清晰理解数学模型 合理探求线段最值

<正>笔者整理了近几年中考中关于"线段最值"的试题,归纳出三种数学模型.从"形"的角度构造"三角形两边之和大于第三边"和"垂线段最短"这两种几何模型,以及从"数"的角度建立函数模型.现举例加以分析.模型一、运用"三角形两边之和大于第三边"模型     

基于三阶和四阶龙格库塔法的GM(1,1)模型优化及应用

为提高GM(1,1)模型的预测精度,提出了应用三阶和四阶龙格库塔法对GM(1,1)模型的建模过程进行优化.给出了优化模型,并讨论了两种优化模型中发展系数的变化对其精度的影响.实例表明,基于三阶和四阶龙格库塔法所建立的GM(1,1)模型,可以有效地提高模型的预测精度和适用性.     

优化布点的TOPSIS法研究

针对多目标系统的优化布点问题,尝试提出用改进的TOPS IS法研究水质环境监测点位的优化,并建立改进的TOPS IS法优化模型.实例分析表明,该方法充分利用了优化矩阵的信息,优选结果符合客观实际,可用于环境监测点位的优化布点.     

对认字排积法里乘7的简记

读到黄冠斌、邵丽娟两同志的认字排积法介绍，其中对7乘所用辅律法，非常巧妙，又用趣味记法．以利速熟运用(见《珠算报》1993、12、1认字排积法介绍四)，对前数为1、2、7三个数的趣味记法以及4、6两数的看用，都处理甚好，但对其余几个数的记法，似嫌烦琐，笔者特提一点看法：拟将辅进数简称为基，按原列：     

一种基于修正二次模型的近似最优梯度法

梯度法因为其迭代形式简单、所需存储量小,在大规模无约束优化问题中得到了广泛的应用.基于修正的二次近似模型,利用修正的BFGS公式,提出了一个新的近似最优步长.用两个著名的BB步长对此步长进行截断,让其保持在两个BB步长之间.在适当的假设条件下,证明了该方法的全局收敛性.数值实验表明,方法优于一些现有的梯度法.     

机场新增卫星厅对登机口影响的评估方法

为了解决航站楼客流量饱和的问题,采用一种增加卫星厅的方法,实现了旅客分流.基于单目标整数线性规划和多目标优化的方法,分别构建了登机口优化分配网络模型和多目标优化模型.利用登机口优化分配网络算法筛选出所使用的共同登机口,建立了目标函数并列出约束条件,采用目标约束法对建立的模型进行求解.在此基础上,根据目标建模的思想,建立了可供中转旅客总体流程时间最短且使用登机口数量最小的航班-登机口分配模型.利用MATLAB计算可知,利用42个登机口即可实现303架航班的正常运转.     

解线性约束优化问题的新锥模型信赖域法

本文提出了一个解线性等式约束优化问题的新锥模型信赖域方法.论文采用零空间技术消除了新锥模型子问题中的线性等式约束,用折线法求解转换后的子问题,并给出了解线性等式约束优化问题的信赖域方法.论文提出并证明了该方法的全局收敛性,并给出了该方法解线性等式约束优化问题的数值实验.理论和数值实验结果表明新锥模型信赖域方法是有效的,这给出了用新锥模型进一步研究非线性优化的基础.     

一类四阶变系数振动问题的复合解

以一类斜拉索模型为背景,讨论了一类四阶变系数振动问题.用匹配法导出了方程的复合解,从而较简捷地得出了一类斜拉索的振幅的渐近表达式,并为解决相关类型的变系数问题提供了一种有效的方法.     

露天矿生产的车辆调度模型

讨论露天矿生产的车辆调度问题,关键是如何分配和调度现有条件下的电铲和卡车,从而得到一个好的生产计划.通过对问题的进一步分析和合理的简化,根据总定量最小原则及产量最大原则分别建立了相应的动态规划模型.由于模型在解决具体问题过程中的复杂程度和变量、不等式约束条件的个数太多等原因,我们采用逐步优化法对模型作了进一步的改进,然后用LINGO、LINDO等数学软件编程进行模型求解,得到了根据总定量最小原则及产量最大原则下的计划方案.     

控制点优化磨光算法在长江径流量预测中的应用

盈亏修正磨光法所得到的逼近效果仍然很差,通过控制点的参数优化和目标函数的最小,提出一种控制点优化磨光算法,利用这个算法得到参数后代入模型,使预测的精度得到提高.通过实例,该算法简单易行,并通过相对误差进行了分析,控制点优化磨光算法所得到的预测值好于神经网络模型、PPAR和小波网络模型的预测值,这为研究磨光法提供了较好的分析方法.     

Optimality criteria coupled adaptive mesh method for optimal shape design of Stokes flow

An adaptive mesh method combined with the optimality criteria algorithm is applied to optimal shape design problems of fluid dynamics. The shape sensitivity analysis of the cost functional is derived. The optimization problem is solved by a simple but robust optimality criteria algorithm, and an automatic local adaptive mesh refinement method is proposed. The mesh adaptation, with an indicator based on the material distribution information, is itself shown as a shape or topology optimization problem. Taking advantages of this algorithm, the optimal shape design problem concerning fluid flow can be solved with higher resolution of the interface and a minimum of additional expense. Details on the optimization procedure are provided. Numerical results for two benchmark topology optimization problems are provided and compared with those obtained by other methods. Copyright © 2016 John Wiley & Sons, Ltd.     

