Generalized concavity and duality with a square root term

Duality results for a class of nondifferentiable mathematical programming problems are given. These results allow for the weakening of the usual convexity conditions required for duality to hold. A pair of symmetric and self dual nondifferentiable programs under weaker convexity conditions are also given. A subgradient symmetric duality is proposed and its limitations discussed. Finally, a pair of nondifferentiable mathematical programs containing arbitrary norms is presented.     

Weak minimization and duality

Sufficient and necessary optimality conditions are given for weakly minimized optimization problems in terms of a vector valued Lagrangian. Lagrangian and Wolfe type duals are constructed and duality established using an ordering that accords with the definition of a weak minimum. The results for differentiable problems continue to hold under weakened convexity assumptions and for problems which quasiminimize rather than minimize.     

Higher order duality in vector optimization over cones

In this paper higher order cone convex, pseudo convex, strongly pseudo convex, and quasiconvex functions are introduced. Higher order sufficient optimality conditions are given for a weak minimum, minimum, strong minimum and Benson proper minimum solution of a vector optimization problem. A higher order dual is associated and weak and strong duality results are established under these new generalized convexity assumptions.     

Convexity Conditions for Generalized Riemann Derivable Functions

Several convexity theorems for generalized Riemann derivative are obtained. A generalized symmetric derivative is introduced which includes the usual symmetric Riemann derivative and with the help of this generalized symmetric derivative several other convexity theorems are established. A partial answer to a conjecture of Butzer and Kozakiewicz is given.     

Optimal solutions to differential inclusions

We treat a control problem given in terms of a differential inclusion $$\dot x(t) \in E(t,x(t))$$ and develop necessary conditions for a minimum in the problem. These conditions are given in terms of certain normals to arbitrary closed sets, and require no smoothness or convexity in the problem. The results subsume related works that incorporate convexity assumptions.     

A general theory of convexity

A generalization of the usual notion of convexity is developed. It is shown that for certain applications some of the postulates of the generalized theory must be relaxed. The independence of the postulates is discussed and representation theorems are given. A topology which is compatible with the convexity structure is constructed.     

Some mathematical properties of a DEA model for the joint determination of efficiencies

This paper explores the Kuhn–Tucker conditions and convexity issues in a non-linear DEA model for the joint determination of efficiencies developed by Mar Molinero. It is shown that the usual convexity conditions that apply to Linear Programming problems are satisfied in this case. First order Kuhn–Tucker conditions are derived and interpreted. Estimation strategies are suggested. Some empirical work is reported.     

半模糊凸模糊映射

In this paper, a new class of fuzzy mappings called semistrictly convex fuzzy mappings is introduced and we present some properties of this kind of fuzzy mappings. In particular, we prove that a local minimum of a semistrictly convex fuzzy mapping is also a global minimum. We also discuss the relations among convexity, strict convexity and semistrict convexity of fuzzy mapping, and give several sufficient conditions for convexity and semistrict convexity.     

Convexity with respect to a differential equation

The concepts of convexity of a set, convexity of a function and monotonicity of an operator with respect to a second-order ordinary differential equation are introduced in this paper. Several well-known properties of usual convexity are derived in this context, in particular, a characterization of convexity of function and monotonicity of an operator. A sufficient optimality condition for a optimization problem is obtained as an application. A number of examples of convex sets, convex functions and monotone operators with respect to a differential equation are presented.     

Necessary and Sufficient Conditions for Efficiency Via Convexificators

Based on the extended Ljusternik Theorem by Jiménez-Novo, necessary conditions for weak Pareto minimum of multiobjective programming problems involving inequality, equality and set constraints in terms of convexificators are established. Under assumptions on generalized convexity, necessary conditions for weak Pareto minimum become sufficient conditions.     

二元凸函数的判别条件

给出了二元凸函数的定义,导出了二元凸函数的判别条件,该判别条件由二元函数的二阶导数给出.用二元凸函数的判别条件和半正定的(半负定)矩阵的性质,得到了二元二次多项式凸性的简单判别形式.     

Elimination of bounds in optimization problems by transforming variables

The problem of minimizing a nonlinear objective function ofn variables, with continuous first and second partial derivatives, subject to nonnegativity constraints or upper and lower bounds on the variables is studied. The advisability of solving such a constrained optimization problem by making a suitable transformation of its variables in order to change the problem into one of unconstrained minimization is considered. A set of conditions which guarantees that every local minimum of the new unconstrained problem also satisfies the first-order necessary (Kuhn—Tucker) conditions for a local minimum of the original constrained problem is developed. It is shown that there are certain conditions under which the transformed objective function will maintain the convexity of the original objective function in a neighborhood of the solution. A modification of the method of transformations which moves away from extraneous stationary points is introduced and conditions under which the method generates a sequence of points which converges to the solution at a superlinear rate are given.     

