广义拟可微函数的微分

本文给出一种广义拟可微函数类,它是Demyanov与Rlubinov(1980)意义下拟可微函数的推广,通过凸集类对的空间的某些理论,建立了这类广义拟可微函数的微分学理论,包括加法运算、数乘运算、乘法运算、除法运算、极大值运算,极小值运算以及复合运算的微分公式和中值定理。这些结果为广义拟可微类函数优化研究提供了基本工具．     

Convergence of the Gradient Projection Method for Generalized Convex Minimization

This paper develops convergence theory of the gradient projection method by Calamai and Moré (Math. Programming, vol. 39, 93–116, 1987) which, for minimizing a continuously differentiable optimization problem min{f(x) : x } where is a nonempty closed convex set, generates a sequence x_k+1 = P(x_k – _k f(x_k)) where the stepsize _k > 0 is chosen suitably. It is shown that, when f(x) is a pseudo-convex (quasi-convex) function, this method has strong convergence results: either x_k x^* and x^* is a minimizer (stationary point); or x_k arg min{f(x) : x } = , and f(x_k) inf{f(x) : x }.     

关于广义函数赋值问题的完善与积分问题

本文采用非标准分析方法，首先对广义函数的赋值问题进行了完善；接着界定了广义函数的积分；最后讨论了广义函数的广义连续性．     

Smooth Transformation of the Generalized Minimax Problem

We consider the generalized minimax problem, that is, the problem of minimizing a function (x)=F(g ₁(x),...,g _m(x)), where F is a smooth function and each g _i is the maximum of a finite number of smooth functions. We prove that, under suitable assumptions, it is possible to construct a continuously differentiable exact barrier function, whose minimizers yield the minimizers of the function . In this way, the nonsmooth original problem can be solved by usual minimization techniques for unconstrained differentiable functions.     

一类不可微函数的二阶广义方向导数

本文研究一类不可微函数Ｃｈａｎｅｙ意义下的二阶广义方向导数,并得到这一类不可微问题的二阶最优性条件。     

一类广义多项式互补问题的光滑化共轭梯度法

本文研究了一类广义多项式互补问题,在一定条件下,证明了其有唯一解.通过极大极小转化技术,将此类广义多项式互补问题转化为光滑化无约束优化问题进行求解,并提出了一种新的光滑化共轭梯度法.在一定假设条件下,证明了该方法的全局收敛性.最后相关的数值实验表明了算法可以有效求解广义多项式互补问题.     

On the Use of Augmented Lagrangians in the Solution of Generalized Semi-Infinite Min-Max Problems

We present an approach for the solution of a class of generalized semi-infinite optimization problems. Our approach uses augmented Lagrangians to transform generalized semi-infinite min-max problems into ordinary semi-infinite min-max problems, with the same set of local and global solutions as well as the same stationary points. Once the transformation is effected, the generalized semi-infinite min-max problems can be solved using any available semi-infinite optimization algorithm. We illustrate our approach with two numerical examples, one of which deals with structural design subject to reliability constraints.     

广义齐次函数和二体问题

讨论了齐次函数概念的推广,并应用于求解二体问题．     

Existence of exact penalty for optimization problems with mixed constraints in Banach spaces

In this paper we use the penalty approach in order to study constrained minimization problems in a Banach space with nonsmooth nonconvex mixed constraints. A penalty function is said to have the exact penalty property [J.-B. Hiriart-Urruty, C. Lemarechal, Convex Analysis and Minimization Algorithms, Springer, Berlin, 1993] if there is a penalty coefficient for which a solution of an unconstrained penalized problem is a solution of the corresponding constrained problem. In this paper we establish sufficient conditions for the exact penalty property.     

不可微函数的不变伪单调性与伪不变凸性

本文讨论不可微函数的强伪不变凸性和不变伪单调性之间的关系,还讨论了不可微函数的伪不变凸性与半严格预拟不变凸性.此外,还给出了一个集值映射的不变伪单调的等价定义.     

