线性规划标准型和整数线性规划最优解的两个注记

本文研究线性规划标准型的基本假设所蕴含的一些性质,并探讨整数线性规划最优解和其松弛问题最优解的关系.首先,分别讨论四种情形下线性规划最优解的性质,即无约束线性规划问题、仅有非负约束的线性规划问题、仅有等式约束的线性规划问题,以及标准线性规划问题系数矩阵的列向量有为零的情形等.然后,构造两族二维整数线性规划,其松弛问题的最优解与其(整数)最优解"相距甚远".     

基于区间数的证券组合投资模型研究

提出了证券组合投资的区间数线性规划模型.通过引入区间数线性规划问题中的目标函数优化水平α和约束水平β将目标函数和约束条件均为区间数的线性规划问题转化为确定型的线性规划问题.投资者可以根据自己的风险喜好程度和客观情况,对这两个参数做出不同的估计,从而得到相应情况下的有效投资方案,使证券组合投资决策更具柔性.最后通过实例分析说明了该模型的可行性.     

线性分式规划优化分析的元模型方法

1引言线性分式规划(LFP): min f(x)=(p~Tx α)/(q~Tx β) s.t. Ax=b (1) x≥0有着重要的应用背景,特别在经济管理中受到广泛关注．例如,以净收益率为优化目标函数的海洋运输问题;当价格系数为随机变量时,优化目标为获得满意的收益水平概率最大的资源分配问题等[11]．线性分式规划是一类特殊的非线性规划,除一般的非线性规划求解方法外,它还有一些特殊的专用算法．这里,我们要考虑的问题是;当右端资源约束向量在一定范围内(即L≤b≤U,L,U分别为b的下界和上界)变化时,目标函数的最优值如何变化?我们把这一问题称之为线性分式规划的优化分析．     

经典一维装箱问题近似算法的研究进展

自20世纪70年代开始,随着计算复杂性理论的建立,近似算法逐渐成为组合优化的重要研究方向。作为第一批研究对象,装箱问题引起了组合优化领域学者的极大关注。装箱问题模型简单、拓展性强,广泛出现在各种带容量约束的资源分配问题中。除了在物流装载和材料切割等方面愈来愈重要的应用外,装箱算法的任何理论突破都关乎到整个组合优化领域的发展。直到今天,对装箱问题近似算法的研究仍如火如荼。本文主要针对一维模型,简述若干经典Fit算法的发展历程,分析基于线性规划松弛的近似方案的主要思路,总结当前的研究现状并对未来的研究提供一些参考建议。     

经典一维装箱问题近似算法的研究进展

自20世纪70年代开始,随着计算复杂性理论的建立,近似算法逐渐成为组合优化的重要研究方向。作为第一批研究对象,装箱问题引起了组合优化领域学者的极大关注。装箱问题模型简单、拓展性强,广泛出现在各种带容量约束的资源分配问题中。除了在物流装载和材料切割等方面愈来愈重要的应用外,装箱算法的任何理论突破都关乎到整个组合优化领域的发展。直到今天,对装箱问题近似算法的研究仍如火如荼。本文主要针对一维模型,简述若干经典Fit算法的发展历程,分析基于线性规划松弛的近似方案的主要思路,总结当前的研究现状并对未来的研究提供一些参考建议。     

含有等式约束非线性规划的全局优化算法

针对含有多个等式约束的非线性规划问题,提出一个全局优化算法.该方法基于可行集策略把改进的模拟退火方法与确定的局部算法方法相结合.对算法的收敛性进行了证明,数值结果表明算法的有效性及正确性.     

求解标准二次优化问题的割平面算法

标准的二次优化问题是NP-hard问题,把该问题转化为半不定的线性规划问题,且提出了一个线性规划的割平面算法来求解这个半不定的线性规划问题,并给出了该算法的收敛性证明.     

