拟凸函数的若干充分条件 (英)

本文提出了中点拟凸函数的概念,在可测函数空间中,给出了中点拟凸函数拟凸的若干个充分条件．     

关于局部极小值点为整体极小值点的函数的一点注记

本引进了一种广义严格拟凸函数的定义,给出了它的一个刻画,证明 了局部极小值点为整体极小值点的函数与本定义的广义严格拟凸函数的等价性。     

拟凸函数的一个充分条件

十多年来,广义凸函数的研究构成了数学规划研究的趋向之一。拟凸函数是数学规划中常见的一种广义凸函数类。然而,对于给定的一个函数,如何用二阶导数判别其广义凸性。现有文献中,见之不多。Ferland[1]等人在七十年代初用K阶加边行列式的方法曾经讨论过一个二次可微函数是拟凸函数的必要条件和充分条件。熟知,对于一个二次可微函数,二阶导数可以表征函数的凸性。本文根据微分方程的极值原理,给出一个二次可微函数是拟     

弧拟凸函数的性质

本文对文［１］中引入的弧拟凸函数的性质做了进一步研究，找出了弧拟凸性与严格弧拟凸性及下半连续性之间的联系。同时，还对拟线性函数的性质做了进一步研究。     

二阶可微广义凸函数的性质

本对二阶可微拟凸函数、曲凸函数、严格曲凸函数进行了讨论，给出了它们的一些性质。     

拟凸函数判别准则的一个注记

我们在上半连续的条件下,给出了拟凸函数的一个新的判别准则,即：凸集上的一个上半连续函数是拟凸的充分必要条件是这个函数是中间拟凸的。     

s-orliz拟凸函数的一些性质

我们给出了定义在线性空间中的s-orliz凸集上的s-orliz拟凸函数,并讨论了其部分性质.     

拟凸函数局部极小的整体性质

在最优化问题中,任一局部极小都是整体极小的函数是相当重要的一类函数。Zang与Ayriel在[1]中证明了函数的任一局部极小都是整体极小的充要条件是它的水平集映象为下半连续的。然而这一条件在很多情况下是难以验证的。因此研究某些具体函数类在怎样的条件下具备这种整体性是很有意义的了。事实上多年来许多作者在这方面做了不少有意义的工作,对最优化算法(特别是整体优化算法)的研究也产生了积极的推动作用。本文在半连续的假设下进一步揭示了拟凸、严格拟凸与局部极小的整体性之间的密切联系。同时我们还通过给出的下半连续和连续的拟凸函数的两个特征性质,导出了拟凸与产格拟凸的一个等价条件     

算子D~α及其刻划的亚纯p叶函数类

用算子Dα刻划了亚纯星象函数,亚纯凸象函数,亚纯近于凸函数和亚纯拟凸函数的新子类,建立了包含关系,讨论了这些类中函数积分算子的性质.     

关于与k-折对称点有关的近于凸和拟凸函数族的子族

本文引入了两类新的与k折对称点有关的近于凸和拟凸函数族的子族C(k)(λ,α,β)和QC(k)(λ,α,β).给出了这些函数族的系数不等式,积分表达式,包含和从属关系,卷积条件和覆盖定理.所得到的结果推广了一些相关文献的结果,并得到了一些新的结果.     

非线性规划的拟罚函数—强次可行方向法

本文首先提出非线性规划的拟Ｋｕｈｎ—Ｔｕｃｋｅｒ点和拟罚函数法的概念和思想，然后结合强次可行方向法思想给出问题的两个新型算法，称之为拟罚函数—强次可行方向法．证明了该算法收敛到原问题的拟Ｋｕｈｎ—Ｔｕｃｋｅｒ点．     

结合修正Curry-Altman步长搜索一个新的共轭梯度算法的收敛性

Conjugate gradient optimization algorithms depend on the search directions with different choices for the parameter in the search directions. In this note, conditions are given on the parameter in the conjugate gradient directions to ensure the descent property of the search directions. Global convergence of such a class of methods is discussed. It is shown that, using reverse modulus of continuity function and forcing function, the new method for solving unconstrained optimization can work for a continuously differentiable function with a modification of the Curry-Altman‘s step-size rule and a bounded level set. Combining PR method with our new method, PR method is modified to have global convergence property.Numerical experiments show that the new methods are efficient by comparing with FR conjugate gradient method.     

