广义凸多目标规划的Mond─Weir型对偶性

本文研究带不等式和等式约束的多目标规划的Ｍｏｎｄ－Ｗｅｉｒ型对偶性理论。在目标和约束是广义凸的假设下，证明了弱对偶定理、直接对偶定理以及逆对偶定理     

广义凸多目标规划的Mond—Weir型对偶性

本文研究带不等式和等式约束的多目标规划的Ｍｏｎｄ－Ｗｅｉｒ型对偶性理论。在目标和约束是广义凸的假设下，证明了弱对偶定理、直接对偶定理以及逆对偶定理。     

利用一个简单不等式解三角题

利用一个简单不等式解三角题２７６００５山东省临沂地区劳动技校孙振英定理若ａ、ｂ、ｃ、ｄ∈Ｒ，则当且仅当ａｄ＝ｂｅ时取等号．故定理得证．不等式（＊）与二维柯西不等式（ａ２＋ｂ２）（ｃ２＋ｄ２）≥（ａｃ＋ｃｄ）２结构类似，便于记忆，使用灵巧，应用广泛．本...     

Brown可表示定理及其应用

这篇综述从背景、证明方法和应用三方面,为Brown可表示定理及其对偶提供一个易于理解的版本;并通过Serre函子给出紧生成三角范畴之间伴随对的一种三分法.     

群体多目标最优化的Lagrange对偶性

本文建立了目标和约束为不对称的群体多目标最优化问题的Lagrange对偶规划，在问题的联合弱有效解意义下，得到群体多目标最优化Lagrange型的弱对偶定理、基本对偶定理、直接对偶定理和逆对偶定理。     

一类非光滑优化问题的最优性与对偶

本文研究了一类带等式和不等式约束的非光滑多目标优化问题,给出了该类问题的Karush-Kuhn-Tucker最优性必要条件和充分条件,建立了该类规划问题的一类混合对偶模型的弱对偶定理、强对偶定理、逆对偶定理、严格逆对偶定理和限制逆对偶定理.     

集合函数多目标规划的拉格朗日型对偶定理

本文在［１］的基础上．给出了集合函数多目标规划的拉格朗日型弱对偶定理，严格对偶定理和逆对偶定理．     

一类非光滑优化问题的最优性与对偶（英文）

本文研究了一类带等式和不等式约束的非光滑多目标优化问题,给出了该类问题的Karush-Kuhn-Tucker最优性必要条件和充分条件,建立了该类规划问题的一类混合对偶模型的弱对偶定理、强对偶定理、逆对偶定理、严格逆对偶定理和限制逆对偶定理.     

巧用韦达定理解二次函数压轴题

<正>一元二次方程根与系数的关系(即韦达定理)是初中数学的难点内容.其应用非常广泛,进入高中后尤为明显,在解决有关方程、代数、三角、解析几何等问题中都有着广泛而实际的应用.近几年,越来越多的地区非常重视对该定理的考查,在各地的中考或名校的模拟考试中,经常会出现融入韦达定理的压轴题.笔者经过研究,发现在二次函数的综合题中应用韦达定理还是有规律可循的,现精选几道试题和大家分享.     

一类三角题的复数解法

在中学数学里,我们研究了复数的代数式a+bi和三角式γ(cosθ+isinθ),复数的灵活性大,技巧性强,所以复数的应用很广泛,它除了是研究代数、几何的一个有效工具,利用复数的三角式研究三角,作用更是显著,特别是对于某些按常规很难处理甚至是无法处理的三角题,只要通过观摩和联想,运用复数的三角式常常可以简捷地合理地出奇制胜地解决。下面略举几例说明。     

一类非光滑规划问题的最优性和对偶

研究一类非光滑多目标规划问题,给出了该规划问题的三个最优性充分条件.同时,研究了该问题的对偶问题,给出了相应的弱对偶定理和强对偶定理.     