求解约束优化问题的记忆梯度Goldstein-Lavintin-Polyak投影算法

给求解无约束规划问题的记忆梯度算法中的参数一个特殊取法,得到目标函数的记忆梯度G o ldste in-L av in tin-Po lyak投影下降方向,从而对凸约束的非线性规划问题构造了一个记忆梯度G o ldste in-L av in tin-Po lyak投影算法,并在一维精确步长搜索和去掉迭代点列有界的条件下,分析了算法的全局收敛性,得到了一些较为深刻的收敛性结果.同时给出了结合FR,PR,HS共轭梯度算法的记忆梯度G o ldste in-L av in tin-Po lyak投影算法,从而将经典共轭梯度算法推广用于求解凸约束的非线性规划问题.数值例子表明新算法比梯度投影算法有效.     

解非线性规划问题的非参数罚函数多目标正交遗传算法

对非线性规划问题的处理通常采用罚函数法,使用罚函数法的困难在于参数的选取.本文提出了一种解非线性规划问题非参数罚函数多目标正交遗传算法,对违反约束的个体进行动态的惩罚以保持群体中不可行解的一定比例,从而不但有效增加种群的多样性,而且避免了传统的过度惩罚缺陷,使群体更好地向最优解逼近.数据实验表明该算法对带约束的非线性规划问题求解是非常有效的.     

向量优化问题的近似解研究

向量优化是数学规划领域中十分重要的研究方向之一,其相关基础理论与基本方法的研究具有非常重要的理论意义与应用价值.近年来,关于近似解的定义及其性质研究已成为向量优化理论与方法研究的热点.现主要介绍国内学者,特别是我们团队在向量优化问题的各类近似解和统一解概念及其发展和各类近似解与统一解的性质研究方面取得的一些重要进展.最后,提出了与向量优化问题的近似解与统一解相关的一些公开问题.     

A linear programming-based optimization algorithm for solving nonlinear programming problems

In this paper a linear programming-based optimization algorithm called the Sequential Cutting Plane algorithm is presented. The main features of the algorithm are described, convergence to a Karush–Kuhn–Tucker stationary point is proved and numerical experience on some well-known test sets is showed. The algorithm is based on an earlier version for convex inequality constrained problems, but here the algorithm is extended to general continuously differentiable nonlinear programming problems containing both nonlinear inequality and equality constraints. A comparison with some existing solvers shows that the algorithm is competitive with these solvers. Thus, this new method based on solving linear programming subproblems is a good alternative method for solving nonlinear programming problems efficiently. The algorithm has been used as a subsolver in a mixed integer nonlinear programming algorithm where the linear problems provide lower bounds on the optimal solutions of the nonlinear programming subproblems in the branch and bound tree for convex, inequality constrained problems.     

线性无关假设如何影响非线性约束最优化算法

首先综述非线性约束最优化最近的一些进展. 首次定义了约束最优化算法的全局收敛性. 注意到最优性条件的精确性和算法近似性之间的差异, 并回顾等式约束最优化的原始的Newton 型算法框架, 即可理解为什么约束梯度的线性无关假设应该而且可以被弱化. 这些讨论被扩展到不等式约束最优化问题. 然后在没有线性无关假设条件下, 证明了一个使用精确罚函数和二阶校正技术的算法可具有超线性收敛性. 这些认知有助于接下来开发求解包括非线性半定规划和锥规划等约束最优化问题的更加有效的新算法.     

非线性最优化一个可行序列等式约束二次规划算法

本文针对非线性不等式约束优化问题,提出了一个新的可行序列等式约束二次规划算法．在每次迭代中,该算法只需求解三个相同规模且仅含等式约束的二次规划(必要时求解一个辅助的线性规划),因而其计算工作量较小．在一般的条件下,证明了算法具有全局收敛及超线性收敛性．数值实验表明算法是有效的．     

Sequential quadratic programming with a flexible step acceptance strategy

A new sequential quadratic programming (SQP) method for nonlinear inequality constrained optimization is proposed. The aim of this paper is to promote global convergence for SQP methods using a flexible step acceptance strategy which combines merit functions and filter techniques. Global convergence is proved under some reasonable assumptions and preliminary numerical results are reported.     

