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.     

Second-Order Optimality Conditions for Strict Efficiency of Constrained Set-Valued Optimization

In this paper, we propose several second-order derivatives for set-valued maps and discuss their properties. By using these derivatives, we obtain second-order necessary optimality conditions for strict efficiency of a set-valued optimization problem with inclusion constraints in real normed spaces. We also establish second-order sufficient optimality conditions for strict efficiency of the set-valued optimization problem in finite-dimensional normed spaces. As applications, we investigate second-order sufficient and necessary optimality conditions for a strict local efficient solution of order two of a nonsmooth vector optimization problem with an abstract set and a functional constraint.     

Second-Order Optimality Conditions in Set Optimization

In this paper, we propose second-order epiderivatives for set-valued maps. By using these concepts, second-order necessary optimality conditions and a sufficient optimality condition are given in set optimization. These conditions extend some known results in optimization.The authors are grateful to the referees for careful reading and helpful remarks.     

Second-Order Optimality Conditions in Multiobjective Optimization Problems

In this paper, we develop second-order necessary and sufficient optimality conditions for multiobjective optimization problems with both equality and inequality constraints. First, we generalize the Lin fundamental theorem (Ref. 1) to second-order tangent sets; then, based on the above generalized theorem, we derive second-order necessary and sufficient conditions for efficiency.     

Second-Order Optimality Conditions in Minimax Optimization Problems

The paper primarily is concerned with the second-order optimality conditions for minimax problems, where the constraints are described by a set inclusion and a finite number of equalities, and where all the functions involved are Fréchet differentiable with locally Lipschitz derivatives. We make use of the Mangasarian Fromovitz regularity conditions and of the second-order Abadie regularity conditions.     

Tests of Optimality in Constrained Optimization

Sufficiency and necessity theorems are described which testthe optimality of a vector when it is required to minimize agiven function of variables which are subject to inequalities. The paper starts with an exposition of theorems of alternatives,which are fundamental when the functions considered are linear,and proceeds to a systematic formulation of various constraintqualifications, which make it possible to extend results whichhold in linear programming to the non-linear case.     

Alternative Second-Order Conditions in Constrained Optimization

New formulations are given for the second-order necessary conditions in parameter optimization with equality constraints. The new conditions are shown to be equivalent to previously known conditions by using the properties of projection matrices. The various conditions require different computational tools. As one of the obtained conditions requires standard computational tools (i.e., matrix inversion and eigenvalue computation), it might be useful in applications (e.g., real-time optimization schemes), when these standard tools are already in use.     

First- and Second-Order Optimality Conditions for Quadratically Constrained Quadratic Programming Problems

Journal of Optimization Theory and Applications - We consider a quadratic programming problem with quadratic cone constraints and an additional geometric constraint. Under suitable assumptions, we...     

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 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     

集值向量优化的二阶最优性条件

对集值映射引入了高阶Clarke导数,给出了判别集值向量优化所有效性的二阶Kuhn-Tucker条件,并且,借助于集值映射的强(弱)伪凸性给出了一个弱有效解的充分条件.     

On Second-Order Optimality Conditions for Vector Optimization

In this article, two second-order constraint qualifications for the vector optimization problem are introduced, that come from first-order constraint qualifications, originally devised for the scalar case. The first is based on the classical feasible arc constraint qualification, proposed by Kuhn and Tucker (Proceedings of the Second Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 481–492, University of California Press, California, 1951) together with a slight modification of McCormick’s second-order constraint qualification. The second—the constant rank constraint qualification—was introduced by Janin (Math. Program. Stud. 21:110–126, 1984). They are used to establish two second-order necessary conditions for the vector optimization problem, with general nonlinear constraints, without any convexity assumption.     

Second-Order Optimality Conditions in Locally Lipschitz Inequality-Constrained Multiobjective Optimization

Journal of Optimization Theory and Applications - The main goal of this paper is to give some primal and dual Karush–Kuhn–Tucker second-order necessary conditions for the existence of a...     

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.     

Second-Order Optimality Conditions for Constrained Optimization Problems with $$C^1$$ C 1 Data Via Regular and Limiting Subdifferentials

Journal of Optimization Theory and Applications - We present the second-order point-based (necessary and sufficient) optimality conditions for nonlinear programming with continuously differentiable...     

Optimality Conditions in Differentiable Vector Optimization via Second-Order Tangent Sets

We provide second-order necessary and sufficient conditions for a point to be an efficient element of a set with respect to a cone in a normed space, so that there is only a small gap between necessary and sufficient conditions. To this aim, we use the common second-order tangent set and the asymptotic second-order cone utilized by Penot. As an application we establish second-order necessary conditions for a point to be a solution of a vector optimization problem with an arbitrary feasible set and a twice Fréchet differentiable objective function between two normed spaces. We also establish second-order sufficient conditions when the initial space is finite-dimensional so that there is no gap with necessary conditions. Lagrange multiplier rules are also given. This research was partially supported by Ministerio de Ciencia y Tecnología (Spain), Project BFM2003-02194. Online publication 29 January 2004.     

New Results on Second-Order Optimality Conditions in Vector Optimization Problems

In this paper, we study second-order optimality conditions for multiobjective optimization problems. By means of different second-order tangent sets, various new second-order necessary optimality conditions are obtained in both scalar and vector optimization. As special cases, we obtain several results found in the literature (see reference list). We present also second-order sufficient optimality conditions so that there is only a very small gap with the necessary optimality conditions. The authors thank Professor P.L. Yu and the referees for valuable comments and helpful suggestions.     

First and Second-Order Optimality Conditions for Convex Composite Multiobjective Optimization

Multiobjective optimization is a useful mathematical model in order to investigate real-world problems with conflicting objectives, arising from economics, engineering, and human decision making. In this paper, a convex composite multiobjective optimization problem, subject to a closed convex constraint set, is studied. New first-order optimality conditions for a weakly efficient solution of the convex composite multiobjective optimization problem are established via scalarization. These conditions are then extended to derive second-order optimality conditions.