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1.
讨论无约束极大极小(minimax)问题,基于积极集识别技术,结合摄动的序列二次规划(SQP)方法,建立问题的一个数值方法.在相当弱的条件下,算法具有弱全局收敛性,并对算法进行了初步的数值试验.  相似文献   

2.
求多目标优化问题Pareto最优解集的方法   总被引:1,自引:0,他引:1  
主要讨论了无约束多目标优化问题Pareto最优解集的求解方法,其中问题的目标函数是C1连续函数.给出了Pareto最优解集的一个充要条件,定义了α强有效解,并结合区间分析的方法,建立了求解无约束多目标优化问题Pareto最优解集的区间算法,理论分析和数值结果均表明该算法是可靠和有效的.  相似文献   

3.
研究线性等式约束连续型minimax问题,其中目标函数为Lipschitz连续函数,基于线性约束函数的区间迭代运算、区域二分原则和无解区域删除原则,建立了求解线性等式约束连续型minimax问题的区间算法,证明了算法的相关定理,给出了数值算例,该算法保证求出问题的整体解,且是可靠和有效的.  相似文献   

4.
带约束的变尺度算法   总被引:3,自引:0,他引:3  
迄今为止,变尺度算法是求解无约束最优化问题最有效的一类方法。因此,近年来,对约束最优化问题建立类似方法的工作。引起了许多优化工作者的兴趣,他们提出了Wilson-Han-Powell算法及其改进等等。并且证明在一定条件下,算法具有超线性的收敛率。但这些条件不仅要求很“高”,而且很难在计算前确定能否成立。文[4]利用文[1]和[2]的结果,提出一类新的算法,求解带线性等式约束条件的非线性规划问题。并且证明了算法的超线性收敛率。本文把这个结果推广到一般的约束规划问题:  相似文献   

5.
<正> 本文使用文[1]的方法,借助于双无限 Toeplitz 矩阵的一个基本定理,讨论确定在实轴上的双参数简单双曲样条~2H_Δ(x) 的存在唯一性问题和渐近性质.另外,按照文[2]的方法,也讨论确定在有限区间上的~2H_(?)(x) 的存在唯一性问题和渐近性质.  相似文献   

6.
对于非光滑的极小化问题,C.Lemaréchel在[1]中对凸函数的无约束极小化问题提示了一个高阶σ-牛顿型算法的思想,并讨论了某些性质。本文对[1]的高阶σ-牛顿型算法作了进一步研究,并提出一个概念性算法,证明了算法的全局收敛性。  相似文献   

7.
带约束的非线性L_1问题   总被引:1,自引:0,他引:1  
文[1]给出了无约束非线性L_1问题的最优性条件,文[2]以文[1]为基础又给出了只带不等式约束的非线性L_1问题的最优性条件。可是他们的推导都略嫌太繁,并且都还缺少二阶必要条件。本文的目的之一就是以较弱的条件对(P)给出通常的全部最优性条件,并在适当的假定下再给出一般问题(P)的一阶充分条件。本文的目的之二就是为带线性约束的非线性L_1问题给出一个算法,以温和的条件证明其收敛性。  相似文献   

8.
1 引言 考虑下列无约束非光滑优化问题 minf(x),(1) x∈R~n,其中f为R~n上的局部Lipschitz函数,本文将‖·‖_2简记为‖·‖.记下列信赖域子问题为S∪B(x,△). min m(x,s)=φ(x,s)+1/2s~TBs, 其中φ:R~(2m)→R为f的迭代函数。 对于无约束非光滑优化问题(1),[11],[13],[3]、[4]和[5]分别在特殊的条件下给出了信赖域算法用以求解(1)的收敛性结果。最近,[10]、[2]和[6]在不同的假设条件下分别给出了信赖域算法求解无约束非光滑优化问题的一般模型,并在子问题的目标函数满足局部一致有界性条件时证明了算法模型的整体收敛性。在目标函数满足某种正则性条件时,[11]和[9]给出了当信赖域子问题的目标函数中二次项不满足一致有界性条件时的收敛性结果.本文则在目标函数仅为局部Lipschitz函数时得到了和[8]、[11]、[9]相同的收敛性结果。  相似文献   

9.
影子价格与企业管理决策   总被引:2,自引:1,他引:1  
本文利用线性规划与非线性规划模型,讨论了目标函数增量,影子价格及相应的常数项增量的特征区间之间的关系,从理论上对文[1]、[2]、[3]中的问题作出了解释。我们还给出了线性规划与非线性规划发生悖论的充要条件,对文[5]、[7]中的结果进行了推广。  相似文献   

10.
刘金魁 《计算数学》2013,35(3):286-296
根据CG-DESCENT算法[1]的结构和Powell在综述文献[11]中的建议,给出了两种新的求解无约束优化问题的非线性共轭梯度算法. 它们在任意线搜索下都具有充分下降性质, 并在标准Wolfe线搜索下对一般函数能够保证全局收敛性. 通过对CUTEr函数库中部分著名的函数进行试验, 并借助著名的Dolan & Moré[2]评价方法, 展示了新算法的有效性.  相似文献   

