区中设定不同的试函数,并在各分区的交界上考虑了连续协调条件.这样共建立了内部平衡、外部边界和交线协调三种残数方程.文中给出了公式和例题.     

约束非光滑优化的投影次梯度法Ⅰ:线性约束

文[1]、[5]、[6]将Rosen投影梯度法的思想的推广到了带线性约束的非光滑目标函数的极小化问题,建立了此类问题的算法,然由于末抓住非光滑函数的特性,而使算法显得复杂,且只能得到ε一稳定点。 本文从另一角度,即约束问题的kuhn-Tucker条件出发,结合Polak和Mayne对非光滑函数提出的c.d.f.映射,将投影次梯度方阳归为求解一个结构性强的二次规划子问题,从而     

解非线性多解边值问题的异步并行打靶法

在多处理机(MIMD)上用异步并行打靶法来数值求解两点边值问题是最为有效的。这是因为求解过程中可以采用分区搜索的方法,而这种搜索过程几乎是完全独立地进行的.另一方面,非线性的具有多个解的数学物理问题的求解是一个比较困难的问题.因为采用通常的迭代法计算,有时很难求出全部解来(参看[1]、[2]),尤其是遇到所谓“排斥性不动点”(repulsive fixed point)时,一般迭代算法往往失败,而采用打靶法则可能将全部解求出来,如果打靶过程是数值稳定的话.用打靶法计算两点边值问题的文献很多(例如参看[3]、[4]).H.B.Keller 和 A.W.Wolfe[5]1965年就成功地应用打靶法来计算非线性分歧问题,后来有了迅速的发展(可参看文献[6]、[7]、[8]).     

多元函数条件极值的充分性讨论

在Lagrange乘数法的基础上,通过引入纠正函数,对文[1],[2]中遗留的条件极值充分性的问题作进一步研究,使条件极值的判定方法更加丰富.     

带约束的变界截尾随机逼近算法

自从50年代 Robbins-Monro 提出随机逼近算法用来求回归函数的零点或极值以来,人们不仅用概率的方法而且用微分方程的方法去处理它.近几年来,出现了随机变界截尾算法,它克服了事先假定算法有界的本质困难.[6]、[7]研究了二步算法,去掉了对某一 Liapunov 函数存在性的要求,[8]将[5]和[6]的方法结合起来,在较弱的条件下求解了优化问题.[9]用这些思想给出了连续时间变界截尾的两步算法.[5—9]讨论的是无约束随机逼近问题.对于有约束的确定性系统的极值问题,我们可以用 Lagrange 乘子法,也可以用罚函数的算法,A.Miele 将 Lagrange 乘子和罚函数结合提出了罚-乘子算法,Kushner 将罚-乘子算法用在有约束的随机逼近算法中,在假定算法有界的条件下证明了收敛性.本文将变界截尾的随机逼近算法和罚-乘子算法结合起来,用到带约束的优化问题中,既不事先假定算法有界,也不要求存在某一 Liapuaov 函数,得到了算法的收敛性.     

机械参数优化──介绍处理多约束的方法

机械优化设计基本上是采用非线性规划方法[1]、[2]、[3],诸如随机方向搜索法、变尺度法(DFP、 BFGS)和惩罚函数法等。这些方法寻求的都是局部极值,靠增多初始点,来增大找到最优解的概率.然而有效的初始点的选取并非易事,有些课题甚至由于找不到初始点而无法优化. 三次设计(系统设计、参数设计、容差设计)于八十年代传入我国,在电子,光学等行业得到成功的应用.但是在国内外文献上([4]、[5])均未见到它在机械优化中的应用,本文在此做一个尝试,以[1]中的一级齿轮圆柱减速器的优化为例,进行探讨,希望起到抛砖引玉的作用. 机械优化问题一般具…     

用向量V函数法研究变系数线性时滞微分大系统零解的稳定性

我们知道,对时滞大系统的稳定性研究,具有重大的理论价值和实际意义.从现有的文献来看,对这一问题的论述还远不够完善,主要困难是缺少研究的方法.在本文中,我们利用[10]中所建立的一个时滞微分不等式,并结合向量 V 函数法,研究两类变系数时滞微分大系统零解的稳定性,获得了一些简单的稳定性代数判据,推广和改进了[4—8]中的工作.     

