弧连通锥-凸数学规划的最优性条件

在弧连通锥-凸假设下讨论Hausdorff局部凸空间中的一类数学规划的最优性条件问题.首先,利用择一定理得到了锥约束标量优化问题的一个必要最优性条件.其次,利用凸集分离定理证明了无约束向量优化问题关于弱极小元的标量化定理和一个一致的充分必要条件.所得结果深化和丰富了最优化理论及其应用的内容.     

Multifunction Optimization Problems Involving Parameters: Necessary Optimality Conditions

We prove the Fritz John and Kuhn-Tucker necessary optimality conditions for vector optimization problems involving multifunctions and parameters under relaxed assumptions.     

Generalized Semi-Infinite Programming: Optimality Conditions Involving Reverse Convex Problems

This article deals with a generalized semi-infinite programming problem (S). Under appropriate assumptions, for such a problem we give necessary and sufficient optimality conditions via reverse convex problems. In particular, a necessary and sufficient optimality condition reduces the problem (S) to a min-max problem constrained with compact convex linked constraints.     

Optimality Conditions and Duality Involving Arcwise Connected and Generalized Arcwise Connected Functions

Some properties of arcwise connected functions in terms of their directional derivatives are investigated. These properties are then utilized to establish necessary and sufficient optimality conditions for scalar-valued nonlinear programming problems. Mond–Weir type duality results are also proved.     

Conditions for Global Optimality 2

In this paper bearing the same title as our earlier survey-paper [11] we pursue the goal of characterizing the global solutions of an optimization problem, i.e. getting at necessary and sufficient conditions for a feasible point to be a global minimizer (or maximizer) of the objective function. We emphasize nonconvex optimization problems presenting some specific structures like convex-anticonvex ones or quadratic ones.     

区间规划问题的最优性条件

区间规划是带有区间参数的规划问题,是一种更易于求解实际问题的柔性规划。它是确定性优化问题的延伸,有区间线性规划和区间非线性规划两种形式。本文讨论了目标函数是区间函数的区间非线性问题。给出了区间规划问题最优性必要条件的较简单证明方法,并利用LU最优解的概念,在一类广义凸函数－(p,r)－ρ－(η,θ)－不变凸函数定义下讨论了最优性充分条件。     

Optimality Conditions for Mechanical Impacts

In this report the necessary conditions for optimal non‐smooth control of rigid body mechanical systems with multiple impacts will be stated in accordance with Pontryagin's Minimum Principle. Criteria based on the Weierstrass‐Erdmann (WE) conditions and contemporary impact theory will be established in order to assess the optimality of an impact.The determination of post‐impact state and costate values from the pre‐impact values will be possible in some cases. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Multiple Blocking Sets and Arcs in Finite Planes

This paper contains two main results relating to the size ofa multiple blocking set in PG(2, q). The first gives a verygeneral lower bound, the second a much better lower bound forprime planes. The latter is used to consider maximum sizes of(k, n)-arcs in PG(2, 11) and PG(2, 13), some of which are determined.In addition, a summary is given of the value of m_n(2, q) forq 13.     

Global Optimality Conditions for Nonconvex Optimization

In this paper we give an analytical equivalent for the inclusion of a set to the Lebesque set of a convex function. Using this results, we obtain global optimality conditions (GOC) related to classical optimization theory for convex maximization and reverse-convex optimization. Several examples illustrate the effectiveness of these optimality conditions allowing to escape from stationary points and local extremums.     

Higher-Order Optimality Conditions for Set-Valued Optimization

This paper deals with higher-order optimality conditions of set-valued optimization problems. By virtue of the higher-order derivatives introduced in (Aubin and Frankowska, Set-Valued Analysis, Birkhäuser, Boston, [1990]) higher-order necessary and sufficient optimality conditions are obtained for a set-valued optimization problem whose constraint condition is determined by a fixed set. Higher-order Fritz John type necessary and sufficient optimality conditions are also obtained for a set-valued optimization problem whose constraint condition is determined by a set-valued map.     

