平方凸函数Hermite-Hadamard型不等式的改进

利用平方凸函数与凸函数的关系,证明了平方凸函数单侧导数的存在性和单调性,建立了平方凸函数与其单侧导数的不等式关系.在此基础上,给出平方凸函数定积分已有下界的改进和新的下界.给出由平方凸函数Hermite-Hadamard型不等式生成的差值的估计.     

调和平方s-凸函数及其Jensen型不等式

考虑函数的凸性及其广义凸性,提出了调和平方s-凸函数的概念,讨论了调和平方s-凸函数的判定方法及其运算性质,建立了调和平方s-凸函数的Jensen型不等式,并给出了其等价形式和推论.     

论双变量同构凸函数

在定义加权同构平均值的基础上将凸函数、几何凸函数、平方凸函数等理论统一为双变量同构凸函数理论,给出其统一的微分判别法则,并由此推导出不同类型的同构平均值间不等关系的简单判定法则.     

强预拟不变凸函数与强拟不变凸函数

本文提出了两类新的广义凸函数—强预拟不变凸函数与强拟不变凸函数.讨论了强预拟不变凸函数与强拟不变凸函数间的关系,强拟不变凸函数与强伪不变凸函数间的关系.研究了强预拟不变凸函数在多目标优化中的应用.     

半严格不变凸函数

文章在Banach空间中定义了一种新的广义凸函数—半严格不变凸函数.对于满足局部Lipschitz条件的半严格不变凸函数,得到了它的广义Clarke次微分性质.文中还讨论了半严格不变凸函数与不变凸函数及半严格预不变凸函数之间的关系,得到了半严格不变凸函数的一些性质.     

半严格不变凸函数

文章在Banach空间中定义了一种新的广义凸函数—半严格不变凸函数.对于满足局部Lipschitz条件的半严格不变凸函数,得到了它的广义Clarke次微分性质.文中还讨论了半严格不变凸函数与不变凸函数及半严格预不变凸函数之间的关系,得到了半严格不变凸函数的一些性质.     

GM-凸函数及其Jensen型不等式

作为对几何凸函数、GA-凸函数、GH-凸函数的推广,提出了GM-凸函数的概念,并研究了它的性质及其判定,进而建立了GM-凸函数的离散型Jensen不等式,并给出若干应用.     

模糊值m-凸函数的性质及其共轭问题的研究

基于m-凸函数提出了一类称为模糊值m-凸函数的新概念.首先,研究了模糊值m-凸函数的若干基本性质;其次,给出了模糊值m-凸函数的共轭函数的概念,并给出了模糊值m-凸函数在一定的条件下的共轭函数是模糊值m-凸函数等相关性质;最后,讨论了两个模糊值m-凸函数的共轭函数与其下卷积的共轭函数之间的相互关系.     

关于P-凸函数的积分型Jensen不等式

基于P-凸函数的函数凸性,研究了P-凸函数的Jensen型不等式的积分形式,通过定积分的定义计算,得到了P-凸函数的积分型Jensen不等式;利用P-凸函数的一个充要条件,建立了P-凸函数的积分型Jensen不等式的加权形式.     

强预拟不变凸函数的充要条件

本文引入了一类新的广义凸函数—强预拟不变凸函数.讨论了强预拟不变凸函数与预拟不变凸函数、严格预拟不变凸函数及半严格预拟不变凸函数之间的关系,得到它的三个充要条件:(i)当条件P_1满足时,f是强预拟不变凸函数的充分必要条件是f是预拟不变凸函数且f满足中间点强预拟不变凸性;(ii)当条件P_2满足时,f是强预拟不变凸函数的充分必要条件是f是严格预拟不变凸函数且f满足中间点强顶拟不变凸性;(iii)当条件P_2满足时,f是强预拟不变凸函数的充分必要条件是f是半严格预拟不变凸函数且f满足中间点强预拟不变凸性.     

近似凸集的某些性质

主要研究了两类近似凸集的关系和性质.首先,举例说明两类近似凸集没有相互包含关系.其次,在近似凸集(nearly convex)条件下,证明了在一定条件下函数上图是近似凸集与凸集的等价关系.同时,考虑了近似凸函数与函数上图是近似凸集的等价刻画、近似凸函数与函数水平集是近似凸集的必要性,并用例子说明近似凸函数与函数水平集是近似凸集的充分性不成立.最后,基于近似凸函数和拟凸函数的概念,给出了近似拟凸函数的概念并研究了近似拟凸函数与水平集是近似凸集的等价刻画.     

