THE TANGENT CONES ON CONSTRAINT QUALIFICATIONS IN OPTIMIZATION PROBLEMS

This article proposes a few tangent cones,which are relative to the constraint qualifications of optimization problems.With the upper and lower directional derivatives of an objective function,the characteristics of cones on the constraint qualifications are presented.The interrelations among the constraint qualifications,a few cones involved, and level sets of upper and lower directional derivatives are derived.

CONSTRAINT QUALIFICATIONS AND DUAL PROBLEMS FOR QUASI-DIFFERENTIABLE PROGRAMMING

In classical nonlinear programming, it is a general method of developing optimality conditions that a nonlinear programming problem is linearized as a linear programming problem by using first order approximations of the functions at a given feasible point. The linearized procedure for differentiable nonlinear programming problems can be naturally generalized to the quasi differential case. As in classical case so called constraint qualifications have to be imposed on the constraint functions to guarantee that for a given local minimizer of the original problem the nullvector is an optimal solution of the corresponding 'quasilinearized' problem. In this paper, constraint qualifications for inequality constrained quasi differentiable programming problems of type min {f(x)|g(x)≤0} are considered, where f and g are qusidifferentiable functions in the sense of Demyanov. Various constraint qualifications for this problem are presented and a new one is proposed. The relations among these conditions are investigated. Moreover, a Wolf dual problem for this problem is introduced, and the corresponding dual theorems are given.

On Basic Constraint Qualifications for Infinite System of Convex Inequalities in Banach Spaces

The BCQ and the Abadie CQ for infinite systems of convex inequalities in Banach spaces are characterized in terms of the upper semi-continuity of the convex cones generated by the subdifferentials of active convex functions. Some relationships with other constraint qualifications such as the CPLV and the Slate condition are also studied. Applications in best approximation theory are provided.

Guignard’s Constraint Qualification (GCQ) and Multiobjective Optimisation Problems

Investigation of optimality conditions has been one of the most interesting topics in the theory of multiobjective optimisation problems (MOP). To derive necessary optimality conditions of MOP, we consider assumptions called constraints qualifications. It is recognised that Guignard Constraint Qualification (GCQ) is the most efficient and general assumption for scalar objective optimisation problems;however, GCQ does not ensure Karush-Kuhn Tucker (KKT) necessary conditions for multiobjective optimisation problems. In this paper, we investigate the reasons behind that GCQ are not allowed to derive KKT conditions in multiobjective optimisation problems. Furthermore, we propose additional assumptions that allow one to use GCQ to derive necessary conditions for multiobjective optimisation problems. Finally, we also include sufficient conditions for multiobjective optimisation problems.

On Semi-infinite Mathematical Programming Problems with Equilibrium Constraints Using Generalized Convexity

In this paper,we consider semi-infinite mathematical programming problems with equilibrium constraints(SIMPPEC).By using the notion of convexificators,we establish sufficient optimality conditions for the SIMPPEC.We formulate Wolfe and Mond–Weir-type dual models for the SIMPPEC under the invexity and generalized invexity assumptions.Weak and strong duality theorems are established to relate the SIMPPEC and two dual programs in the framework of convexificators.

A CONSTRAINT QUALIFICATION FOR CONVEX PROGRAMMING

This paper derives a new constraint qualification for nondifferential convex programming problem, by using the distance between the feasible set and the perturbed feasible sets. If the feasible sot is bounded, then this constraint qualification is weaker than Stater's constrains qualification.

Sufficiency and Duality for Nonsmooth Interval-Valued Optimization Problems via Generalized Invex-Infine Functions

In this paper,a new concept of generalized-affineness type of functions is introduced.This class of functions is more general than some of the corresponding ones discussed in Chuong(Nonlinear Anal Theory Methods Appl 75:5044–5052,2018),Sach et al.(J Global Optim 27:51–81,2003)and Nobakhtian(Comput Math Appl 51:1385–1394,2006).These concepts are used to discuss the sufficient optimality conditions for the interval-valued programming problem in terms of the limiting/Mordukhovich subdifferential of locally Lipschitz functions.Furthermore,two types of dual problems,namely Mond–Weir type and mixed type duals are formulated for an interval-valued programming problem and usual duality theorems are derived.Our results improve and generalize the results appeared in Kummari and Ahmad(UPB Sci Bull Ser A 82(1):45–54,2020).

