双层规划问题的一种模式搜索Filter方法

双层规划在工程设计和经济管理中应用广泛,结合模式搜索方法和Filter方法提出了一种解决双层规划问题的算法—模式搜索Filter方法.算法以Filter法思想构造接受准则,以模式搜索提供迭代方向和步长,能够有效的解决一类双层规划问题.     

多目标决策问题及其求解方法研究

针对多目标决策问题的多目标最优化问题化为单目标最优化问题进行了研究.其主要方法有:理想点法、等级权重法、加权算术平均法、加权几何平均法、风险偏好系数法、乘除法、模糊规划法等.此外,还对多目标最大最小和多目标最小最大决策问题进行了处理.     

A new hybrid method for solving global optimization problem

In this paper we present a new hybrid method, called the SASP method. The purpose of this method is the hybridization of the simulated annealing (SA) with the descent method, where we estimate the gradient using simultaneous perturbation. Firstly, the new hybrid method finds a local minimum using the descent method, then SA is executed in order to escape from the currently discovered local minimum to a better one, from which the descent method restarts a new local search, and so on until convergence.The new hybrid method can be widely applied to a class of global optimization problems for continuous functions with constraints. Experiments on 30 benchmark functions, including high dimensional functions, show that the new method is able to find near optimal solutions efficiently. In addition, its performance as a viable optimization method is demonstrated by comparing it with other existing algorithms. Numerical results improve the robustness and efficiency of the method presented.     

自适应多重网格法与超松弛法的比较

多重网格法(Multiple Grid Method,简称M-G方法)是近年来出现的快速方法之一,本文在M-G方法中采用自适应控制层间转换的技术,并将自适应M-G方法与G-S迭代方法及SOR迭代方,法进行了比较。其计算结果表明,自适应M-G方法的计算量比G-S迭代及SOR迭代少得多,当M-G方法所用层数为4-6层,这种优越性就更加明显,且自适应M-G方法中选取控制参数有很大的灵活性。     

复合材料旋转壳非线性稳定性分析计算

利用前屈曲一致理论和能量变分法分析计算了复合材料旋转壳非线性稳定性.前屈曲应变-位移关系采用非线性的卡门方程,能量积分采用数值积分,用势能最小原理求解前屈曲位移和内力,提出了求解临界载荷的实用计算方法,用FORTRAN语言编制了相应的计算机程序,并给出了算例.     

A Hybrid Algorithm for the Non-Guillotine Cutting Problem

In this article, a meta-heuristic method to solve the non-guillotine cutting stock problem is proposed. The method is based on a combination between the basic principles of the constructive and evolutive methods. With an adequate management of the parameters involved, the method allows regulation of the solution quality to computational effort relationship. This method is applied to a particular case of cutting problems, with which the computational behaviors is evaluated. In fact, 1000 instances of the problem have been classified according to their combinatorial degree and then the efficiency and robustness of the method have been tested. The final results conclude that the proposed method generates an average error close to 2.18% with respect to optimal solutions. It has also been verified that the method yields solutions for all of the instances examined; something that has not been achieved with an exact constructive method, which was also implemented. Comparison of the running times demonstrates the superiority of the proposed method as compared with the exact method.     

The Impact of Equal Weighting of Low- and High-Confidence Observations on Robust Linear Regression Computations

Equal weighting of low- and high-confidence observations occurs for Huber, Talwar, and Barya weighting functions when Newton's method is used to solve robust linear regression problems. This leads to easy updates and/or downdates of existing matrix factorizations or easy computation of coefficient matrices in linear systems from previous ones. Thus Newton's method based on these functions has been shown to be computationally cheap. In this paper we show that a combination of Newton's method and an iterative method is a promising approach for solving robust linear regression problems. We show that Newton's method based on the Talwar function is an active set method. Further we show that it is possible to obtain improved estimates of the solution vector by combining a line search method like Newton's method with an active set method.This revised version was published online in October 2005 with corrections to the Cover Date.     

