积分—水平集总极值算法的另一实现途径

在（１）中提出了一个积分－水平集求总极值的概念性算法及Ｍｏｎｔｅ－Ｃａｒｌｏ随机投点的实现途径，并在不少实际问题中得到了很好的应用。但这一实际算法的收敛性是个未解决的问题。本文给出了另一实现途径，并证明了收敛性。从而从理论上证明了这一实现算法一定能求到总极值和总极值点，数值试验结果也支持这一理论结果。     

一种修正的求约束总极值的积分-水平集方法

对于有约束的全局最优化问题,在Chew-Zheng的《Integral Global Optimization》和邬冬华等的《一种修正的求总极值的积分-水平集方法的实现算法收敛性》的基础上,给出一种修正的求约束总极值的积分-水平集方法,它同样具有修正的求总极值的积分-水平集方法的两个特点: 1) 每一步构造一个新函数,它与原目标函数具有相同的总极值; 2) 避免了郑权算法在一般情况下,由于水平集不易求得而造成难以求出水平集的困难．同时给出了其实现算法,并证明了算法的收敛性．     

修正积分水平集算法的一个实现算法及其收敛性证明

郑权等（1978）在“一个求总极值的方法”一文中给出了一个积分水平集求总极值的概念性算法及Monte-Carlo随机投点的实现算法,其收敛性一直未得以解决,本文在张连生,邬冬华等提出的修正算法的基础上,利用数论中一致分布佳点集列,给出了一个实现算法及全局收敛性的证明,为了提高算法的计算效率,文中对算法进行了并行化处理。     

求总极值问题的最优性条件

郑权提出了求总极值问题的积分－水平集的概念性算法,同时给出了最优性条件。本文提出了修正的积分－水平集算法,并且给出了类似的总极值存在的最优性条件。     

解非线性经补问题的积分水平集方法

本文考虑有约束的非线性互补问题的全局最优化问题,在文《Integral Global Optimization Method fro Solution of Nonlinear Complementarity problem》和《一的求总极值的积分－水平集方法》的基础上,给出了一种修正的求约束总极值的积分－水平集方法,它同样具有修正的求总极值的积分－水平集方法的两个特点：1）第一步需要构造一个新的函数,而且它与原目标函数具有相同的总极值;2）避免了郑权算法在一般情况下,由于水平集不易求得而造成难以求出水平的困难,并证明了算法的收敛性。     

用均值—水平集求多个总极值点的方法

在文（１）、（２）中给出了一个积分求总极值的概念性算法及其Ｍｏｎｔｅ－Ｃａｒｌｏ随机取点的实现途径。并在光学薄膜设计、透镜设计等领域中，取得了较好的应用效果，但其Ｍｏｎｔｅ－Ｃａｒｌｏ实现途径的全局收敛性是未解决问题。     

解非线性互补问题的积分水平集方法

本文考虑有约束的非线性互补问题的全局最优化问题,在文《IntegralGlobalOptimizationMethodforSolutionofNonlinearComplementarityproblem》和《一种修正的求总极值的积分一水平集方法》的基础上,给出了一种修正的求约束总极值的积分一水平集方法,它同样具有修正的求总极值的积分一水平集方法的两个特点：1）每一步需要构造一个新的函数,而且它与原目标函数具有相同的总极值;2）避免了郑权算法在一般情况下,由于水平集不易求得而造成难以求出水平的困难,并证明了算法的收敛性     

数学期望型水平值逼近全局最小值的方法

郑权在1978年提出的一种积分水平集算法概念性算法.由于水平集一般情况下难以求出,此算法通过Monte-Carlo随机取点来实现.本文提出了数学期望型水平值逼近全局最小值的概念性算法,它利用了相对熵主要思想,通过改变重要样本密度函数,克服了郑权算法水平集不易求得而难以求出水平值的困难.本文还给出了求全局最小值的收敛准则并证明了它的渐进收敛性.     

