一类推广后的Feigenbaum函数方程的光滑解

考虑一类推广后的Feigenbaum函数方程｛g（0）=1,-1≤g（x）≤1,x∈[-1,1],h（g（x））=g（g（h（x）））其中h（x）是[-1,1]上的递减光滑奇函数且满足h（0）=0,-1〈h’（x）〈0,z∈[-1,1]．利用构造性方法讨论上述方程的光滑解的存在性及唯一性．     

有界型凸函数

1.前言和预备知识 A.W.Goodman引进并研究了下面的函数族,设函数：■在单位园盘E={|z|<1}内正则单叶,用S表示该族。若在E内又是凸形的,自然f(E)是凸区域,在?f(E)上的曲率半径ρ∈[R_1,R_2],当0相似文献   

LC^1最优化问题的最优性条件

1 引言 LC~1最优化问题是一类非光滑最优化问题,它们广泛存在于运筹学的各种情形中.对于这些问题,其目标函数和约束函数一般不具有二阶可微性,但是它们是可微的,其导数是局部Lipschitz的.LC~1最优化问题的一般形式是 rminf(x) s.t.h_i(x)=0,i∈E,(1.1) g_i(x)≤0,j∈I, 其中,f:R~n→R,h:R~n→R~m,g:R~n→R~l是LC~1函数,即它们有局部Lipschitz导数,E={1,…,m},I={1,…,l}.从非线性互补问题、变分不等式和非线性规划中产生的不少问题可以形成 为非光滑方程,其中C~1条件(即连续可微条件)不成立,但LC条件(即局部Lipscchitz条件)成立,这些问题对应于LC~1最优化问题.[4],[6],[7]给出LC~1最优化问题的例子. 最优性条件对研究非光滑最优化是重要的.若干作者研究了非光滑优化的最优性条件问题,例如[1]、[2]、[4].在本文中我们将讨论LC~1最优化的最优性条件,它们包括:无约束LC~1最优化问题的二阶最优性条件和一般约束LC~1最优化问题的二阶最优性条件. 2 基本概念     

关于CM分担四个公共小函数的亚纯函数结论的改进

关于CM分担四个公共小函数的亚纯函数结论，我们在考虑重值的条件下，改进了李平和杨重骏^[2]的结论：设f(z)与g(z)为非常数亚纯函数，aj(z)(j=1,2,….4)为f(z)与g(z)的四个判别的小函数，若f(z)与g(z)满足Ek)(aj,f)=Ek)(aj,g),(j=1,2,3,4)且k(≥15）是正整数，则f(z)是g(z)的拟分式线性变换。即:存在f(z)与g(z)的四个小函数a(z),b(z),c(z),d(z),使得f=ag b/cg d(ad-bc≠0），（亦称Quasi-Mobuys变换）。     

对"条件x+y=1下1/xn+λ/yn(n≥2)的最小值"再探究

文[1]把解决问题(1):“已知z,Y∈R+且z+.y一1,求去+4j,的最小值’’的“]一z+y”的代换方法移植到问题(2):巳知z,Y∈R+且z+y一1,求壶+歹8的最小值’’时思此时z—i歹舞,y—i歹斋.路受阻后,提出在(≯1+多)( )中,括号内 。=应配上什么式子才能解出的疑问,由此利用文[1]中的(*)式和待定系数法探讨出了一般性的结论:“已知z,y E R+且z+y一1,若^>o,则当且仅当导一^南时,击+号("≥2)取得最小值为(1+A南)一十一. 笔者读后颇受启发,但在(去+io)( )中,括号内到底应配上什么式子呢?文[1]的一般性结论能否再推广?为此,本文再作些探究.1 大胆尝试,印…     

星象函数族的一个子族

Lee,sang Hua 等在文[1]中引入了带有非正系数的解析函数族 T 的一个子族,即满足下列条件的函数所构成的函数族 S(α,β,σ):f(z)=z-sum from n=2 to ∞α_nz~n(α_n≥0)且对所有z∈D={z:|z|<1}有|(zf′(z))/(f(z))-1|/|α(zf′(z))/(f(z))+(1-σ)|<β (1)(其中0≤α≤1,0<β≤1,0≤σ<1)。文[1]讨论了此类函数的系数界、偏差等极值性质。本文讨论一般情形:设 f(z)=z+sum from n=2 to ∞α_nz~n(α_n 为任意复数)在 D 内解析且满足不     

