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。     

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.     

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．     

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。     

一种修正的求总极值的积分—水平集方法的实现算法收敛性

1978年,郑权等提出了一个积分型求总极值的概念性算法及Monte-Carlo随机投点的实现算法,给出了概念性算法的总极值存在的充分必要条件,但是其实现算法收敛性仍未解决,1986年,张连生等给出离散均值－水平集的实现算法,并证明了它的收敛性。本文给出修正的积分－水平集方法,用一致分布搂九值积分逼近水平集构造实现算法,并证明了算法的收敛性。     

非线性互补问题的水平值估计算法

本文研究非线性互补问题（NCP）的求解算法，先将NCP转化为约束全局优化问题（CGOP），然后直接移植求解问题（CGOP）的水平值估计算法^[4,5]来求解问题（NCP）．文章证明了算法对于NCP是收敛的，数值实验说明了算法的有效性．     

求解非线性互补问题的一个下降算法

在[1]中,Soldov将非线性互补问题等价地转化成一个带非负约束的优化问题,基于这种转化形式,我们给出了一种求解非线性互补问题的下降算法,在映射为强单调时,证明了算法的全局收敛性。     

解非线性互补问题的光滑牛顿方法

由于退化解会导致再生方程的奇异性,非线性互补问题的求解通常采用基于半光滑技术的广义牛顿法.基于2-正则性的概念,提出了一类利用光滑互补函数求解互补问题的光滑牛顿算法.算法采用积极集技术,能在解的附近估计出退化指标,并把原问题降阶为一个非奇异方程组,从而保证了迭代效率.算法具有整体收敛性和局部超线性收敛性,数值实验显示算法是有效的.     

求解非线性互补问题的一个非精确信赖域方法

本文研究了基于非线性互补问题的等价非光滑优化问题的非精确依赖域方法，利用非线性规划的理论和方法，在一定条件下，获得了该方法的全局收敛性结果．     

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。     

应用迭代法求解一类非线性问题

本文应用迭代法求解一类有限维非线性问题,该方法是求解线性问题的雅可比迭代法在非线性问题上的推广,且此迭代方法具有几何收敛性质.     

求解非线性互补问题的一个下降算法(英文)

在[1]中,Solodov将非线性互补问题等价地转化成一个带非负约束的优化问题.基于这种转化形式,我们给出了一种求解非线性互补问题的下降算法.在映射为强单调时,证明了算法的全局收敛性.     

A Regularization Newton Method for Solving Nonlinear Complementarity Problems

In this paper we construct a regularization Newton method for solving the nonlinear complementarity problem (NCP(F )) and analyze its convergence properties under the assumption that F is a P ₀ -function. We prove that every accumulation point of the sequence of iterates is a solution of NCP(F ) and that the sequence of iterates is bounded if the solution set of NCP(F ) is nonempty and bounded. Moreover, if F is a monotone and Lipschitz continuous function, we prove that the sequence of iterates is bounded if and only if the solution set of NCP(F ) is nonempty by setting , where is a parameter. If NCP(F) has a locally unique solution and satisfies a nonsingularity condition, then the convergence rate is superlinear (quadratic) without strict complementarity conditions. At each step, we only solve a linear system of equations. Numerical results are provided and further applications to other problems are discussed. Accepted 25 March 1998     

A Null Space Approach for Solving Nonlinear Complementarity Problems

In this work, null space techniques are employed to tackle nonlinear complementarity problems （NCPs）. NCP conditions are transform into a nonlinear programming problem, which is handled by null space algorithms, The NCP conditions are divided into two groups, Some equalities and inequalities in an NCP are treated as constraints, While other equalities and inequalities in an NCP are to be regarded as objective function. Two groups are all updated in every step. Null space approaches are extended to nonlinear complementarity problems. Two different solvers are employed for all NCP in an algorithm.     

求解无限维非线性互补问题的光滑化牛顿法

研究一类无限维非线性互补问题的光滑化牛顿法.借助于非线性互补函数,将无限维非线性互补问题转化为一个非光滑算子方程.构造光滑算子逼近非光滑算子,在光滑逼近算子满足方向可微相容性的条件下,证明了光滑化牛顿法具有超线性收敛性.     

NCP Functions Applied to Lagrangian Globalization for the Nonlinear Complementarity Problem

Based on NCP functions, we present a Lagrangian globalization (LG) algorithm model for solving the nonlinear complementarity problem. In particular, this algorithm model does not depend on some specific NCP function. Under several theoretical assumptions on NCP functions we prove that the algorithm model is well-defined and globally convergent. Several NCP functions applicable to the LG-method are analyzed in details and shown to satisfy these assumptions. Furthermore, we identify not only the properties of NCP functions which enable them to be used in the LG method but also their properties which enable the strict complementarity condition to be removed from the convergence conditions of the LG method. Moreover, we construct a new NCP function which possesses some favourable properties.     

带有P0函数的非线性互补问题的一个新的非内点连续算法

研究带有P₀函数的非线性互补问题. 基于一个新的光滑函数, 把问题近似成参数化的光滑方程组, 并且给出一个新的非内点连续算法. 所给算法在每步迭代只需要求解一个线性方程组和执行一次Armijo类型的线搜索. 在不需要严格互补条件的情况下, 证明了算法是全局收敛和超线性收敛的. 并且, 在一个较弱的条件下该算法具有局部二阶收敛性. 数值实验证实了算法的可行性和有效性.     

An NE/SQP Method for the Bounded Nonlinear Complementarity Problem

NE/SQP (Refs. 2–3) is a recent algorithm that has proven quite effective for solving the nonlinear complementarity problem (NCP). NE/SQP is robust in the sense that its direction-finding subproblems are always solvable; in addition, the convergence rate of this method is q-quadratic. In this note, we consider a generalized version of NE/SQP, as first described in Ref. 4, which is suitable for the bounded NCP. We extend the work in Ref. 4 by demonstrating a stronger convergence result and present numerical results on test problems.     

An Inner Approximation Method for Optimization over the Weakly Efficient Set

In this paper, we consider an optimization problem which aims to minimize a convex function over the weakly efficient set of a multiobjective programming problem. To solve such a problem, we propose an inner approximation algorithm, in which two kinds of convex subproblems are solved successively. These convex subproblems are fairly easy to solve and therefore the proposed algorithm is practically useful. The algorithm always terminates after finitely many iterations by compromising the weak efficiency to a multiobjective programming problem. Moreover, for a subproblem which is solved at each iteration of the algorithm, we suggest a procedure for eliminating redundant constraints.