Optimality Conditions for Vector Optimization Problems

In this paper, some necessary and sufficient optimality conditions for the weakly efficient solutions of vector optimization problems (VOP) with finite equality and inequality constraints are shown by using two kinds of constraints qualifications in terms of the MP subdifferential due to Ye. A partial calmness and a penalized problem for the (VOP) are introduced and then the equivalence between the weakly efficient solution of the (VOP) and the local minimum solution of its penalized problem is proved under the assumption of partial calmness. This work was supported by the National Natural Science Foundation of China (10671135), the Specialized Research Fund for the Doctoral Program of Higher Education (20060610005) and the National Natural Science Foundation of Sichuan Province (07ZA123). The authors thank Professor P.M. Pardalos and the referees for comments and suggestions.     

On a Conjecture in Second-Order Optimality Conditions

In this paper, we deal with a conjecture formulated in Andreani et al. (Optimization 56:529–542, 2007), which states that whenever a local minimizer of a nonlinear optimization problem fulfills the Mangasarian–Fromovitz constraint qualification and the rank of the set of gradients of active constraints increases at most by one in a neighborhood of the minimizer, a second-order optimality condition that depends on one single Lagrange multiplier is satisfied. This conjecture generalizes previous results under a constant rank assumption or under a rank deficiency of at most one. We prove the conjecture under the additional assumption that the Jacobian matrix has a smooth singular value decomposition. Our proof also extends to the case of the strong second-order condition, defined in terms of the critical cone instead of the critical subspace.     

Second-Order Global Optimality Conditions for Optimization Problems

Second-order optimality conditions are studied for the constrained optimization problem where the objective function and the constraints are compositions of convex functions and twice strictly differentiable functions. A second-order sufficient condition of a global minimizer is obtained by introducing a generalized representation condition. Second-order minimizer characterizations for a convex program and a linear fractional program are derived using the generalized representation condition     

Second-Order Optimality Conditions for Constrained Domain Optimization

This paper develops boundary integral representation formulas for the second variations of cost functionals for elliptic domain optimization problems. From the collection of all Lipschitz domains Ω which satisfy a constraint ∫ _Ω g(x) dx=1, a domain is sought which maximizes either , fixed x ₀∈Ω, or ℱ(Ω)=∫ _Ω F(x,u(x)) dx, where u solves the Dirichlet problem Δu(x)=−f(x), x∈Ω, u(x)=0, x∈∂Ω. Necessary and sufficient conditions for local optimality are presented in terms of the first and second variations of the cost functionals and ℱ. The second variations are computed with respect to domain variations which preserve the constraint. After first summarizing known facts about the first variations of u and the cost functionals, a series of formulas relating various second variations of these quantities are derived. Calculating the second variations depends on finding first variations of solutions u when the data f are permitted to depend on the domain Ω.     

非光滑半定规划的一阶最优性条件

首次考虑了非光滑半定规化问题．运用与非线性规划类似的技巧,把现存的理论扩展到约束是结构稀疏矩阵的情况,给出了其一阶最优性条件。考虑了严格互补条件不成立的情形．在约束矩阵为对角阵条件下,所用的正则条件与传统非线性优化意义下的是一致的．     

Second-Order Global Optimality Conditions for Optimization Problems

Second-order optimality conditions are studied for the constrained optimization problem where the objective function and the constraints are compositions of convex functions and twice strictly differentiable functions. A second-order sufficient condition of a global minimizer is obtained by introducing a generalized representation condition. Second-order minimizer characterizations for a convex program and a linear fractional program are derived using the generalized representation condition     

Optimality Conditions for Nonconvex Constrained Optimization Problems

Abstract

We present optimality conditions for a class of nonsmooth and nonconvex constrained optimization problems. To achieve this aim, various well-known constraint qualifications are extended based on the concept of tangential subdifferential and the relations between them are investigated. Moreover, local and global necessary and sufficient optimality conditions are derived in the absence of convexity of the feasible set. In addition to the theoretical results, several examples are provided to illustrate the advantage of our outcomes.