位移障碍下一个四阶变分不等式的某些强间断非协调元逼近

考虑[1]中四阶变分不等式问题:其中为非空闭凸集,而障碍函数φ∈C~2(Ω),φ<0,在?Ω上.关于解的性质,有下述结果:当Ω?R~2是具有光滑边界?Ω的有界凸区域且f∈L~2(Ω)时,问题(1)存在唯     

关于Gackstatter猜想

荟1.引言Gaekstatte:和Laine[‘1提出以下猜想:设a‘(z)(f=0,1,…,n一k)是亚纯函数,a,一,(:)等0.k是正整数满足1摇左(n一1,则方程。‘”=名a‘(:)。‘(1)‘毋有允许解,这里允许解是指。(二)为满足(1)的亚纯函数,且对所有,除去一个测度有限的r集有T(r,a‘)=0(T(r,。)). Ozawat“〕考虑了以上猜想,证明了以下定理: 定理A设a‘(二)f“0,1,2,3是亚纯函数,则方程(除非。,.二a3(。 a)3)。,”=兔。3十吼。“十。户十a。,。妻4,a。年。没有允许解. 设f和a均为亚纯函数,_旦T(r,a)“o〔T(r,f)),可能除去线性测度为有限的集合E,则称a(z)为f的小函数…     

有关星象函数的一族解析函数

本文分为两部分.第一部分讨论圆|z|<1中的解析函数 gλ(z)=λf(z)+(1—λ)zf′(z),其中0≤λ≤1,而f(z)适合利用Schwarz引理,对于gλ(z)的一些有关数量作了估值.第二部分研究 g(z)=1/2(f(z)+zf′(z))的开始多项式.对于某些星象函数f(z),求得g(z)的开始多项式的单叶半径、星象半径及凸象半径.     

关于近于凸函数类的某些子类

§1 引言设 f(z)在单位圆盘 E={z∶|z|<1}内解析,f(0)=1-f′(0)=0,其全体记作 A.用S~*,S~*(β)(β≤1),K 与 C 表示 A 的子类,类中函数在 E 内分别是星象的(关于原点),β级星象的,凸象的与近于凸的.函数 f(z)∈A 是β(β≤1)级预星象的(prestarxlike)当且仅当z/((1-z)~(2(1-β)))*f(z)∈S~*(β),若β<1;Re(f(z))/z>1/2(z∈E),若β=1,这里运算*表示两解析函数的 Hadamard 乘积(卷积).β级预星象函数类记作 R(β).显物 R(0)=K,R(1/2)=S~*(1/2).给定实数λ>-1,用 D~λ(z)=z/((1-z)~(λ+1))*f(z)定义算子 D~λ,这里 f(z)∈A.设 α≥0,0≤β<1,k 为正整数,又设解析函数 h(z)在 E 内是凸象单叶的,h(0)=1,Reh(z)>β     

Some kind of Pareto stationarity for multiobjective problems with equilibrium constraints

ABSTRACT

In this paper, we derive some optimality and stationarity conditions for a multiobjective problem with equilibrium constraints (MOPEC). In particular, under a generalized Guignard constraint qualification, we show that any locally Pareto optimal solution of MOPEC must satisfy the strong Pareto Kuhn-Tucker optimality conditions. We also prove that the generalized Guignard constraint qualification is the weakest constraint qualification for the strong Pareto Kuhn-Tucker optimality. Furthermore, under certain convexity or generalized convexity assumptions, we show that the strong Pareto Kuhn-Tucker optimality conditions are also sufficient for several popular locally Pareto-type optimality conditions for MOPEC.     

Global Optimality Conditions for Some Classes of Optimization Problems

We establish new necessary and sufficient optimality conditions for global optimization problems. In particular, we establish tractable optimality conditions for the problems of minimizing a weakly convex or concave function subject to standard constraints, such as box constraints, binary constraints, and simplex constraints. We also derive some new necessary and sufficient optimality conditions for quadratic optimization. Our main theoretical tool for establishing these optimality conditions is abstract convexity.     