A sequential equality constrained quadratic programming algorithm for inequality constrained optimization

In this paper, the feasible type SQP method is improved. A new SQP algorithm is presented to solve the nonlinear inequality constrained optimization. As compared with the existing SQP methods, per single iteration, in order to obtain the search direction, it is only necessary to solve equality constrained quadratic programming subproblems and systems of linear equations. Under some suitable conditions, the global and superlinear convergence can be induced.     

基于增广Lagrange函数的RQP方法

Recursive quadratic programming is a family of techniques developd by Bartholomew-Biggs and other authors for solving nonlinear programming problems.This paperdescribes a new method for constrained optimization which obtains its search di-rections from a quadratic programming subproblem based on the well-known aug-mented Lagrangian function.It avoids the penalty parameter to tend to infinity.We employ the Fletcher‘s exact penalty function as a merit function and the use of an approximate directional derivative of the function that avoids the need toevaluate the second order derivatives of the problem functions.We prove that thealgorithm possesses global and superlinear convergence properties.At the sametime, numerical results are reported.     

A globally convergent trust region algorithm for optimization with general constraints and simple bounds

1.IntroductionInthispaper,weconsiderthefollowingnonlinearprogr~ngproblemwherec(x)=(c,(x),c2(2),',We(.))',i(x)andci(x)(i=1,2,',m)arerealfunctions*ThisworkissupPOrtedbytheNationalNaturalScienceFOundationofChinaandtheManagement,DecisionandinformationSystemLab,theChineseAcademyofSciences.definedinD={xEReIISx5u}.Weassumethath相似文献   

Variable Programming: A Generalized Minimax Problem. Part I: Models and Theory

In this two-part series of papers, a new generalized minimax optimization model, termed variable programming (VP), is developed to solve dynamically a class of multi-objective optimization problems with non-decomposable structure. It is demonstrated that such type of problems is more general than existing optimization models. In this part, the VP model is proposed first, and the relationship between variable programming and the general constrained nonlinear programming is established. To illustrate its practicality, problems on investment and the low-side-lobe conformal antenna array pattern synthesis to which VP can be appropriately applied are discussed for substantiation. Then, theoretical underpinnings of the VP problems are established. Difficulties in dealing with the VP problems are discussed. With some mild assumptions, the necessary conditions for the unconstrained VP problems with arbitrary and specific activated feasible sets are derived respectively. The necessary conditions for the corresponding constrained VP problems with the mild hypotheses are also examined. Whilst discussion in this part is concentrated on the formulation of the VP model and its theoretical underpinnings, construction of solution algorithms is discussed in Part II.This work was supported by the RGC grant CUHK 152/96H of the Hong Kong Research Grant Council.     

A SUPERLINEARLY CONVERGENT TRUST REGION ALGORITHM FOR LC^1 CONSTRAINED OPTIMIZATION PROBLEMS

In this paper, a new trust region algorithm for nonlinear equality constrained LC^1 optimization problems is given. It obtains a search direction at each iteration not by solving a quadratic programming subproblem with a trust region bound, but by solving a system of linear equations. Since the computational complexity of a QP-Problem is in general much larger than that of a system of linear equations, this method proposed in this paper may reduce the computational complexity and hence improve computational efficiency. Furthermore, it is proved under appropriate assumptions that this algorithm is globally and super-linearly convergent to a solution of the original problem. Some numerical examples are reported, showing the proposed algorithm can be beneficial from a computational point of view.     

Test examples for nonlinear programming codes

The increasing importance of nonlinear programming software requires an enlarged set of test examples. The purpose of this note is to point out how an interested mathematical programmer could obtain computer programs of more than 120 constrained nonlinear programming problems which have been used in the past to test and compare optimization codes.