Optimality and Duality for A Class of Non-Smooth Generalized B-vex Programming

1.IntroductionItiswellknownthatconvexityplayanimportantroleinestablishingthesufficientoptimalityconditionsanddualitytheoremsforanonlinearprogrammingproblem.Severalclassoffunctionshavebeendefinedforthepurposeofweakeningthelimitationsofconvexity.Recent...     

一类广义凸函数的充要条件

本文给出了广义梯度的广义单调性定义,借助这些定义给出了一类广义凸函数的充要条件。     

梯度投影算子的广义陡度引理及其应用

在一般闭凸集上建立了梯度投影算子的广义陡度引理,利用它证明了几种松 弛搜索下梯度投影算法的全局收敛性、强收敛性以及若干良好的收敛性质.     

采用广义Curry线搜索的重新开始共轭梯度算法及其全局收敛性

本文对无约束最优化问题：ｍｉｎｆ（ｘ），ｘ∈Ｒ^ｎ，提出一种新的重新开始共轭梯度算法．该算法采用一类广义Ｃｕｒｒｙ线搜索原则，参数β_ｋ可在一个有限闭区间内选择，且允许β_ｋ取负值．在较弱的条件下证明了该算法的全局收敛性．     

Clarke Generalized Jacobian of the Projection onto the Cone of Positive Semidefinite Matrices

This paper studies the differentiability properties of the projection onto the cone of positive semidefinite matrices. In particular, the expression of the Clarke generalized Jacobian of the projection at any symmetric matrix is given.*Research supported by NSERC.     

Two Classes of Smooth Objective Penalty Functions for Constrained Problems

This research aims at solving constrained problems by providing two classes of objective penalty functions which respectively approach to a class of exact penalty functions smoothly. Meanwhile, the authors present two algorithms based on the two objective penalty functions, and give the conclusion that all of the cluster points of the two sequences generated by the two algorithms are the optimal points of the original problem. Furthermore, this article discusses that both of the two penalty functions are of well-condition. Finally, the authors report numerical results to show the applicability of the two presented algorithms.     

具有等式与不等式约束条件拟可微优化的Lagrange乘子型最优性条件

讨论了不等式约束优化问题中拟微分形式下Ｆｒｉｔｚ Ｊｏｈｎ必要条件与 Ｃｌａｒｋｅ广义梯度形式下Ｆｒｉｔｚ Ｊｏｈｎ必要条件的关系．在较弱条件下给出了具有等式与不等式约束条件的两个Ｌａｇｒａｎｇｅ乘子形式的最优性必要条件,在这两个条件中等式约束函数的拟微分和Ｃｌａｒｋｅ广义梯度分别被使用。     

一类亚纯函数的广义积分及其Cauchy主值

利用函数zp(-1相似文献   

Representative of Quasidifferentials and Its Formula for a Quasidifferentiable Function

The quasidifferential of a quasidifferentiable function in the sense of Demyanov and Rubinov is not uniquely defined. Xia proposed the notion of the kernelled quasidifferential, which is expected to be a representative for the equivalent class of quasidifferentials. In the 2-dimensional case, the existence of the kernelled quasidifferential was shown. In this paper, the existence of the kernelled quasidifferential in the n-dimensional space (n>2) is proved under the assumption that the Minkowski difference and the Demyanov difference of subdifferential and minus superdifferential coincide. In particular, given a quasidifferential, the kernelled quasidifferential can be formulated. Applications to two classes of generalized separable quasidifferentiable functions are developed. Mathematics Subject Classifications (2000) 49J52, 54C60, 90C26. This work was supported by Shanghai Education Committee (04EA01).     

求解无约束优化问题的新谱共轭梯度法及其收敛性

强Wolfe条件不能保证标准CD共轭梯度法全局收敛．本文通过建立新的共轭参数,提出无约束优化问题的一个新谱共轭梯度法,该方法在精确线搜索下与标准CD共轭梯度法等价,在标准wolfe线搜索下具有下降性和全局收敛性．初步的数值实验结果表明新方法是有效的,适合于求解非线性无约束优化问题．