Adaptive mesh method for topology optimization of fluid flow

In order to solve the topology optimization problems of fluid flow and obtain higher resolution of the interface with a minimum of additional expense, an automatic local adaptive mesh refinement method is proposed. The optimization problem is solved by a simple but robust optimality criteria (OC) algorithm. A material distribution information based adaptive mesh method is adopted during the optimization process. The optimization procedure is provided and verified with several benchmark examples.     

Driver Behaviour at Unlit Non-urban T-Junctions in Daylight and Darkness

Observations on driver gap acceptance at rural T-junctions without road lighting in daylight and darkness are reported. It is found that drivers crossing the near side main road stream and merging with the far side stream are more cautious in darkness when the far side gap is large. There are no significant differences between behaviour in daylight and darkness in the simple crossing and merging manoeuvres. This contrasts with earlier observations of merging at T-junctions with road lighting.Literature on the effects of street lighting is briefly reviewed.     

A new shrinking gradient-like projection method for equilibrium problems

The paper proposes a new shrinking gradient-like projection method for solving equilibrium problems. The algorithm combines the generalized gradient-like projection method with the monotone hybrid method. Only one optimization program is solved onto the feasible set at each iteration in our algorithm without any extra-step dealing with the feasible set. The absence of an optimization problem in the algorithm is explained by constructing slightly different cutting-halfspace in the monotone hybrid method. Theorem of strong convergence is established under standard assumptions imposed on equilibrium bifunctions. An application of the proposed algorithm to multivalued variational inequality problems (MVIP) is presented. Finally, another algorithm is introduced for MVIPs in which we only use a value of main operator at the current approximation to construct the next approximation. Some preliminary numerical experiments are implemented to illustrate the convergence and computational performance of our algorithms over others.     

Riemannian Newton-type methods for joint diagonalization on the Stiefel manifold with application to independent component analysis

The joint approximate diagonalization of non-commuting symmetric matrices is an important process in independent component analysis. This problem can be formulated as an optimization problem on the Stiefel manifold that can be solved using Riemannian optimization techniques. Among the available optimization techniques, this study utilizes the Riemannian Newton’s method for the joint diagonalization problem on the Stiefel manifold, which has quadratic convergence. In particular, the resultant Newton’s equation can be effectively solved by means of the Kronecker product and the vec and veck operators, which reduce the dimension of the equation to that of the Stiefel manifold. Numerical experiments are performed to show that the proposed method improves the accuracy of the approximate solution to this problem. The proposed method is also applied to independent component analysis for the image separation problem. The proposed Newton method further leads to a novel and fast Riemannian trust-region Newton method for the joint diagonalization problem.     

一类非线性两级整数规划问题的全局优化方法

本文研究了整数规划连续化的途径,对一类非线性两级整数规划问题的上级规划连续化以后采用模拟退火算法;其对应的下级规划问题采用离散搜索法求解,从而给出了求解一类非线性两级整数规划问题的一种全局优化算法,并通过算例验证了该算法是有效的.     

Shape Optimization and Fictitious Domain Approach for Solving Free Boundary Problems of Bernoulli Type

This contribution deals with an efficient method for the numerical realization of the exterior and interior Bernoulli free boundary problems. It is based on a shape optimization approach. The state problems are solved by a fictitious domain solver using boundary Lagrange multipliers.     

求解一类约束优化信赖域方法的子问题

１引言设Ｈ为一给定的ｎ×ｎ对称矩阵,ｃＲ＂,本文考虑如｝的约束优化问题这里ａ＞０为给定的参数,Ｃ＝｛ｘＲｎｘ＜ａ是Ｒ”中的一个球体,Ｋ是一个简单凸闭集．当Ｋ＝Ｒｎ时,问题（Ｐ）便是无约束优化的信赖域子问题．当Ｋ＝｛ｘＲｎμ≤ｘ≤υ５,（μ１,μ２,…,μｎ）Ｔ,υ＝（υ１,υ２…,υｎ）Ｔ,且—∞＜μｉ＜υｉ＜ｖ＜＋∞,ｉ＝１,２,…,ｎ时,问题（Ｐ）便是用信赖域方法求解带上下界约束的优化问题时遇到的子问题．对于无约束信赖域方法的子问题已经有了比较成熟的算法［８,１２－１３,１５－１６］．Ｋ＝Ｒ…     

A new filled function method for nonlinear equations

In this paper, a new global optimization approach based on the filled function method is proposed for solving box-constrained systems of nonlinear equations. We first convert the nonlinear system into an equivalent global optimization problem, and then propose a new filled function method to solve the converted global optimization problem. Several numerical examples are presented and solved by using different local minimization methods, which illustrate the efficiency of the present approach.     

On the acceleration of the double smoothing technique for unconstrained convex optimization problems

In this article, we investigate the possibilities of accelerating the double smoothing (DS) technique when solving unconstrained nondifferentiable convex optimization problems. This approach relies on the regularization in two steps of the Fenchel dual problem associated with the problem to be solved into an optimization problem having a differentiable strongly convex objective function with Lipschitz continuous gradient. The doubly regularized dual problem is then solved via a fast gradient method. The aim of this article is to show how the properties of the functions in the objective of the primal problem influence the implementation of the DS approach and its rate of convergence. The theoretical results are applied to linear inverse problems by making use of different regularization functionals.