An Optimization Problem with a Separable Non-Convex Objective Function and a Linear Constraint

For a class of global optimization (maximization) problems, with a separable non-concave objective function and a linear constraint a computationally efficient heuristic has been developed.The concave relaxation of a global optimization problem is introduced. An algorithm for solving this problem to optimality is presented. The optimal solution of the relaxation problem is shown to provide an upper bound for the optimal value of the objective function of the original global optimization problem. An easily checked sufficient optimality condition is formulated under which the optimal solution of concave relaxation problem is optimal for the corresponding non-concave problem. An heuristic algorithm for solving the considered global optimization problem is developed.The considered global optimization problem models a wide class of optimal distribution of a unidimensional resource over subsystems to provide maximum total output in a multicomponent systems.In the presented computational experiments the developed heuristic algorithm generated solutions, which either met optimality conditions or had objective function values with a negligible deviation from optimality (less than 1/10 of a percent over entire range of problems tested).     

群体多目标决策联合有效解类的不变凸充分条件

对于群体多目标决策问题，文[1]引进它的联合有效解类的概念，并给出这类解的最优性必要条件，在对于问题的目标函数和约束函数附加凸性的条件下，文[2]又给出了联合有效解类的最优性充分条件，本文进一步在目标函数和约束函数具不变凸和不变广义 凸的情况下，分别给出了联合有效解类的若干最优性充分条件。     

Stability of solutions for a class of nonlinear cone constrained optimization problems,part 1: Basic theory

We Gonsider a class of nonlinear cone constrained optimization problems depending on a parameter. Under the assumption of a constraint qualification, a second order sufficient optimality condition and a stability condition for the Lagrange multipliers it is shown, that for sufficiently smooth perturbations of the constraints and the objective function the optimal solutions obey a type of Lipschitz condition.     

集值优化问题严最大有效解的高阶刻画

在实赋范线性空间中考虑集值优化问题的严有效性.利用高阶导数的性质给出了受约束于固定集的集值优化问题取得严最大有效解的高阶导数型最优性必要条件.当目标函数为锥凹集值映射时,利用严最大有效点的性质得到集值优化问题取得严最大有效解的充分条件.     

全局优化问题的一类积分型最优性条件 (英)

本文通过构造水平集辅助函数对一类积分全局最优性条件进行研究. 所构造的辅助函数仅含有一个参数变量与一个控制变量,该参数变量用以表征对原问题目标函数最优值的估计,而控制变量用以控制积分型全局最优性条件的精度. 对参数变量做极限运算即可得到积分型全局最优性条件.继而给出了用该辅助函数所刻画的全局最优性的充要条件, 从而将原全局优化问题的求解转化为寻找一个非线性方程根的问题.更进一步地,若所取测度为勒贝格测度且积分区域为自然数集合的一个有限子集, 则该积分最优性条件便化为有限极大极小问题中利用凝聚函数对极大值函数进行逼近的近似系统.从而积分型全局最优性条件可以看作是该近似系统从离散到连续的一种推广.     

Nonlinear separation in the image space with applications to constrained optimization

In this paper, by means of the image space analysis, we obtain optimality conditions for vector optimization of objective multifunction with multivalued constraints based on disjunction of two suitable subsets of the image space. By the oriented distance function a nonlinear regular separation is introduced and some optimality conditions for the constrained extremum problem are obtained. It is shown that the existence of a nonlinear separation is equivalent to a saddle point condition for the generalized Lagrangian function.     

A hierarchical scheduling problem with a well-solvable second stage

In the hierarchical scheduling model to be considered, the decision at the aggregate level to acquire a number of identical machines has to be based on probabilistic information about the jobs that have to be scheduled on these machines at the detailed level. The objective is to minimize the sum of the acquisition costs and the expected average completion time of the jobs. In contrast to previous models of this type, the second part of this objective function corresponds to a well-solvable scheduling problem that can be solved to optimality by a simple priority rule. A heuristic method to solve the entire problem is described, for which strong asymptotic optimality results can be established.     

Properties of equation reformulation of the Karush–Kuhn–Tucker condition for nonlinear second order cone optimization problems

We give an equation reformulation of the Karush–Kuhn–Tucker (KKT) condition for the second order cone optimization problem. The equation is strongly semismooth and its Clarke subdifferential at the KKT point is proved to be nonsingular under the constraint nondegeneracy condition and a strong second order sufficient optimality condition. This property is used in an implicit function theorem of semismooth functions to analyze the convergence properties of a local sequential quadratic programming type (for short, SQP-type) method by Kato and Fukushima (Optim Lett 1:129–144, 2007). Moreover, we prove that, a local solution x* to the second order cone optimization problem is a strict minimizer of the Han penalty merit function when the constraint nondegeneracy condition and the strong second order optimality condition are satisfied at x*.     

拟不变凸集值优化问题严有效解的最优性条件

在赋范线性空间中借助切导数研究集值优化问题的严有效性．当目标函数和约束函数相对于同一向量函数为拟不变凸时,利用凸集分离定理给出了集值优化问题取得严有效元的Kuhn—Xhcker型最优陛必要条件．利用切导数的性质,用构造性方法得到了拟不变凸集值优化问题取得严有效元的充分条件．     

Duality and saddle-point type optimality for interval-valued programming

In this paper, Mond-Weir’s type dual in programming problem with an interval-valued objective function and interval-valued inequality constrict conditions is formulated. Duality theorems are established under suitable conditions. A real-valued Lagrangian function for the interval-valued programming is defined. Further, the saddle point of Lagrangian function is also defined and saddle point optimality conditions are presented.     

