一种解决不等式约束优化问题的光滑牛顿法

本通过引入松弛变量和Fischer函数把带有不等式约束优化问题的K-T条件转化为一个等价的非线性系统，并引入一参数μ，从而提出了一种新的光滑牛顿法。在适当的条件下，证明了算法的全局收敛性，并提供了数值结果。     

求解线性不等式约束优化问题的一类信赖域算法

本文对线性不等式约束的非线性规划问题提出了一类信赖域算法,证明了算法所产生的序列的任一聚点为Ｋｕｈｎ－Ｔｕｃｋｅｒ点,并讨论了子问题求解的有效集方法．     

求解变量带简单界约束的非线性规划问题的信赖域方法

1．引言。本文考虑下述变量带简单界约束的非线性规划问题：问题（1．1）不仅是实际应用中出现的简单的约束最优化问题，而且相当一部分最优化问题可以把变量限制在有意义的区间内181．因此，无论在理论方面还是在实际应用方面，都有必要研究此种问题．给出简便而且有效的算法．有些文章提出了一些特殊的方法．如011和［2］．14］及16］提出了一类信赖域方法，它们都借助于某种辅助点，证明了算法的全局收敛性．在收敛速度的分析方面，除要求在＊－T点满足严格互补松弛外，它们还要求另一个条件，即在每次迭代中，辅助点的有效约束必须在尝…     

不等式约束优化全局收敛的滤子线性方程组算法

设计了求解不等式约束非线性规划问题的一种新的滤子序列线性方程组算法,该算法每步迭代由减小约束违反度和目标函数值两部分构成.利用约束函数在某个中介点线性化的方法产生搜索方向.每步迭代仅需求解两个线性方程组,计算量较小.在一般条件下,证明了算法产生的无穷迭代点列所有聚点都是可行点并且所有聚点都是所求解问题的KKT点.     

求解不等式约束优化问题无严格互补松弛条件的QP-Free新算法

本文针对不等式约束优化问题,结合Facchinei-Fischer-Kanzow精确有效集识别技术,给出—个新的线性方程组与辅助方向相结合的可行下降算法.算法每步迭代只需求解一个降维的线性方程组或计算一次辅助方向,且获取辅助方向的投影矩阵只涉及近似有效约束集中的元素,问题规模大为减少,且当迭代次数充分大时,只需求解一个降维的线性方程组.无需严格互补松弛条件,算法全局且一步超线性收敛.     

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.     

求解非线性等式和不等式约束优化问题的三项记忆梯度广义投影算法

利用广义投影矩阵，对求解无约束规划的三项记忆梯度算法中的参数给一条件，确定它们的取值范围，以保证得到目标函数的三项记忆梯度广义投影下降方向，建立了求解非线性等式和不等式约束优化问题的三项记忆梯度广义投影算法，并证明了算法的收敛性．同时给出了结合FR，PR，HS共轭梯度参数的三项记忆梯度广义投影算法，从而将经典的共轭梯度算法推广用于求解约束规划问题．数值例子表明算法是有效的．     

框式约束凸二次规划问题的内点算法

In this paper,a primal-dual interior point algorithm for convex quadratic progromming problem with box constrains is presented.It can be started at any primal-dual interior feasible point.If the initial point is close to the central path,it becomes a central path-following alogorithm and requires a total of O(√nL)number of iterations,where L is the input length.     

不等式约束优化的非单调可行信赖域-SQP算法

本文讨论不等式约束优化问题,给出一个信赖域方法与SQP方法相结合的新的可行算法,算法中采用了压缩技术,使得QP子问题产生的搜索方向尽可能为可行方向,并且采用了高阶校正的方法来克服算法产生的Maratos效应现象.在适当的条件下,证明了算法的全局收敛性和超线性收敛性.数值结果表明算法是有效的.     

求解全局优化问题的填充函数算法

填充函数法是求解多变量、多极值函数全局优化问题的有效方法.这种方法的关键是构造填充函数.本文在无Lipschitz连续条件下,对一般无约束最优化问题提出了一类单参数填充函数.讨论了其填充性质,并设计了一个求解约束全局优化问题的填充函数算法,数值实验表明,算法是有效的.     

求解非线性规划的原始对偶内点法(英文)

In this paper, we propose a primal-dual interior point method for solving general constrained nonlinear programming problems. To avoid the situation that the algorithm we use may converge to a saddle point or a local maximum, we utilize a merit function to guide the iterates toward a local minimum. Especially, we add the parameter ε to the Newton system when calculating the decrease directions. The global convergence is achieved by the decrease of a merit function. Furthermore, the numerical results confirm that the algorithm can solve this kind of problems in an efficient way.     

Convergence Analysis of an Inexact Infeasible Interior Point Method for Semidefinite Programming

In this paper we present an extension to SDP of the well known infeasible Interior Point method for linear programming of Kojima, Megiddo and Mizuno (A primal-dual infeasible-interior-point algorithm for Linear Programming, Math. Progr., 1993). The extension developed here allows the use of inexact search directions; i.e., the linear systems defining the search directions can be solved with an accuracy that increases as the solution is approached. A convergence analysis is carried out and the global convergence of the method is proved.     

Quadratic Convergence of a Primal-Dual Interior Point Method for Degenerate Nonlinear Optimization Problems

Recently studies of numerical methods for degenerate nonlinear optimization problems have been attracted much attention. Several authors have discussed convergence properties without the linear independence constraint qualification and/or the strict complementarity condition. In this paper, we are concerned with quadratic convergence property of a primal-dual interior point method, in which Newton’s method is applied to the barrier KKT conditions. We assume that the second order sufficient condition and the linear independence of gradients of equality constraints hold at the solution, and that there exists a solution that satisfies the strict complementarity condition, and that multiplier iterates generated by our method for inequality constraints are uniformly bounded, which relaxes the linear independence constraint qualification. Uniform boundedness of multiplier iterates is satisfied if the Mangasarian-Fromovitz constraint qualification is assumed, for example. By using the stability theorem by Hager and Gowda (1999), and Wright (2001), the distance from the current point to the solution set is related to the residual of the KKT conditions.By controlling a barrier parameter and adopting a suitable line search procedure, we prove the quadratic convergence of the proposed algorithm.     

全局收敛的凸规划的原始—对偶不可行内点算法

本对一类凸规划提出了一个原始-对偶不可行内点算法,并证明了算法的全局收敛性。     

A SQP Method for Inequality Constrained Optimization

Abstract In this paper, a new SQP method for inequality constrained optimization is proposed and the globalconvergence is obtained under very mild conditions.     

盒子型区域上变分不等式的一个光滑牛顿法(英文)

The box constrained variational inequality problem can be reformulated as a nonsmooth equation by using median operator.In this paper,we present a smoothing Newton method for solving the box constrained variational inequality problem based on a new smoothing approximation function.The proposed algorithm is proved to be well defined and convergent globally under weaker conditions.     

基于代数等价变换下的凸二次规划不可行内点算法

基于代数等价变换和在KMM算法的框架基础上,在原始-对偶内点方法的牛顿方程里嵌入一种自调节功能.从而对凸二次规划提出了一种新的迭代方向的不可行内点算法,并证明了算法的全局收敛性.     

A Combined Homotopy Infeasible Interior-Point Method for Convex Nonlinear Programming

In this paper, on the basis of the logarithmic barrier function and KKT conditions , we propose a combined homotopy infeasible interior-point method (CHIIP) for convex nonlinear programming problems. For any convex nonlinear programming, without strict convexity for the logarithmic barrier function, we get different solutions of the convex programming in different cases by CHIIP method.