多目标最优化的一种积分型实现算法

在文[1]中给出了求解多目标最优化的一种积分总极值的概念性算法．本文利用数论中的一致分布佳点集列，较为简便的得出了多目标最优化的积分总极值的实现算法和算法终止准则．并经过有关函数数值计算表明该算法是有效的，可用来求解多目标最优化问题的有效解．     

一个求总极值的实现算法及其收敛性

1978年,郑权等首先提出了一种用积分─水平集求总极值的方法及用Monte－Carlo随机投点实现的实现其法,其实现算法是否收敛未解决的问题．本文提出一种用数论方法实现的实现算法,并证明了该实现其法是收敛的．初步的数值结果表明,该实现其法是较有效的．     

AN IMPLEMENTABLE ALGORITHM AND ITS CONVERGENCE FOR GLOBAL MINIMIZATION WITH CONSTRAINS

With the integral-level approach to global optimization, a class of discontinuous penalty functions is proposed to solve constrained minimization problems. In this paper we propose an implementable algorithm by means of the good point set of uniform distribution which conquers the default of Monte-Carlo method. At last we prove the convergence of the implementable algorithm.     

解非线性互补问题的约束积分水平集算法

本文考虑有约束的非线性互补问题的全局最优化问题,在文[1][5]的基础上,利用数论中一致分布佳点集列,给出了以数论方法代替Monte-Caclo投点的实现算法,并证明了所给实现算法的全局收敛性.最后所给出的两个数值例子表明本算法对求非线性互补问题是有效的.     

Projected subgradient techniques and viscosity methods for optimization with variational inequality constraints

In this paper, we propose an easily implementable algorithm in Hilbert spaces for solving some classical monotone variational inequality problem over the set of solutions of mixed variational inequalities. The proposed method combines two strategies: projected subgradient techniques and viscosity-type approximations. The involved stepsizes are controlled and a strong convergence theorem is established under very classical assumptions. Our algorithm can be applied for instance to some mathematical programs with complementarity constraints.     

Level bundle-like algorithms for convex optimization

We propose two restricted memory level bundle-like algorithms for minimizing a convex function over a convex set. If the memory is restricted to one linearization of the objective function, then both algorithms are variations of the projected subgradient method. The first algorithm, proposed in Hilbert space, is a conceptual one. It is shown to be strongly convergent to the solution that lies closest to the initial iterate. Furthermore, the entire sequence of iterates generated by the algorithm is contained in a ball with diameter equal to the distance between the initial point and the solution set. The second algorithm is an implementable version. It mimics as much as possible the conceptual one in order to resemble convergence properties. The implementable algorithm is validated by numerical results on several two-stage stochastic linear programs.     

Global convergence and stabilization of unconstrained minimization methods without derivatives

In this paper, acceptability criteria for the stepsize and global convergence conditions are established for unconstrained minimization methods employing only function values. On the basis of these results, the convergence of an implementable line search algorithm is proved and some global stabilization schemes are described.The authors would like to thank the anonymous referees for their useful suggestions.     

A forward scheme for backward SDEs

We introduce a forward scheme for simulating backward SDEs. Compared to existing schemes, ours avoids high order nestings of conditional expectations backwards in time. In this way the error, when approximating the conditional expectation, depending on the time partition, is significantly reduced. Besides this generic result, we present an implementable algorithm and prove its convergence. Finally, we demonstrate the strength of the new algorithm by solving a financial problem numerically.     

A cutting-plane method for quadratic semi infinite programming problems

A cutting plane algorithm for solving convex quadratic semi-infinite programming problems is presented. Nonbinding constraints can be dropped. Its arithmetic convergence rate is proved by taking into consideration the error of the approximate solution of the auxiliary problem to calculate the most violate constraint. An implementable variant of this method is described which is due to the adaptive discretization of the index set and its stability is shown. Computational experiments show the behaviour of the method.