KUHN-TUCKER NECESSARY CONDITIONS FOR (h, ψ)-MULTIOBJECTIVE OPTIMIZATION PROBLEMS

Five kinds of cones are introduced, which are used to establish the constraints qualifications, under which the generalized Kuhn-Tucker necessary conditions are developed for a class of generalized (h,φ)-differentiable single-objective and multiobjective programming problems by using Motzkin's alternative theorem and Ben-Tal generalized algebraic operations.

Stable and Total Fenchel Duality for Composed Convex Optimization Problems

In this paper, we consider the composed convex optimization problem which consists in minimizing the sum of a convex function and a convex composite function. By using the properties of the epigraph of the conjugate functions and the subdifferentials of convex functions, we give some new constraint qualifications which completely characterize the strong Fenchel duality and the total Fenchel duality for composed convex optimiztion problem in real locally convex Hausdorff topological vector spaces.

Necessary Optimality Conditions for a Class of Nonsmooth Vector Optimization

The Kuhn-Tucker type necessary conditions of weak efficiency are given for the problem of mini- mizing a vector function whose each component is the sum of a differentiable function and a convex function, subjcct to a set of differentiable nonlinear inequalities on a convex subset C of R^n, under the conditions similar to the Abadie constraint qualification, or the Kuhn-Tucker constraint qualification, or the Arrow-Hurwicz-Uzawa constraint qualification.

Optimality conditions for sparse nonlinear programming

The sparse nonlinear programming （SNP） is to minimize a general continuously differentiable func- tion subject to sparsity, nonlinear equality and inequality constraints. We first define two restricted constraint qualifications and show how these constraint qualifications can be applied to obtain the decomposition properties of the Frechet, Mordukhovich and Clarke normal cones to the sparsity constrained feasible set. Based on the decomposition properties of the normal cones, we then present and analyze three classes of Karush-Kuhn- Tucker （KKT） conditions for the SNP. At last, we establish the second-order necessary optimality condition and sufficient optimality condition for the SNP.

A COMPLETE CHARACTERIZATION OF THE ROBUST ISOLATED CALMNESS OF NUCLEAR NORM REGULARIZED CONVEX OPTIMIZATION PROBLEMS

In this paper, we provide a complete characterization of the robust isolated calmness of the Karush-Kuhn-Tucker （KKT） solution mapping for convex constrained optimization problems regularized by the nuclear norm function. This study is motivated by the recent work in [8], where the authors show that under the Robinson constraint qualification at a local optimal solution, the KKT solution mapping for a wide class of conic programming problems is robustly isolated calm if and only if both the second order sufficient condition （SOSC） and the strict Robinson constraint qualification （SRCQ） are satisfied. Based on the variational properties of the nuclear norm function and its conjugate, we establish the equivalence between the primal/dual SOSC and the dual/primal SRCQ. The derived results lead to several equivalent characterizations of the robust isolated calmness of the KKT solution mapping and add insights to the existing literature on the stability of nuclear norm regularized convex optimization problems.

A Smoothing Method for Solving Bilevel Multiobjective Programming Problems

In this paper,a bilevel multiobjective programming problem,where the lower level is a convex parameter multiobjective program,is concerned.Using the KKT optimality conditions of the lower level problem,this kind of problem is transformed into an equivalent one-level nonsmooth multiobjective optimization problem.Then,a sequence of smooth multiobjective problems that progressively approximate the nonsmooth multiobjective problem is introduced.It is shown that the Pareto optimal solutions(stationary points)of the approximate problems converge to a Pareto optimal solution(stationary point)of the original bilevel multiobjective programming problem.Numerical results showing the viability of the smoothing approach are reported.