Adaptive discontinuous Galerkin methods in multiwavelets bases

We use a multiwavelet basis with the Discontinuous Galerkin (DG) method to produce a multi-scale DG method. We apply this Multiwavelet DG method to convection and convection-diffusion problems in multiple dimensions. Merging the DG method with multiwavelets allows the adaptivity in the DG method to be resolved through manipulation of multiwavelet coefficients rather than grid manipulation. Additionally, the Multiwavelet DG method is tested on non-linear equations in one dimension and on the cubed sphere.     

A partial inexact alternating direction method for structured variational inequalities

In this article, a new method is proposed for solving a class of structured variational inequalities (SVIs). The proposed method is referred to as the partial inexact proximal alternating direction (piPAD) method. In the method, two subproblems are solved independently. One is handled by an inexact proximal point method and the other is solved directly. This feature is the major difference between the proposed method and some existing alternating direction-like methods. The convergence of the piPAD method is proved. Two examples of the modern convex optimization problem arising from engineering and information sciences, which can be reformulated into the encountered SVIs, are presented to demonstrate the applicability of the piPAD method. Also, some preliminary numerical results are reported to validate the feasibility and efficiency of the piPAD method.     

多孔介质二相驱动问题一种修正特征有限元方法的迭代解法及其收敛性分析

0 引言 多孔介质二相驱动问题的数学模型是由压力方程与浓度方程组成的偏微分方程组的初边值问题.关于该问题的数值解问题,已有大量的文献.为了得到最优的L~2-模误差估计,好多方法用混合元方法解压力方程.我们知道,混合元法得到的方程组系数矩阵是非正定的,从而解混合元比解标准元要困难得多,虽然许多人研究了混合元方法的求解问题,但到目前为止,还没有看到令人满意的好的算法.为了避开对混合元的求解,著名学者T.F.Russell考虑了用标准有限元方法解压力方程,用特征有限元方法解浓度方程的求解方法及其迭代解法,对只有分子扩散的二相驱动问题得到了最优的L~2模误差估计,对有机械弥散的一般二相驱动问题得不到最优的L~2模误差估计,同时在收敛性证明中要求压力有限元空间的指数至少是二.     

Block diagonally preconditioned PIU methods of saddle point problem

For large sparse saddle point problems, we firstly introduce the block diagonally preconditioned Gauss-Seidl method (PBGS) which reduces to the GSOR method [Z.-Z. Bai, B.N. Parlett, Z.-Q. Wang, On generalized successive overrelaxation methods for augmented linear systems, Numer. Math. 102 (2005) 1-38] and PIU method [Z.-Z. Bai, Z.-Q. Wang, On parameterized inexact Uzawa methods for generalized saddle point problems, Linear Algebra Appl. 428 (2008) 2900-2932] when the preconditioners equal to different matrices, respectively. Then we generalize the PBGS method to the PPIU method and discuss the sufficient conditions such that the spectral radius of the PPIU method is much less than one. Furthermore, some rules are considered for choices of the preconditioners including the splitting method of the (1, 1) block matrix in the PIU method and numerical examples are given to show the superiority of the new method to the PIU method.     

Finding roots of a real polynomial simultaneously by means of Bairstow's method

Aberth's method for finding the roots of a polynomial was shown to be robust. However, complex arithmetic is needed in this method even if the polynomial is real, because it starts with complex initial approximations. A novel method is proposed for real polynomials that does not require any complex arithmetic within iterations. It is based on the observation that Aberth's method is a systematic use of Newton's method. The analogous technique is then applied to Bairstow's procedure in the proposed method. As a result, the method needs half the computations per iteration than Aberth's method. Numerical experiments showed that the new method exhibited a competitive overall performance for the test polynomials.     

Derivative-Free Filter Simulated Annealing Method for Constrained Continuous Global Optimization

In this paper, a simulated-annealing-based method called Filter Simulated Annealing (FSA) method is proposed to deal with the constrained global optimization problem. The considered problem is reformulated so as to take the form of optimizing two functions, the objective function and the constraint violation function. Then, the FSA method is applied to solve the reformulated problem. The FSA method invokes a multi-start diversification scheme in order to achieve an efficient exploration process. To deal with the considered problem, a filter-set-based procedure is built in the FSA structure. Finally, an intensification scheme is applied as a final stage of the proposed method in order to overcome the slow convergence of SA-based methods. The computational results obtained by the FSA method are promising and show a superior performance of the proposed method, which is a point-to-point method, against population-based methods.     