Auxiliary problem principles for equilibria

The auxiliary problem principle allows solving a given equilibrium problem (EP) through an equivalent auxiliary problem with better properties. The paper investigates two families of auxiliary EPs: the classical auxiliary problems, in which a regularizing term is added to the equilibrium bifunction, and the regularized Minty EPs. The conditions that ensure the equivalence of a given EP with each of these auxiliary problems are investigated exploiting parametric definitions of different kinds of convexity and monotonicity. This analysis leads to extending some known results for variational inequalities and linear EPs to the general case together with new equivalences. Stationarity and convexity properties of gap functions are investigated as well in this framework. Moreover, both new results on the existence of a unique solution and new error bounds based on gap functions with good convexity properties are obtained under weak quasimonotonicity or weak concavity assumptions.     

Necessary optimality criteria in mathematical programming in the presence of differentiability

We consider the problem of minimizing a function over a region defined by an arbitrary set, equality constraints, and constraints of the inequality type defined via a convex cone. Under some moderate convexity assumptions on the arbitrary set we develop Optimality criteria of the minimum principle type which generalize the Fritz John Optimality conditions. As a consequence of this result necessary Optimality criteria of the saddle point type drop out. Here convexity requirements on the functions are relaxed to convexity at the point under investigation. We then present the weakest possible constraint qualification which insures positivity of the lagrangian multiplier corresponding to the objective function.     

Readily implementable conjugate gradient methods

Conjugate gradient methods have been extensively used to locate unconstrained minimum points of real-valued functions. At present, there are several readily implementable conjugate gradient algorithms that do not require exact line search and yet are shown to be superlinearly convergent. However, these existing algorithms usually require several trials to find an acceptable stepsize at each iteration, and their inexact line search can be very timeconsuming.In this paper we present new readily implementable conjugate gradient algorithms that will eventually require only one trial stepsize to find an acceptable stepsize at each iteration.Making usual continuity assumptions on the function being minimized, we have established the following properties of the proposed algorithms. Without any convexity assumptions on the function being minimized, the algorithms are globally convergent in the sense that every accumulation point of the generated sequences is a stationary point. Furthermore, when the generated sequences converge to local minimum points satisfying second-order sufficient conditions for optimality, the algorithms eventually demand only one trial stepsize at each iteration, and their rate of convergence isn-step superlinear andn-step quadratic.This research was supported in part by the National Science Foundation under Grant No. ENG 76-09913.     

Strict convexity of sequence Orlicz-Musielak spaces with Orlicz norm

H. Milnes gave in (Pacific J. Math. 18 (1957), 1451–1483) a criterion for strict convexity of Orlicz spaces with respect to the so called Orlicz norm, in the case of nonatomic measure and a usual Young function. Here there are presented necessary and sufficient conditions for strict convexity of Orlicz-Musielak spaces (J. Musielak and W. Orlicz, Studia Math. 18 (1957), 49–65) with Orlicz norm in the case of purely atomic measure. For sequence Orlicz-Musielak spaces with Luxemburg norm, such a criterion is given in (A. Kami ska, Bull. Acad. Polon. Sci. Sér. Sci. Math. Astronom. Phys. 29 (1981), 137–144).     

Uniform convexity of Musielak-Orlicz-Sobolev spaces and applications

This paper deals with uniform convexity of Musielak-Orlicz-Sobolev spaces and its applications to variational problems. Some sufficient conditions and examples for uniform convexity of Musielak-Orlicz-Sobolev spaces are given. Some special properties relative to the uniformly convex modular for uniformly convex Musielak-Orlicz-Sobolev spaces are presented. As an application of these abstract results, the local minimizers and the mountain pass type critical point of an integral functional with more complicated growth than the p(x)-growth are studied.     

Three kinds of convexity and concavity properties of optimal value function in parametric nonlinear programming

This work is concerned with exploring the new convexity and concavity properties of the optimal value function in parametric programming. Some convex (concave) functions are discussed and sufficient conditions for new convexity and concavity of the optimal value function in parametric programming are given. Many results in this paper can be considered as deepen the convexity and concavity studies of convex (concave) functions and the optimal value functions.     

Minimum selections and fixed points of set-valued operators in Banach spaces with some uniform convexity

A new continuity theorem of minimum selection is presented for a continuous set-valued operator from a topological space into a Banach space with some uniform convexity. As applications, some problems concerning minimum right inverses for linear operators and minimum fixed points for condensing set-valued nonlinear operators are discussed. Also, the existence of minimum solutions for an integral inclusion is proved.     

New optimality conditions for unconstrained vector equilibrium problem in terms of contingent derivatives in Banach spaces

This article presents necessary and sufficient optimality conditions for weakly efficient solution, Henig efficient solution, globally efficient solution and superefficient solution of vector equilibrium problem without constraints in terms of contingent derivatives in Banach spaces with stable functions. Using the steadiness and stability on a neighborhood of optimal point, necessary optimality conditions for efficient solutions are derived. Under suitable assumptions on generalized convexity, sufficient optimality conditions are established. Without assumptions on generalized convexity, a necessary and sufficient optimality condition for efficient solutions of unconstrained vector equilibrium problem is also given. Many examples to illustrate for the obtained results in the paper are derived as well.