n元函数极值的探讨

在高等数学或数学分析的教学中，经常遇到这样的问题：如何将二元函数求极值的方法推广到，n元函数中去。这一问题在教材中很小涉及，本在[1]、[2]基础上，讨论了，n(n≥2)元函数极值点的判别法，确立了判别临界点为极值点的一个充分条件。这些条件与[1]、[2]的方法是等价的，但方法简单。     

关于多裂纹圆柱体的扭转*

本文在文[1]基础上,导出了含有任意分布裂纹系的圆柱扭曲函数的解析表达式,从而把问题化为以未知位错密度函数表示的奇异积分方程组.文中利用奇异积分方程的数值方法[2,7],对带有多根裂纹的圆柱的抗扭刚度和应力强度因子作了若干数值计算.此外,本文还首次将裂纹切割法[5]推广用于求解矩形柱的扭转,数值结果表明方法是成功的.     

一类概周期函数的准解析函数族

关于概周期函数的准解析函数族的问题,已为李国平教授[1]—[3],Б.Левин教授[4]等所研究。本文,将应用[1]中关于整函数的一个结果,类似于[4]中的证明方法,得出一类概周期函数的准解析函数族。从[2]关于整函数的一个结果,直接推出下列引理:     

Nested partitions for the large-scale extended job shop scheduling problem

This paper addresses the large-scale extended job shop scheduling problem while considering the bill of material and the working shifts constraints. We propose two approaches for the problem. One is based on dispatching rules (DR), and the other is an application of the Nested Partitions (NP) Framework. A sampling approach for the exact feasible subregion is developed to complete the NP method. Furthermore, to efficiently search each subregion, a weighted sampling approach is also presented. Computational experiments show that the NP method with weighted sampling can find good solutions for most large-scale extended job shop scheduling problems.     

Numerical response of an arbitrarily shaped harbour

The numerical development of resonance of a harbour of arbitrary shape and depth is studied. The harbour is subdivided into subregions according to the variations of bottom topography such that each subregion is of uniform depth. The Helmholtz wave equation is formulated in each subregion as an integral equation of the Green's theorem. The solution to the entire harbour basin is obtained by a matching procedure at the subregion boundaries. Here, we consider a harbour with basins of constant depths connected in series successively to accommodate a more complicated harbour geometry. An application of this study is made to Kincardine harbour with five basins connected in series successively.     

Interval oriented multi-section techniques for global optimization

This paper deals with two different optimization techniques to solve the bound-constrained nonlinear optimization problems based on division criteria of a prescribed search region, finite interval arithmetic and interval ranking in the context of a decision maker’s point of view. In the proposed techniques, two different division criteria are introduced where the accepted region is divided into several distinct subregions and in each subregion, the objective function is computed in the form of an interval using interval arithmetic and the subregion containing the best objective value is found by interval ranking. The process is continued until the interval width for each variable in the accepted subregion is negligible. In this way, the global optimal or close to global optimal values of decision variables and the objective function can easily be obtained in the form of an interval with negligible widths. Both the techniques are applied on several benchmark functions and are compared with the existing analytical and heuristic methods.     

弹性厚板的分区广义变分原理

本文提出弹性厚板分区广义变分原理,其要点如下:1.各分区可任意定为势能区或余能区.分区势能、分区余能、分区混合变分原理是它的三种特殊形式.2.每个分区中独立变分变量的个数可任意规定.每个分区可定为单类变量区、二类变量区或三类变量区.3.每个交界线上的位移和力的连接条件可以放宽.这个原理为非协调元的厚板有限元法提供理论基础.各种厚板有限元模型可看作这个原理的特殊应用.特别是弹性厚板分区混合变分原理的提出为分区混合有限元法应用于厚板问题打下了基础.     

