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1.
随机需求的车辆路线问题的新模型   总被引:7,自引:0,他引:7  
倪勤  袁健  刘晋 《运筹与管理》2001,10(3):74-79
本主要研究随机需求的VRP问题,其中服务需求量满足二项式分布,根据期望值的大小我们提出了在一条路线上理想最大服务点数的新概念,并在此基础上建立了三种VRP问题的新模型,由于允许服务失败两次和部分服务使得模糊能适应多种实际问题,以模拟退火思想为基础的两阶段方法经修正后用于解新模型并取得较好的数值结果,理论分析和数值结果表明,新模型较好地描述随机需求的VRP问题,并且容易求解。  相似文献
2.
In this paper, a new derivative free trust region method is developed basedon the conic interpolation model for the unconstrained optimization. The conic inter-polation model is built by means of the quadratic model function, the collinear scalingformula, quadratic approximation and interpolation. All the parameters in this model axedetermined by objective function interpolation condition. A new derivative free method isdeveloped based upon this model and the global convergence of this new method is provedwithout any information on gradient.  相似文献
3.
实用下料问题的评注   总被引:2,自引:1,他引:1  
本文简述2004年全国首届部分高校研究生数模竞赛B题的研究背景,并对B题参赛论文所涉及的思想与方法进行评注.  相似文献
4.
§1 引言 在无约束最优化问题中,一个形式简单,引人注目的方法是对称秩一校正方法,简称SR1方法,这个方法是1959年Davidon(参见[2])首次提出来的,以后它一直吸引着许多数学工作者对它进行研究和改进(参见[1],[8],[10])。 SR1方法具有一个重要的性质,即不需要寻查步骤便可获得二次终止性,这个重要  相似文献
5.
A subspace projected conjugate gradient method is proposed for solving large bound constrained quadratic programming. The conjugate gradient method is used to update the variables with indices outside of the active set, while the projected gradient method is used to update the active variables. At every iterative level, the search direction consists of two parts, one of which is a subspace trumcated Newton direction, another is a modified gradient direction. With the projected search the algorithm is suitable to large problems. The convergence of the method is proved and same numerical tests with dimensions ranging from 5000 to 20000 are given.  相似文献
6.
In this paper we report a sparse truncated Newton algorithm for handling large-scale simple bound nonlinear constrained minimixation problem. The truncated Newton method is used to update the variables with indices outside of the active set, while the projected gradient method is used to update the active variables. At each iterative level, the search direction consists of three parts, one of which is a subspace truncated Newton direction, the other two are subspace gradient and modified gradient directions. The subspace truncated Newton direction is obtained by solving a sparse system of linear equations. The global convergence and quadratic convergence rate of the algorithm are proved and some numerical tests are given.  相似文献
7.
本文提出了一个解不等式约束非线性规划问题有效方法.在这个方法中,考虑解一个等价Kuhn-Tucker条件的非线性方程组.这个方程组中NCP函数的使用消去了对应于不等式约束的Lagrange乘子的非负性.截断牛顿方法被用来解这个非线性方程组.为了保证全局收敛性,一个强健的损失函数被选为寻查函数,同时方法中插入修正最速下降方向.本文证明了方法的分Q-二阶收敛性,同时指出新方法可以有效地解稀疏大规模非线性规划问题。  相似文献
8.
This paper represents an inexact sequential quadratic programming (SQP) algorithm which can solve nonlinear programming (NLP) problems. An inexact solution of the quadratic programming subproblem is determined by a projection and contraction method such that only matrix-vector product is required. Some truncated criteria are chosen such that the algorithm is suitable to large scale NLP problem. The global convergence of the algorithm is proved.  相似文献
9.
1. IntroductionConsider the following linearly constrained nonlinear programming problemwhere x e R", A E Rmxn and f E C2. We are interested in the case when n and m arelarge and when the Hessian matrix of f is difficult to compute or is dense. It is ajssumed thatA is a matrix of full row rank and that the level set S(xo) = {x: f(x) 5 f(xo), Ax ~ b} isnonempty and compact.In the past few years j there were two kinds of methods for solving the large-scaleproblem (1.1). FOr the one kind, pr…  相似文献
10.
1.IntroductionIn[6],aQPFTHmethodwasproposedforsolvingthefollowingnonlinearprogrammingproblemwherefunctionsf:R"-- RIandgi:R"-- R',jeJaretwicecontinuouslydifferentiable.TheQPFTHalgorithmwasdevelopedforsolvingsparselarge-scaleproblem(l.l)andwastwo-stepQ-quadraticallyandR-quadraticallyconvergent(see[6]).Theglobalconvergenceofthisalgorithmisdiscussedindetailinthispaper.Forthefollowinginvestigationwerequiresomenotationsandassumptions.TheLagrangianofproblem(1.1)isdefinedbyFOundationofJiangs…  相似文献
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