Another approach to multiobjective programming problems withF-convex functions

In this paper, optimality conditions for multiobjective programming problems havingF-convex objective and constraint functions are considered. An equivalent multiobjective programming problem is constructed by a modification of the objective function. Furthermore, anF—Lagrange function is introduced for a constructed multiobjective programming problem, and a new type of saddle point is introduced. Some results for the new type of a saddle point are given.     

Trust region affine scaling algorithms for linearly constrained convex and concave programs

We study a trust region affine scaling algorithm for solving the linearly constrained convex or concave programming problem. Under primal nondegeneracy assumption, we prove that every accumulation point of the sequence generated by the algorithm satisfies the first order necessary condition for optimality of the problem. For a special class of convex or concave functions satisfying a certain invariance condition on their Hessians, it is shown that the sequences of iterates and objective function values generated by the algorithm convergeR-linearly andQ-linearly, respectively. Moreover, under primal nondegeneracy and for this class of objective functions, it is shown that the limit point of the sequence of iterates satisfies the first and second order necessary conditions for optimality of the problem. © 1998 The Mathematical Programming Society, Inc. Published by Elsevier Science B.V.The work of these authors was based on research supported by the National Science Foundation under grant INT-9600343 and the Office of Naval Research under grants N00014-93-1-0234 and N00014-94-1-0340.     

Global Optimality Conditions and Optimization Methods for Quadratic Knapsack Problems

The quadratic knapsack problem (QKP) maximizes a quadratic objective function subject to a binary and linear capacity constraint. Due to its simple structure and challenging difficulty, it has been studied intensively during the last two decades. This paper first presents some global optimality conditions for (QKP), which include necessary conditions and sufficient conditions. Then a local optimization method for (QKP) is developed using the necessary global optimality condition. Finally a global optimization method for (QKP) is proposed based on the sufficient global optimality condition, the local optimization method and an auxiliary function. Several numerical examples are given to illustrate the efficiency of the presented optimization methods.     

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     

Lipschitz B-Vex Functions and Nonsmooth Programming

In this paper, the equivalence between the class of B-vex functions and that of quasiconvex functions is proved. Necessary and sufficient conditions, under which a locally Lipschitz function is B-vex, are established in terms of the Clarke subdifferential. Regularity of locally Lipschitz B-vex functions is discussed. Furthermore, under appropriate conditions, a necessary optimality condition of the Slater type and a sufficient optimality condition are obtained for a nonsmooth programming problem involving B-vex functions.     

Second-order invex functions in nonlinear programming

We introduce a notion of a second-order invex function. A Fréchet differentiable invex function without any further assumptions is second-order invex. It is shown that the inverse claim does not hold. A Fréchet differentiable function is second-order invex if and only if each second-order stationary point is a global minimizer. Two complete characterizations of these functions are derived. It is proved that a quasiconvex function is second-order invex if and only if it is second-order pseudoconvex. Further, we study the nonlinear programming problem with inequality constraints whose objective function is second-order invex. We introduce a notion of second-order type I objective and constraint functions. This class of problems strictly includes the type I invex ones. Then we extend a lot of sufficient optimality conditions with generalized convex functions to problems with second-order type I invex objective function and constraints. Additional optimality results, which concern type I and second-order type I invex data are obtained. An answer to the question when a kernel, which is not identically equal to zero, exists is given.     

集值优化问题的高阶最优性条件(借助Studniarski导数)(英文)

本文研究的是约束集值优化问题的高价最优性条件.首先通过借助集值映射的Stud-niarski导数和严格局部有效性,讨论了集值优化问题的高阶必要条件和充分条件.对于充分条件,初始空间必须是有限维的.其次在初始空间和目标空间是有限维的以及集值映射是m阶稳定的条件下,也得到了此约束集值优化问题的高阶最优性条件.     

Optimal resource allocation in a two-sector economic model with singular regimes

The article considers the problem of resource allocation in a two-sector economic model with a nonlinear production function of a special type. The main mathematical apparatus is Pontryagin’s maximum principle, i.e., the theorem on necessary conditions of optimality. It is shown that in the given problem the maximum principle provides a necessary and sufficient condition of optimality. A possible singular solution of the problem is found. An extremum solution is constructed in explicit form under various assumptions about the initial values. A “sufficiently long” planning horizon is assumed. An alternative approach is described, which does not use the maximum principle and instead investigates the integral representation of the optimand functional. The detailed theoretical investigation of the problem is accompanied by numerous illustrations.     

Optimality Conditions and Characterizations of the Solution Sets in Generalized Convex Problems and Variational Inequalities

We derive necessary and sufficient conditions for optimality of a problem with a pseudoconvex objective function, provided that a finite number of solutions are known. In particular, we see that the gradient of the objective function at every minimizer is a product of some positive function and the gradient of the objective function at another fixed minimizer. We apply this condition to provide several complete characterizations of the solution sets of set-constrained and inequality-constrained nonlinear programming problems with pseudoconvex and second-order pseudoconvex objective functions in terms of a known solution. Additionally, we characterize the solution sets of the Stampacchia and Minty variational inequalities with a pseudomonotone-star map, provided that some solution is known.