Solving Rectilinear Planar Location Problems with Barriers by a Polynomial Partitioning

This paper considers planar location problems with rectilinear distance and barriers where the objective function is any convex, nondecreasing function of distance. Such problems have a non-convex feasible region and a nonconvex objective function. Based on an equivalent problem with modified barriers, derived in a companion paper [3], the non convex feasible set is partitioned into a network and rectangular cells. The rectangular cells are further partitioned into a polynomial number of convex subcells, called convex domains, on which the distance function, and hence the objective function, is convex. Then the problem is solved over the network and convex domains for an optimal solution. Bounds are given that reduce the number of convex domains to be examined. The number of convex domains is bounded above by a polynomial in the size of the problem.     

On Some Generalized Polyhedral Convex Constructions

Generalized polyhedral convex sets, generalized polyhedral convex functions on locally convex Hausdorff topological vector spaces, and the related constructions such as sum of sets, sum of functions, directional derivative, infimal convolution, normal cone, conjugate function, subdifferential are studied thoroughly in this paper. Among other things, we show how a generalized polyhedral convex set can be characterized through the finiteness of the number of its faces. In addition, it is proved that the infimal convolution of a generalized polyhedral convex function and a polyhedral convex function is a polyhedral convex function. The obtained results can be applied to scalar optimization problems described by generalized polyhedral convex sets and generalized polyhedral convex functions.     

一类光滑凸规划的牛顿法

给出了一个求解一类光滑凸规划的算法,利用光滑精确乘子罚函数把一个光滑凸规划的极小化问题化为一个紧集上强凸函数的极小化问题,然后在给定的紧集上用牛顿法对这个强凸函数进行极小化．     

The Strong Continuity of Convex Functions

A convex function defined on an open convex set of a finite-dimensional space is known to be continuous at every point of this set. In fact, a convex function has a strengthened continuity property. The notion of strong continuity is introduced in this study to show that a convex function has this property. The proof is based on only the definition of convexity and Jensen’s inequality. The definition of strong continuity involves a constant (the constant of strong continuity). An unimprovable value of this constant is given in the case of convex functions. The constant of strong continuity depends, in particular, on the form of a norm introduced in the space of arguments of a convex function. The polyhedral norm is of particular interest. It is straightforward to calculate the constant of strong continuity when it is used. This requires a finite number of values of the convex function.     

关于半连续函数与凸函数的注记

在半连续前提下,给出凸函数和严格凸函数的不等式刻划．指出非空凸集上的半连续函数满足中间点凸性时,成为凸函数,满足中间点严格凸性时,成为严格凸函数．最后定义F—G广义凸函数和条件p1,p2等概念,列举若干满足条件p1,p2的数量函数和向量函数,并指出,对于F—G广义凸函数,在条件p1,p2及一定连续性条件下,可以得到类似结果．     

交替最小化算法求解强凸函数与弱凸函数和的极小值问题

交替最小化算法(简称AMA)最早由[SIAM J.Control Optim.,1991,29(1):119-138]提出,并能用于求解强凸函数与凸函数和的极小值问题.本文直接利用AMA算法来求解强凸函数与弱凸函数和的极小值问题.在强凸函数的模大于弱凸函数的模的假设下,我们证明了AMA生成的点列全局收敛到优化问题的解,并且若该优化问题中的某个函数是光滑函数时,AMA所生成的点列的收敛率是线性的.     

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.     

关于凸函数定义的几点思考

考察十余种国内通用的高等数学教材对凸函数定义的异同,论证凸函数的两种原始定义之间的关系,给出凸函数所满足的三个重要不等式,并证明凸函数在一元函数微分学范围内所满足的两个重要性质——连续性和单侧可导性。利用实分析中的Hausdorff极大定理给出满足第一种原始定义而不满足第二种原始定义的一个函数的例子．     

On iterative convexity of diffeomorphism

Abstract

A function f is said to be iteratively convex if f possesses convex iterative roots of all orders. We give several constructions of iteratively convex diffeomorphisms and explain the phenomenon that the non-existence of convex iterative roots is a typical property of convex functions. We show also that a slight perturbation of iteratively convex functions causes loss of iterative convexity. However, every convex function can be approximate by iteratively convex functions.