Unified Duality Theory for Constrained Extremum Problems. Part II: Special Duality Schemes

In the first part of this paper series, a unified duality scheme for a constrained extremum problem is proposed by virtue of the image space analysis. In the present paper, we pay our attention to study of some special duality schemes. Particularly, the Lagrange-type duality, Wolfe duality and Mond–Weir duality are discussed as special duality schemes in a unified interpretation. Moreover, three practical classes of regular weak separation functions are also considered.     

Optimal Potentials for Problems with Changing Sign Data

The main purpose of this paper is to study the duality and penalty method for a constrained nonconvex vector optimization problem. Following along with the image space analysis, a Lagrange-type duality for a constrained nonconvex vector optimization problem is proposed by virtue of the class of vector-valued regular weak separation functions in the image space. Simultaneously, some equivalent characterizations to the zero duality gap property are established including the Lagrange multiplier, the Lagrange saddle point and the regular separation. Moreover, an exact penalization is also obtained by means of a local image regularity condition and a class of particular regular weak separation functions in the image space.     

A Unified Approach for Constrained Extremum Problems: Image Space Analysis

In this paper, by exploiting the image space analysis we investigate a class of constrained extremum problems, the constraining function of which is set-valued. We show that a (regular) linear separation in the image space is equivalent to the existence of saddle points of Lagrangian and generalized Lagrangian functions and we also give Lagrangian type optimality conditions for the class of constrained extremum problems under suitable generalized convexity and compactness assumptions. Moreover, we consider an exact penalty problem for the class of constrained extremum problems and prove that it is equivalent to the existence of a regular linear separation under suitable generalized convexity and compactness assumptions.     

Nonlinear separation functions and constrained extremum problems

In this paper, the image space analysis is employed to investigate constrained extremum problems. A new nonlinear separation function for the constrained extremum problems is given. Some optimality conditions and a strong duality theorem for the constrained extremum problem are obtained. These results extend and improve the corresponding ones in the literature.     

Nonlinear Separation Approach to Constrained Extremum Problems

In this paper, by virtue of a nonlinear scalarization function, two nonlinear weak separation functions, a nonlinear regular weak separation function, and a nonlinear strong separation function are first introduced, respectively. Then, by the image space analysis, a global saddle-point condition for a nonlinear function is investigated. It is shown that the existence of a saddle point is equivalent to a nonlinear separation of two suitable subsets of the image space. Finally, some necessary and sufficient optimality conditions are obtained for constrained extremum problems.     

Characterization of duality for a generalized quasi-equilibrium problem

In this paper, the duality theory of a generalized quasi-equilibrium problem (also called generalized Ky Fan quasi-inequality) is investigated by using the image space approach. Generalized quasi-equilibrium problem is transformed into a minimization problem. The minimization problem is further reformulated as an image problem by virtue of linear/nonlinear separation function. The dual problem of the image problem is constructed in the image space, then zero duality gap between the image problem and its dual problem is derived under saddle point condition as well as the equivalent regular linear/nonlinear separation condition. Finally, some more sufficient conditions guaranteeing zero duality gap are also proposed.     

Some applications of the image space analysis to the duality theory for constrained extremum problems

By means of the Image Space Analysis, duality properties of a constrained extremum problem are investigated. The analysis of the lower semicontinuity of the perturbation function, related to a right-hand side perturbation of the given problem, leads to a characterization of zero duality gap in the image space.     

On the theory of Lagrangian duality

It is shown that a general Lagrangian duality theory for constrained extremum problems can be drawn from a separation scheme in the Image Space, namely in the space where the functions of the given problem run.     

Coercivity and image of constrained extremum problems

The concept of coercivity is extended to the image space, and it is exploited to obtain a lower bound for the minimum of a constrained extremum problem and a sufficient condition for the optimality. Problems which are coercive in the image space turn out to have zero duality gap. A possible application to variational inequalities is illustrated by means of an example.     

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.     

Theorems of the alternative for multifunctions with applications to optimization: General results

Theorems of the alternative and separation theorems have been shown to be very useful concepts in constrained extremum problems (see, for instance, Refs. 1–12). Their use has stressed the concept of image of a constrained extremum problem, which has turned out to be a powerful and promising tool for investigating the main aspects of optimization (see Refs. 13 and 19). It should be pointed out that, in this approach, a finite-dimensional image problem can be associated to the given extremum problem, even if this is infinite-dimensional and provided that its constraints are expressed by functionals. Such a development can be carried on by means of theorems of the alternative for systems of single-valued functions.In this paper, theorems of the alternative for systems of multifunctions are studied, some general properties are stated, and connections with known results investigated. It is shown how the present approach can be used to analyze extremum problems, where the image of the domain of the constraining functions belongs to a functional space. Such a development will be carried on in a subsequent paper.Useful discussions with O. Ferrero and C. Zlinescu are gratefully acknowledged.     