对流占优扩散方程的特征线楔形基无网格法

本文研究了一维对流占优扩散方程的初边值问题.利用特征线法与楔形基无网格法,获得了特征线楔形基无网格显格式与隐格式算法.数值实验表明算法具有精度高、计算简单等优点.     

On the limited memory BFGS method for large scale optimization

We study the numerical performance of a limited memory quasi-Newton method for large scale optimization, which we call the L-BFGS method. We compare its performance with that of the method developed by Buckley and LeNir (1985), which combines cycles of BFGS steps and conjugate direction steps. Our numerical tests indicate that the L-BFGS method is faster than the method of Buckley and LeNir, and is better able to use additional storage to accelerate convergence. We show that the L-BFGS method can be greatly accelerated by means of a simple scaling. We then compare the L-BFGS method with the partitioned quasi-Newton method of Griewank and Toint (1982a). The results show that, for some problems, the partitioned quasi-Newton method is clearly superior to the L-BFGS method. However we find that for other problems the L-BFGS method is very competitive due to its low iteration cost. We also study the convergence properties of the L-BFGS method, and prove global convergence on uniformly convex problems.This work was supported by the Applied Mathematical Sciences subprogram of the Office of Energy Research, U.S. Department of Energy, under contract DE-FG02-87ER25047, and by National Science Foundation Grant No. DCR-86-02071.     

用不确定德尔菲法预测GDP

德尔菲法是一种建立在专家意见基础上的预测评估方法.不确定统计是利用不确定理论收集和整理分析专家数据的一种统计方法,其中关键的一点是如何构造不确定变量的不确定分布.把德尔菲法和不确定统计相结合,就得到了一种估计不确定分布的新方法——不确定德尔菲法.对该方法的估计误差进行了改进,得到了一种预测GDP的新方法,并利用其预测邯郸市的生产总值(GDP).     

蒙特卡罗方法计算定积分的进一步讨论

介绍了蒙特卡罗方法计算定积分的原理和方法.给出了用蒙特卡罗方法计算定积分的一个简单证明,从而揭示了蒙特卡罗方法和定积分定义间的内在联系.针对蒙特卡罗方法收敛慢的特点,提出将蒙特卡罗方法与相应的数值计算方法相结合,提高计算结果的精度.此外,将蒙特卡罗方法推广到反常积分上去.     

一个解凸二次规划的预测-校正光滑化方法

本文为凸二次规划问题提出一个光滑型方法,它是Engelke和Kanzow提出的解线性规划的光滑化算法的推广。其主要思想是将二次规划的最优性K-T条件写成一个非线性非光滑方程组,并利用Newton型方法来解其光滑近似。本文的方法是预测-校正方法。在较弱的条件下,证明了算法的全局收敛性和超线性收敛性。     

高体积分数颗粒增强复合材料有效线性与非线性介电性质的研究

基于两球相互作用的近似解,提出了一种解析方法来预测颗粒增强复合材料的线性有效介电性质,该方法可以应用于颗粒体积分数较高(可达50%)的情况．利用割线方法还研究了该类复合材料的非线性有效性质．结果表明当外加电场较弱时该文提出的方法与Stroud和Hui的方法比较接近,当外场较强时与Yu和Hui的方法一致．     

无约束优化问题的对角稀疏拟牛顿法

对无约束优化问题提出了对角稀疏拟牛顿法,该算法采用了Armijo非精确线性搜索,并在每次迭代中利用对角矩阵近似拟牛顿法中的校正矩阵,使计算搜索方向的存贮量和工作量明显减少,为大型无约束优化问题的求解提供了新的思路．在通常的假设条件下,证明了算法的全局收敛性,线性收敛速度并分析了超线性收敛特征。数值实验表明算法比共轭梯度法有效,适于求解大型无约束优化问题．