A divisive clustering method for functional data with special consideration of outliers

This paper presents DivClusFD, a new divisive hierarchical method for the non-supervised classification of functional data. Data of this type present the peculiarity that the differences among clusters may be caused by changes as well in level as in shape. Different clusters can be separated in different subregion and there may be no subregion in which all clusters are separated. In each step of division, the DivClusFD method explores the functions and their derivatives at several fixed points, seeking the subregion in which the highest number of clusters can be separated. The number of clusters is estimated via the gap statistic. The functions are assigned to the new clusters by combining the k-means algorithm with the use of functional boxplots to identify functions that have been incorrectly classified because of their atypical local behavior. The DivClusFD method provides the number of clusters, the classification of the observed functions into the clusters and guidelines that may be for interpreting the clusters. A simulation study using synthetic data and tests of the performance of the DivClusFD method on real data sets indicate that this method is able to classify functions accurately.     

A subregion DRBEM formulation for the dynamic analysis of two-dimensional cracks

The dual reciprocity boundary element method employing the step by step time integration technique is developed to analyse two-dimensional dynamic crack problems. In this method the equation of motion is expressed in boundary integral form using elastostatic fundamental solutions. In order to transform the domain integral into an equivalent boundary integral, a general radial basis function is used for the derivation of the particular solutions. The dual reciprocity boundary element method is combined with an efficient subregion boundary element method to overcome the difficulty of a singular system of algebraic equations in crack problems. Dynamic stress intensity factors are calculated using the discontinuous quarter-point elements. Several examples are presented to show the formulation details and to demonstrate the computational efficiency of the method.     

Adaptive Methods for Spatial Scan Analysis via Semiparametric Mixture Models

Spatial scan density (SSD) estimation via mixture models is an important problem in the field of spatial statistical analysis and has wide applications in image analysis. The “borrowed strength” density estimation (BSDE) method via mixture models enables one to estimate the local probability density function in a random field wherein potential similarities between the density functions for the subregions are exploited. This article proposes an efficient methods for SSD estimation by integrating the borrowed strength technique into the alternative EM framework which combines the statistical basis of the BSDE approach with the stability and improved convergence rate of the alternative EM methods. In addition, we propose adaptive SSD estimation methods that extend the aforementioned approach by eliminating the need to find the posterior probability of membership of the component densities afresh in each subregion. Simulation results and an application to the detection and identification of man-made regions of interest in an unmanned aerial vehicle imagery experiment show that the adaptive methods significantly outperform the BSDE method. Other applications include automatic target recognition, mammographic image analysis, and minefield detection.     

Uncertain constrained optimization by interval-oriented algorithm

This paper deals with an interval-oriented approach to solve general interval constrained optimization problems. Generally, this type of problems has infinitely many compromise solutions. The aim of this approach is to obtain one of such solutions with higher accuracy and lower computational cost. The proposed algorithm is nothing but a different kind of branch and bound algorithm with multi-section division criterion of the search region (or box). In the proposed technique, the prescribed/accepted region is divided into several distinct subregions and in each feasible subregion the interval objective function value is computed. Then the subregion containing the best objective value is found by applying a specific interval ranking rule defined with respect to the pessimistic decision makers’ point of view. The process is continued until the interval width for each variable in the accepted subregion is negligible. Finally, the algorithm converges to a compromise solution in interval form. To illustrate the method and also to test the efficiency as well as the effectiveness of the proposed algorithm, we have solved some numerical examples.     

An efficient interval computing technique for bound-constrained uncertain optimization problems

In this article, a competent interval-oriented approach is proposed to solve bound-constrained uncertain optimization problems. This new class of problems is considered here as an extension of the classical bound-constrained optimization problems in an inexact environment. The proposed technique is nothing but an imitation of the well-known interval analysis-based branch-and-bound optimization approach. Efficiency of this technique is strongly dependent on division, bounding, selection/rejection and termination criteria. The technique involves a multisection division criterion of the accepted/proposed search region. Then, we have employed the interval-ranking definitions with respect to the pessimistic decision makers’ point of view given by Mahato and Bhunia [Interval-arithmetic-oriented interval computing technique for global optimization, Appl. Math. Res. Express 2006 (2006), pp. 1–19] to compare the interval-valued objectives calculated in each subregion and also to select the subregion containing the best interval objective value. The process is continued until the interval width for each variable in the accepted subregion is negligible and ultimately the global or close-to-global interval-valued optimal solution is obtained. The proposed technique has been evaluated numerically using a wide set of newly introduced univariate/multivariate test problems. Finally, to compare the computational results obtained by the proposed method, the graphical representation for some test problems is given.