Steuer-appro ximationsprobleme und fenchel-rock afellar - dualität

In this paper linear control-approximation problems in normed spaces are considered. The control-approximation problems are transformed into several “fit” optimizaion problems by the aid of the Fenchell-Rockafellar duality theorem. Hitherto in the literature such optimization problems have been constructed in a different way. The duality theorem of Fenchel-Rockafellar enables a completely uniform treatment and provides known propositions and also new results.     

广义I型连通级小极大分式问题的对偶

在I型弧连通和广义I型弧连通假设下,建立了极大极小分式优化问题的对偶模型,并提出了弱对偶定理、强对偶定理和严格逆对偶定理.     

On duality theory in multiobjective programming

In this paper, we study different vector-valued Lagrangian functions and we develop a duality theory based upon these functions for nonlinear multiobjective programming problems. The saddle-point theorem and the duality theorem are derived for these problems under appropriate convexity assumptions. We also give some relationships between multiobjective optimizations and scalarized problems. A duality theory obtained by using the concept of vector-valued conjugate functions is discussed.The author is grateful to the reviewer for many valuable comments and helpful suggestions.     

A new proof of the strong duality theorem for semidefinite programming

Semidefinite programs are convex optimization problems arising in a wide variety of applications and are the extension of linear programming. Most methods for linear programming have been generalized to semidefinite programs. Just as in linear programming, duality theorem plays a basic and an important role in theory as well as in algorithmics. Based on the discretization method and convergence property, this paper proposes a new proof of the strong duality theorem for semidefinite programming, which is different from other common proofs and is more simple.     

A solvability theorem and minimax fractional programming

This paper presents a stable solvability theorem for general inequality systems under a local closedness condition. It is shown how this mild regularity condition can be characterized by the validity of the solvability theorem for all local perturbations. Based on this solvability theorem zero duality gap and stability are established for general minimax fractional programming problems.The research was initiated while the first named author was a visitor at the University of New South Wales and was completed while the second named author was a visitor at the Technische Hochschule Darmstadt.     

Robust Duality in Parametric Convex Optimization

Modelling of convex optimization in the face of data uncertainty often gives rise to families of parametric convex optimization problems. This motivates us to present, in this paper, a duality framework for a family of parametric convex optimization problems. By employing conjugate analysis, we present robust duality for the family of parametric problems by establishing strong duality between associated dual pair. We first show that robust duality holds whenever a constraint qualification holds. We then show that this constraint qualification is also necessary for robust duality in the sense that the constraint qualification holds if and only if robust duality holds for every linear perturbation of the objective function. As an application, we obtain a robust duality theorem for the best approximation problems with constraint data uncertainty under a strict feasibility condition.     

Surrogate duality for robust optimization

Robust optimization problems, which have uncertain data, are considered. We prove surrogate duality theorems for robust quasiconvex optimization problems and surrogate min–max duality theorems for robust convex optimization problems. We give necessary and sufficient constraint qualifications for surrogate duality and surrogate min–max duality, and show some examples at which such duality results are used effectively. Moreover, we obtain a surrogate duality theorem and a surrogate min–max duality theorem for semi-definite optimization problems in the face of data uncertainty.     

Robust duality for generalized convex programming problems under data uncertainty

In this paper we present a robust duality theory for generalized convex programming problems in the face of data uncertainty within the framework of robust optimization. We establish robust strong duality for an uncertain nonlinear programming primal problem and its uncertain Lagrangian dual by showing strong duality between the deterministic counterparts: robust counterpart of the primal model and the optimistic counterpart of its dual problem. A robust strong duality theorem is given whenever the Lagrangian function is convex. We provide classes of uncertain non-convex programming problems for which robust strong duality holds under a constraint qualification. In particular, we show that robust strong duality is guaranteed for non-convex quadratic programming problems with a single quadratic constraint with the spectral norm uncertainty under a generalized Slater condition. Numerical examples are given to illustrate the nature of robust duality for uncertain nonlinear programming problems. We further show that robust duality continues to hold under a weakened convexity condition.     

On the relationship between fenchel and Lagrange duality for optimization problems in general spaces

In this paper, the equivalence between a Fenchel and Lagrange duality theorem for optimization problems in dual pairs of real vector spaces is proved in a direct way.