Separations and Optimality of Constrained Multiobjective Optimization via Improvement Sets

In this paper, we investigate the separations and optimality conditions for the optimal solution defined by the improvement set of a constrained multiobjective optimization problem. We introduce a vector-valued regular weak separation function and a scalar weak separation function via a nonlinear scalarization function defined in terms of an improvement set. The nonlinear separation between the image of the multiobjective optimization problem and an improvement set in the image space is established by the scalar weak separation function. Saddle point type optimality conditions for the optimal solution of the multiobjective optimization problem are established, respectively, by the nonlinear and linear separation methods. We also obtain the relationships between the optimal solution and approximate efficient solution of the multiobjective optimization problem. Finally, sufficient and necessary conditions for the (regular) linear separation between the approximate image of the multiobjective optimization problem and a convex cone are also presented.     

Extended Lagrange and Penalty Functions in Optimization

We consider nonlinear Lagrange and penalty functions for optimization problems with a single constraint. The convolution of the objective function and the constraint is accomplished by an increasing positively homogeneous of the first degree function. We study necessary and also sufficient conditions for the validity of the zero duality gap property for both Lagrange and penalty functions and for the exact penalization. We also study the so-called regular weak separation functions.     

On Regularity for Constrained Extremum Problems. Part 1: Sufficient Optimality Conditions

The main aspect of the paper consists in the application of a particular theorem of separation between two sets to the image associated with a constrained extremum problem. In the image space, the two sets are a convex cone, which depends on the constraints (equalities or inequalities) of the given problem, and its image. In this way, a condition for the existence of a regular saddle point (i.e., a sufficient optimality condition) is obtained. This regularity condition is compared with those existing in the literature.     

Constrained Extremum Problems and Image Space Analysis—Part III: Generalized Systems

In Part I, sufficient and necessary optimality conditions and the image regularity conditions of constrained scalar and vector extremum problems are reviewed for Image Space Analysis. Part II presents the main feature of the duality and penalization of constrained scalar and vector extremum problems by virtue of Image Space Analysis. In the light, as said in Part I and Part II, to describe the state of Image Space Analysis for constrained optimization, and to stress that it allows us to unify and generalize the several topics of Optimization, in this Part III, we continue to give an exhaustive literature review on separation functions, gap functions and error bounds for generalized systems. Part III also throws light on some research gaps and concludes with the scope of future research in this area.     

Saddle points and gap functions for weak generalized Ky Fan inequalities

In this paper, we employ the image space analysis method to investigate a weak generalized Ky Fan inequality with cone constraints. Some regular weak separation functions are introduced, and generalized Lagrangian functions are constructed by using these regular weak separation functions. Under suitable convexity assumptions and Slater condition, the existence of solution for the weak generalized Ky Fan inequality with cone constraints is equivalent to a saddle point of the generalized Lagrangian functions. Moreover, we also use the regular weak separation functions to construct gap functions for the weak generalized Ky Fan inequality with cone constraints, and obtain its error bound.     

Separation of sets and Wolfe duality

Lagrangian duality can be derived from separation in the Image Space, namely the space where the images of the objective and constraining functions of the given extremum problem run. By exploiting such a result, we analyse the relationships between Wolfe and Mond-Weir duality and prove their equivalence in the Image Space under suitable generalized convexity assumptions.      

Constrained Extremum Problems,Regularity Conditions and Image Space Analysis. Part II: The Vector Finite-Dimensional Case

The scalar finite-dimensional case has been discussed in the first part of this work series, which aims at exploiting the image space analysis to establish a general regularity condition for constrained extremum problems. Based on this preliminary result, the present paper dedicates itself to further study the regularity conditions for vector constrained extremum problems in a Euclidean space. The case of infinite-dimensional image will be the subject of a subsequent paper.     

Unified Nonlinear Lagrangian Approach to Duality and Optimal Paths

In the context of an inequality constrained optimization problem, we present a unified nonlinear Lagrangian dual scheme and establish necessary and sufficient conditions for the zero duality gap property. From these results, we derive necessary and sufficient conditions for four classes of zero duality gap properties and establish the equivalence among them. Finally, we obtain the convergence of an optimal path for the unified scheme and present a sufficient condition for the finite termination of the optimal path. This research was partially supported by the Research Grants Council of Hong Kong Grant PolyU 5250/03E, the National Natural Science Foundation of China Grants 10471159 and 10571106, NCET, and the Natural Science Foundation of Chongqing     

Image space approach to penalty methods

In this paper, we introduce a unified framework for the study of penalty concepts by means of the separation functions in the image space (see Ref. 1). Moreover, we establish new results concerning a correspondence between the solutions of the constrained problem and the limit points of the unconstrained minima. Finally, we analyze some known classes of penalty functions and some known classical results about penalization, and we show that they can be derived from our results directly.