Characterizations of optimal solution sets of convex infinite programs

In this paper, several Lagrange multiplier characterizations of the solution set of a convex infinite programming problem are given. Characterizations of solution sets of cone-constrained convex programs are derived as well. The procedure is then adopted to a semi-convex problem with convex constraints. For this problem, we present firstly a necessary and sufficient condition for optimality and secondly a characterization of its optimal solution set, based on a Lagrange multiplier associated with a given solution and on directional derivatives of the objective function.      

Lagrange Multiplier Conditions Characterizing the Optimal Solution Sets of Cone-Constrained Convex Programs

Various characterizations of optimal solution sets of cone-constrained convex optimization problems are given. The results are expressed in terms of subgradients and Lagrange multipliers. We establish first that the Lagrangian function of a convex program is constant on the optimal solution set. This elementary property is then used to derive various simple Lagrange multiplier-based characterizations of the solution set. For a finite-dimensional convex program with inequality constraints, the characterizations illustrate that the active constraints with positive Lagrange multipliers at an optimal solution remain active at all optimal solutions of the program. The results are applied to derive corresponding Lagrange multiplier characterizations of the solution sets of semidefinite programs and fractional programs. Specific examples are given to illustrate the nature of the results.     

A closedness condition and its applications to DC programs with convex constraints

This paper concerns a closedness condition called (CC) involving a convex function and a convex constrained system. This type of condition has played an important role in the study of convex optimization problems. Our aim is to establish several characterizations of this condition and to apply them to study problems of minimizing a DC function under a cone-convex constraint and a set constraint. First, we establish several so-called ‘Toland–Fenchel–Lagrange’ duality theorems. As consequences, various versions of generalized Farkas lemmas in dual forms for systems involving convex and DC functions are derived. Then, we establish optimality conditions for DC problem under convex constraints. Optimality conditions for convex problems and problems of maximizing a convex function under convex constraints are given as well. Most of the results are established under the (CC) condition. This article serves as a link between several corresponding known ones published recently for DC programs and for convex programs.     

Motzkin decomposition of closed convex sets

Theodore Motzkin proved, in 1936, that any polyhedral convex set can be expressed as the (Minkowski) sum of a polytope and a polyhedral convex cone. This paper provides five characterizations of the larger class of closed convex sets in finite dimensional Euclidean spaces which are the sum of a compact convex set with a closed convex cone. These characterizations involve different types of representations of closed convex sets as the support functions, dual cones and linear systems whose relationships are also analyzed in the paper. The obtaining of information about a given closed convex set F and the parametric linear optimization problem with feasible set F from each of its different representations, including the Motzkin decomposition, is also discussed.     

Dual Characterizations of Set Containments with Strict Convex Inequalities

Characterizations of the containment of a convex set either in an arbitrary convex set or in the complement of a finite union of convex sets (i.e., the set, described by reverse-convex inequalities) are given. These characterizations provide ways of verifying the containments either by comparing their corresponding dual cones or by checking the consistency of suitable associated systems. The convex sets considered in this paper are the solution sets of an arbitrary number of convex inequalities, which can be either weak or strict inequalities. Particular cases of dual characterizations of set containments have played key roles in solving large scale knowledge-based data classification problems where they are used to describe the containments as inequality constraints in optimization problems. The idea of evenly convex set (intersection of open half spaces), which was introduced by W. Fenchel in 1952, is used to derive the dual conditions, characterizing the set containments.     

Lagrange multiplier characterizations of solution sets of constrained pseudolinear optimization problems

In this article, we study the minimization of a pseudolinear (i.e. pseudoconvex and pseudoconcave) function over a closed convex set subject to linear constraints. Various dual characterizations of the solution set of the minimization problem are given. As a consequence, several characterizations of the solution sets of linear fractional programs as well as linear fractional multi-objective constrained problems are given. Numerical examples are also given.     

Dual characterizations of the set containments with strict cone-convex inequalities in Banach spaces

We give characterizations of the containment of a convex set either in an arbitrary convex set or in a set described by reverse cone-convex inequalities in Banach spaces. The convex sets under consideration are the solution sets of an arbitrary number of cone-convex inequalities, which can be either weak or strict inequalities. These characterizations provide ways of verifying the containments either by comparing their corresponding dual cones or by checking the consistency of suitable associated systems. Particular cases of dual characterizations of set containments have played key roles in solving large scale knowledge-based data classification problems, where they are used to describe the containments as inequality constraints in optimization problems. The concept of evenly convex set is used to derive the dual conditions, characterizing the set containments.      

Lagrange Multiplier Characterizations of Solution Sets of Constrained Nonsmooth Pseudolinear Optimization Problems

This paper deals with the minimization of a class of nonsmooth pseudolinear functions over a closed and convex set subject to linear inequality constraints. We establish several Lagrange multiplier characterizations of the solution set of the minimization problem by using the properties of locally Lipschitz pseudolinear functions. We also consider a constrained nonsmooth vector pseudolinear optimization problem and derive certain conditions, under which an efficient solution becomes a properly efficient solution. The results presented in this paper are more general than those existing in the literature.     

Characterizations of solution sets of mathematical programs in terms of Lagrange multipliers

The main aim of this article is to obtain characterizations of the solution set of two non-linear programs in terms of Lagrange multipliers. Both the programs have pseudolinear constraints but the objective function is convex for the first program and pseudolinear for the second program, where all the functions are defined in terms of bifunctions.     