11.
In the paper we investigate smoothing method for solving semi-infinite minimax problems. Not like most of the literature in semi-infinite minimax problems which are concerned with the continuous time version(i.e., the one dimensional semi-infinite minimax problems), the primary focus of this paper is on multi- dimensional semi-infinite minimax problems. The global error bounds of two smoothing approximations for the objective function are given and compared. It is proved that the smoothing approximation given in this paper can provide a better error bound than the existing one in literature.  相似文献   

12.
Many real life problems can be stated as a continuous minimax optimization problem. Well-known applications to engineering, finance, optics and other fields demonstrate the importance of having reliable methods to tackle continuous minimax problems. In this paper a new approach to the solution of continuous minimax problems over reals is introduced, using tools based on modal intervals. Continuous minimax problems, and global optimization as a particular case, are stated as the computation of semantic extensions of continuous functions, one of the key concepts of modal intervals. Modal intervals techniques allow to compute, in a guaranteed way, such semantic extensions by means of an efficient algorithm. Several examples illustrate the behavior of the algorithms in unconstrained and constrained minimax problems.  相似文献   

13.
Recently, Liu and Lou [On the equivalence of some approaches to the OWA operator and RIM quantifier determination, Fuzzy Sets and Systems 159 (2007) 1673-1688] investigated the equivalence of solutions to the minimum-variance and minimax disparity RIM quantifier problems. However, their proofs are very sensitive to the assumption, and some are mathematically incomplete. In this regard, this paper provides a counterexample of the minimax disparity RIM quantifier problem for the case in which generating functions are continuous. The paper also provides a correct proof of the minimax disparity RIM quantifier problem for the case in which generating functions are absolutely continuous and a generalized result for the minimum-variance RIM quantifier problem for the case in which generating functions are Lebesgue integrable. Based on the results, the paper provides a correct relationship between the minimum-variance and minimax disparity RIM quantifier problems.  相似文献   

14.
In 2013, a minimax method for finding saddle points of locally Lipschitz continuous functional was designed (Yao Math. Comp. 82 2087–2136 2013). The method can be applied to numerically solve hemivariational inequality for multiple solutions. Its subsequence and sequence convergence results in functional analysis were established in the same paper. But, since these convergence results do not consider discretization, they are not convergence results in numerical analysis. In this paper, we point out what approximation problem is, when this minimax method is used to solve hemivariational inequality and the finite element method is used in discretization. Computation of the approximation problem is discussed, numerical experiment is carried out and its global convergence is verified. Finally, as element size goes to zero, convergence of solutions of the approximation problem to solutions of hemivariational inequality is proved.  相似文献   

15.
In this paper a review of application of Bayesian approach to global and stochastic optimization of continuous multimodal functions is given. Advantages and disadvantages of Bayesian approach (average case analysis), comparing it with more usual minimax approach (worst case analysis) are discussed. New interactive version of software for global optimization is discussed. Practical multidimensional problems of global optimization are considered  相似文献   

16.
This paper proposes an efficient method for solving complex multicriterial optimization problems, for which the optimality criteria may be multiextremal and the calculations of the criteria values may be time-consuming. The approach involves reducing multicriterial problems to global optimization ones through minimax convolution of partial criteria, reducing dimensionality by using Peano curves and implementing efficient information-statistical methods for global optimization. To efficiently find the set of Pareto-optimal solutions, it is proposed to reuse all the search information obtained in the course of optimization. The results of computational experiments indicate that the proposed approach greatly reduces the computational complexity of solving multicriterial optimization problems.  相似文献   

17.
We propose an algorithm for the global optimization of three problem classes: generalized semi-infinite, continuous coupled minimax and bi-level problems. We make no convexity assumptions. For each problem class, we construct an oracle that decides whether a given objective value is achievable or not. If a given value is achievable, the oracle returns a point with a value better than or equal to the target. A binary search is then performed until the global optimum is obtained with the desired accuracy. This is achieved by solving a series of appropriate finite minimax and min-max-min problems to global optimality. We use Laplace’s smoothing technique and a simulated annealing approach for the solution of these problems. We present computational examples for all three problem classes.  相似文献   

18.
An extreme point property of optimal solutions of general concave programming problems is established that generalizes both Du-Hwang’s minimax theorem and its continuous version by Du and Pardalos.  相似文献   

19.
We consider minimax optimization problems where each term in the objective function is a continuous, strictly decreasing function of a single variable and the constraints are linear. We develop relaxation-based algorithms to solve such problems. At each iteration, a relaxed minimax problem is solved, providing either an optimal solution or a better lower bound. We develop a general methodology for such relaxation schemes for the minimax optimization problem. The feasibility tests and formulation of subsequent relaxed problems can be done by using Phase I of the Simplex method and the Farkas multipliers provided by the final Simplex tableau when the corresponding problem is infeasible. Such relaxation-based algorithms are particularly attractive when the minimax optimization problem exhibits additional structure. We explore special structures for which the relaxed problem is formulated as a minimax problem with knapsack type constraints; efficient algorithms exist to solve such problems. The relaxation schemes are also adapted to solve certain resource allocation problems with substitutable resources. There, instead of Phase I of the Simplex method, a max-flow algorithm is used to test feasibility and formulate new relaxed problems.Corresponding author.Work was partially done while visiting AT&T Bell Laboratories.  相似文献   

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