一类线性约束下不可微最优化问题的可行下降方法

本文给出了一类线性约束下不可微量优化问题的可行下降方法，这类问题的目标函数是凸函数和可微函数的合成函数，算法通过解系列二次规划寻找可行下降方向，新的迭代点由不精确线搜索产生，在较弱的条件下，我们证明了算法的全局收敛性     

The regularized feasible directions method for nonconvex optimization

This paper develops and studies a feasible directions approach for the minimization of a continuous function over linear constraints in which the update directions belong to a predetermined finite set spanning the feasible set. These directions are recurrently investigated in a cyclic semi-random order, where the stepsize of the update is determined via univariate optimization. We establish that any accumulation point of this optimization procedure is a stationary point of the problem, meaning that the directional derivative in any feasible direction is nonnegative. To assess and establish a rate of convergence, we develop a new optimality measure that acts as a proxy for the stationarity condition, and substantiate its role by showing that it is coherent with first-order conditions in specific scenarios. Finally we prove that our method enjoys a sublinear rate of convergence of this optimality measure in expectation.     

有效点、弱有效点和真有效点的锥刻画

利用集合在某点的相依切锥、法向锥和可行方向锥等研究向量优化问题的有效点、 弱有效点和真有效点的特征,对局部有效点、局部弱有效点和局部真有效点与集合的各 锥之间的关系作了刻画.     

Objective-derivative-free methods for constrained optimization

We propose feasible descent methods for constrained minimization that do not make explicit use of the derivative of the objective function. The methods iteratively sample the objective function value along a finite set of feasible search arcs and decrease the sampling stepsize if an improved objective function value is not sampled. The search arcs are obtained by projecting search direction rays onto the feasible set and the search directions are chosen such that a subset approximately generates the cone of first-order feasible variations at the current iterate. We show that these methods have desirable convergence properties under certain regularity assumptions on the constraints. In the case of linear constraints, the projections are redundant and the regularity assumptions hold automatically. Numerical experience with the methods in the linearly constrained case is reported. Received: November 12, 1999 / Accepted: April 6, 2001?Published online October 26, 2001     

Error Process Indexed by Bandwidth Matrices in Multivariate Local Linear Smoothing

We focus on nonparametric multivariate regression function estimation by locally weighted least squares. The asymptotic behavior for a sequence of error processes indexed by bandwidth matrices is derived. We discuss feasible data-driven consistent estimators minimizing asymptotic mean squared error or efficient estimators reducing asymptotic bias at points where opposite sign curvatures of the regression function are present in different directions.     

On the convexity of the multiplicative version of Karmarkar's potential function

Karmarkar's potential function is quasi-convex, but not convex. This note investigates the multiplicative version of the potential function, and shows that it is not necessarily convex in general, but is strictly convex when the corresponding feasible region is bounded. This implies that the multiplicative version of the potential function in Karmarkar's algorithm is convex, since it works on a simplex.     

Global optimization over a box via canonical dual function

In this paper, we study global concave optimization by the canonical dual function. A differential flow on the dual feasible space is introduced. We show that the flow reaches a global minimizer of the concave function over a box. An example is illustrated.     

Second-order conditions for extremum problems with nonregular equality constraints

Combining results of Avakov about tangent directions to equality constraints given by smooth operators with results of Ben-Tal and Zowe, we formulate a second-order theory for optimality in the sense of Dubovitskii-Milyutin which gives nontrivial conditions also in the case of equality constraints given by nonregular operators. Secondorder feasible and tangent directions are defined to construct conical approximations to inequality and equality constraints which within a single construction lead to first- and second-order conditions of optimality for the problem also in the nonregular case. The definitions of secondorder feasible and tangent directions given in this paper allow for reparametrizations of the approximating curves and give approximating sets which form cones. The main results of the paper are a theorem which states second-order necessary condition of optimality and several corollaries which treat special cases. In particular, the paper generalizes the Avakov result in the smooth case.This research was supported by NSF Grant DMS-91-009324, NSF Grant DMS-91-00043, SIUE Research Scholar Award and Fourth Quarter Fellowship, Summer 1992.