Characterizing robust set containments and solutions of uncertain linear programs without qualifications

Qualification-free dual characterizations are given for robust polyhedral set containments where a robust counterpart of an uncertain polyhedral set is contained in another polyhedral set or a polyhedral set is contained in a robust counterpart of an uncertain polyhedral set. These results are used to characterize robust solutions of uncertain linear programs, where the uncertainty is defined in terms of intervals or l₁-balls. The hidden separable sub-linearity of the robust counterparts allows qualification-free dual characterizations.     

Characterizing the solution set of convex optimization problems without convexity of constraints

Some characterizations of solution sets of a convex optimization problem with a convex feasible set described by tangentially convex constraints are given.     

Monotone operator theory in convex optimization

In this paper, we establish tractable sum of squares characterizations of the containment of a convex set, defined by a SOS-concave matrix inequality, in a non-convex set, defined by difference of a SOS-convex polynomial and a support function, with Slater’s condition. Using our set containment characterization, we derive a zero duality gap result for a DC optimization problem with a SOS-convex polynomial and a support function, its sum of squares polynomial relaxation dual problem, the semidefinite representation of this dual problem, and the dual problem of the semidefinite programs. Also, we present the relations of their solutions. Finally, through a simple numerical example, we illustrate our results. Particularly, in this example we find the optimal solution of the original problem by calculating the optimal solution of its associated semidefinite problem.     

Characterizations of the solution set for a class of nonsmooth optimization problems

In this paper, a class of nonsmooth optimization problems with inequality constraints is considered. It is shown that the Lagrangian function associated with a fixed Lagrange multiplier is constant on the solution set under suitable conditions. Then, some characterizations of the solution set of this class of optimization problems are obtained. Examples are given to illustrate our main results.     

On the cone of a finite dimensional compact convex set at a point

Four equivalent conditions for a convex cone in a Euclidean space to be an F_σ-set are given. Our result answers in the negative a recent open problem posed by Tam [5], characterizes the barrier cone of a convex set, and also provides an alternative proof for the known characterizations of the inner aperture of a convex set as given by Brønsted [2] and Larman [3].     

Characterizations of robust solution set of convex programs with uncertain data

In this paper, we study convex programming problems with data uncertainty in both the objective function and the constraints. Under the framework of robust optimization, we employ a robust regularity condition, which is much weaker than the ones in the open literature, to establish various properties and characterizations of the set of all robust optimal solutions of the problems. These are expressed in term of subgradients, Lagrange multipliers and epigraphs of conjugate functions. We also present illustrative examples to show the significances of our theoretical results.     

Enhanced Efficiency in Multi-objective Optimization

For a given multi-objective optimization problem, we introduce and study the notion of α-proper efficiency. We give two characterizations of such proper efficiency: one is in terms of exact penalization and the other is in terms of stability of associated parametric problems. Applying the aforementioned characterizations and recent results on global error bounds for inequality systems, we obtain verifiable conditions for α-proper efficiency. For a large class of polynomial multi-objective optimization problems, we show that any efficient solution is α-properly efficient under some mild conditions. For a convex quadratically constrained multi-objective optimization problem with convex quadratic objective functions, we show that any efficient solution is α-properly efficient with a known estimate on α whenever its constraint set is bounded. Finally, we illustrate our achieved results with examples, and give an example to show that such an enhanced efficiency property may not hold for multi-objective optimization problems involving C ^∞-functions as objective functions.     

Minimization of entropy functionals

Entropy (i.e. convex integral) functionals and extensions of these functionals are minimized on convex sets. This paper is aimed at reducing as much as possible the assumptions on the constraint set. Primal attainment, dual equalities, dual attainment and characterizations of the minimizers are obtained with weak constraint qualifications. These results improve several aspects of the literature on the subject.     

Dual characterizations of set containments involving uncertain polyhedral sets in Banach spaces with applications

In this paper, we first give a dual characterization for set containments defined by lower semi-continuous and sublinear functions on Banach spaces. Next, we provide dual characterizations for robust polyhedral containments where a robust counterpart of an uncertain polyhedral set is contained in another polyhedral set or a polyhedral set is contained in a robust counterpart of an uncertain polyhedral set. Finally, as an application, we derive Lagrange multiplier characterizations for robust solutions of the robust uncertain linear programming problems.     

Farkas-Type Results for Vector-Valued Functions with Applications

The main purpose of this paper consists of providing characterizations of the inclusion of the solution set of a given conic system posed in a real locally convex topological space into a variety of subsets of the same space defined by means of vector-valued functions. These Farkas-type results are used to derive characterizations of the weak solutions of vector optimization problems (including multiobjective and scalar ones), vector variational inequalities, and vector equilibrium problems.     

Infra-Mackey spaces, weak barrelledness and barrelledness

A weaker Mackey topology, infra-Mackey topology, is introduced. For an infra-Mackey space, dual local quasi-completeness, c₀-quasi-barrelledness, Ruess' property (quasi-L) and C-quasi-barrelledness are equivalent to each other. Inspired by the definition of Mazur spaces, locally convex spaces are classified according to various conditions ensuring linear functionals continuous. In the classification, every class of special locally convex spaces is characterized by some completeness of the duals. From this, some new characterizations of quasi-barrelledness and barrelledness are given.