Global optimality conditions for fixed charge quadratic programs

In this note, we establish sufficient and necessary global optimality conditions for fixed charge quadratic programming problem. The main theoretical tool for establishing these global optimality conditions is abstract convexity. The newly obtained sufficient condition extends the existing sufficient conditions. A numerical example is also provided to illustrate our optimality conditions.     

Weak sharp efficiency in multiobjective optimization

By using the generalized Fermat rule, the Mordukhovich subdifferential for maximum functions, the fuzzy sum rule for Fréchet subdifferentials and the sum rule for Mordukhovich subdifferentials, we establish a necessary optimality condition for the local weak sharp efficient solution of a constrained multiobjective optimization problem. Moreover, by employing the approximate projection theorem, and some appropriate convexity and affineness conditions, we also obtain some sufficient optimality conditions respectively for the local and global weak sharp efficient solutions of such a multiobjective optimization problem.     

Optimality conditions for nonsmooth mathematical programs with equilibrium constraints,using convexificators

In this paper, we deal with constraint qualifications, stationary concepts and optimality conditions for a nonsmooth mathematical program with equilibrium constraints (MPEC). The main tool in our study is the notion of convexificator. Using this notion, standard and MPEC Abadie and several other constraint qualifications are proposed and a comparison between them is presented. We also define nonsmooth stationary conditions based on the convexificators. In particular, we show that GS-stationary is the first-order optimality condition under generalized standard Abadie constraint qualification. Finally, sufficient conditions for global or local optimality are derived under some MPEC generalized convexity assumptions.     

向量值最优化问题的最优性条件与对偶性

本文我们首先给出一类向量值优化问题（VP）的正切锥真有效解的定义，在锥方向导数的假设下，讨论了一类单目标问题 的最优性必要条件；然后利用正切锥方向导数定义一类正切锥F－凸函数类，并给出了（VP）正切锥真有效解的充分性条件，最后我们亦讨论了（VP）在正切锥真有效解意义下的对偶性质。     

Nondifferentiable Minimax Fractional Programming Problems with (C, α, ρ, d)-Convexity

In this paper, we present necessary optimality conditions for nondifferentiable minimax fractional programming problems. A new concept of generalized convexity, called (C, α, ρ, d)-convexity, is introduced. We establish also sufficient optimality conditions for nondifferentiable minimax fractional programming problems from the viewpoint of the new generalized convexity. When the sufficient conditions are utilized, the corresponding duality theorems are derived for two types of dual programs. This research was partially supported by NSF and Air Force grants     

On Minimax Fractional Optimality and Duality with Generalized Convexity

In this paper, we establish several sufficient optimality conditions for a class of generalized minimax fractional programming. Based on the sufficient conditions, a new dual model is constructed and duality results are derived. Our study naturally unifies and extends some previously known results in the framework of generalized convexity and dual models. Mathematics Subject Classifications: 90C25, 90C32, 90C47.     

Generalized convexity and efficiency for non-regular multiobjective programming problems with inequality-type constraints

For multiobjective problems with inequality-type constraints the necessary conditions for efficient solutions are presented. These conditions are applied when the constraints do not necessarily satisfy any regularity assumptions, and they are based on the concept of 2-regularity introduced by Izmailov. In general, the necessary optimality conditions are not sufficient and the efficient solution set is not the same as the Karush-Kuhn-Tucker points set. So it is necessary to introduce generalized convexity notions. In the multiobjective non-regular case we give the notion of 2-KKT-pseudoinvex-II problems. This new concept of generalized convexity is both necessary and sufficient to guarantee the characterization of all efficient solutions based on the optimality conditions.     

Optimality conditions for vector optimization problems with non-cone constraints in image space

ABSTRACT

In this paper, we employ the image space analysis method to investigate a vector optimization problem with non-cone constraints. First, we use the linear and nonlinear separation techniques to establish Lagrange-type sufficient and necessary optimality conditions of the given problem under convexity assumptions and generalized Slater condition. Moreover, we give some characterizations of generalized Lagrange saddle points in image space without any convexity assumptions. Finally, we derive the vectorial penalization for the vector optimization problem with non-cone constraints by a general way.