等式约束优化问题的一类新的简单光滑精确罚函数

精确罚函数方法是求解优化问题的一类经典方法,传统的精确罚函数不可能既是简单的又是光滑的,这里简单的是指罚函数中不包含目标函数和约束函数的梯度信息。针对等式约束问题提出了不同与传统罚函数的一类新的简单光滑罚函数并证明了它是精确的。给出了以新的罚函数为基础的罚函数方法并用数值例子说明算法是可行的。     

低阶精确罚函数的一种光滑化逼近

本文对不等式约束优化问题给出了低阶精确罚函数的一种光滑化逼近.提出了通过搜索光滑化后的罚问题的全局解而得到原优化问题的近似全局解的算法.给出了几个数值例子以说明所提出的光滑化方法的有效性.     

简单光滑精确指数乘子罚函数

解决有约束非线性规划问题的一个基本方法足将之简化为无约束问题,比如罚函数法.其中精确罚函数法是通过解决某个无约束问题来获得原有约束问题的一个解.就经典的罚函数定义而言,简单精确罚函数是非光滑的,从而难以处理.作者提出一个简单光滑精确指数乘子罚函数,验证在二阶充分条件下它存在相应的超线性收敛率,并得到关于它的强弱对偶结果.     

约束优化问题的一类光滑罚算法的全局收敛特性

对约束优化问题给出了一类光滑罚算法.它是基于一类光滑逼近精确罚函数 l_p(p\in(0,1]) 的光滑函数 L_p 而提出的.在非常弱的条件下, 建立了算法的一个摄动定理, 导出了算法的全局收敛性.特别地, 在广义Mangasarian-Fromovitz约束规范假设下, 证明了当 p=1 时, 算法经过有限步迭代后, 所有迭代点都是原问题的可行解; p\in(0,1) 时,算法经过有限迭代后, 所有迭代点都是原问题可行解集的内点.     

低阶精确罚函数的一种二阶光滑逼近

给出了求解约束优化问题的低阶精确罚函数的一种二阶光滑逼近方法,证明了光滑后的罚优化问题的最优解是原约束优化问题的ε-近似最优解,基于光滑后的罚优化问题,提出了求解约束优化问题的一种新的算法,并证明了该算法的收敛性,数值例子表明该算法对于求解约束优化问题是有效的.     

基于二次函数光滑化逼近的修正低阶罚函数

针对不等式约束优化问题, 给出了通过二次函数对低阶精确罚函数进行光滑化逼近的两种函数形式, 得到修正的光滑罚函数. 证明了在一定条件下, 当罚参数充分大, 修正的光滑罚问题的全局最优解是原优化问题的全局最优解. 给出的两个数值例子说明了所提出的光滑化方法的有效性.     

非线性规划整体精确罚函数的一个新的充分条件

§1.引言 非线性规划的精确罚函数法,越来越引起人们的注意,后为它可以把一个带约束问题化为一个或有限个无约束问题。 关于精确罚函数的存在性的研究,大致可以分成两类:一类是局部精确罚函数,其中     

关于一类等式约束优化的简单光滑精确罚函数

本文给出了一类等式约束优化的简单光滑精确罚函数,该精确罚函数有别于传统罚函数,它是光滑的和简单的,即在该精确罚函数表达式中,不含有目标函数的梯度.     

带等式约束的光滑优化问题的一类新的精确罚函数

罚函数方法是将约束优化问题转化为无约束优化问题的主要方法之一. 不包含目标函数和约束函数梯度信息的罚函数, 称为简单罚函数. 对传统精确罚函数而言, 如果它是简单的就一定是非光滑的; 如果它是光滑的, 就一定不是简单的. 针对等式约束优化问题, 提出一类新的简单罚函数, 该罚函数通过增加一个新的变量来控制罚项. 证明了此罚函数的光滑性和精确性, 并给出了一种解决等式约束优化问题的罚函数算法. 数值结果表明, 该算法对于求解等式约束优化问题是可行的.     

关于约束极小化问题的一个新的简单精确罚函数

针对等式及不等式约束极小化问题,通过对原问题添加一个变量,给出一个新的简单精确罚函数,即在该精确罚函数表达式中,不含有目标函数及约束函数的梯度．在满足某些约束品性的条件下,可以证明:当罚参数充分大时,所给出的罚问题的局部极小点是原问题的局部极小点．     

An RQP algorithm using a differentiable exact penalty function for inequality constrained problems

In this paper we propose a recursive quadratic programming algorithm for nonlinear programming problems with inequality constraints that uses as merit function a differentiable exact penalty function. The algorithm incorporates an automatic adjustment rule for the selection of the penalty parameter and makes use of an Armijo-type line search procedure that avoids the need to evaluate second order derivatives of the problem functions. We prove that the algorithm possesses global and superlinear convergence properties. Numerical results are reported.     

An exact lower order penalty function and its smoothing in nonlinear programming

In this article, we consider a lower order penalty function and its ε-smoothing for an inequality constrained nonlinear programming problem. It is shown that any strict local minimum satisfying the second-order sufficiency condition for the original problem is a strict local minimum of the lower order penalty function with any positive penalty parameter. By using an ε-smoothing approximation to the lower order penalty function, we get a modified smooth global exact penalty function under mild assumptions.     

非线性约束优化的光滑化平方根罚函数

介绍一种非线性约束优化的不可微平方根罚函数,为这种非光滑罚函数提出了一个新的光滑化函数和对应的罚优化问题,获得了原问题与光滑化罚优化问题目标之间的误差估计. 基于这种罚函数,提出了一个算法和收敛性证明,数值例子表明算法对解决非线性约束优化具有有效性.     

An exact penalty function method with global convergence properties for nonlinear programming problems

In this paper a new continuously differentiable exact penalty function is introduced for the solution of nonlinear programming problems with compact feasible set. A distinguishing feature of the penalty function is that it is defined on a suitable bounded open set containing the feasible region and that it goes to infinity on the boundary of this set. This allows the construction of an implementable unconstrained minimization algorithm, whose global convergence towards Kuhn-Tucker points of the constrained problem can be established.     

On the role of continuously differentiable exact penalty functions in constrained global optimization

The aim of this paper is to show that the new continuously differentiable exact penalty functions recently proposed in literature can play an important role in the field of constrained global optimization. In fact they allow us to transfer ideas and results proposed in unconstrained global optimization to the constrained case.First, by drawing our inspiration from the unconstrained case and by using the strong exactness properties of a particular continuously differentiable penalty function, we propose a sufficient condition for a local constrained minimum point to be global.Then we show that every constrained local minimum point satisfying the second order sufficient conditions is an attraction point for a particular implementable minimization algorithm based on the considered penalty function. This result can be used to define new classes of global algorithms for the solution of general constrained global minimization problems. As an example, in this paper we describe a simulated annealing algorithm which produces a sequence of points converging in probability to a global minimum of the original constrained problem.     

On differentiable exact penalty functions

In this work, we study a differentiable exact penalty function for solving twice continuously differentiable inequality constrained optimization problems. Under certain assumptions on the parameters of the penalty function, we show the equivalence of the stationary points of this function and the Kuhn-Tucker points of the restricted problem as well as their extreme points. Numerical experiments are presented that corroborate the theory, and a rule is given for choosing the parameters of the penalty function.