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

Image space analysis has proved to be instrumental in unifying several theories, apparently disjoint from each other. With reference to constraint qualifications/regularity conditions in optimization, such an analysis has been recently introduced by Moldovan and Pellegrini. Based on this result, the present paper is a preliminary part of a work, which aims at exploiting the image space analysis to establish a general regularity condition for constrained extremum problems. The present part deals with scalar constrained extremum problems in a Euclidean space. The vector case as well as the case of infinite-dimensional image will be the subject of a subsequent part.     

Unified Duality Theory for Constrained Extremum Problems. Part I: Image Space Analysis

This paper is concerned with a unified duality theory for a constrained extremum problem. Following along with the image space analysis, a unified duality scheme for a constrained extremum problem is proposed by virtue of the class of regular weak separation functions in the image space. Some equivalent characterizations of the zero duality property are obtained under an appropriate assumption. Moreover, some necessary and sufficient conditions for the zero duality property are also established in terms of the perturbation function. In the accompanying paper, the Lagrange-type duality, Wolfe duality and Mond–Weir duality will be discussed as special duality schemes in a unified interpretation. Simultaneously, three practical classes of regular weak separation functions will be also considered.     

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.     

Constrained Extremum Problems with Infinite-Dimensional Image: Selection and Necessary Conditions

This paper deals with image space analysis for constrained extremum problems having an infinite-dimensional image. It is shown that the introduction of selection for point-to-set maps and of quasi multipliers allows one to establish optimality conditions for problems where the classical approach fails.     

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.     

序线性空间中向量极值问题的最优性条件

本文在序线性空间中建立了广义次似凸映射下的择一定理,运用此定理,得出一类向量极值问题的最优性条件.     

Refinements on Gap Functions and Optimality Conditions for Vector Quasi-Equilibrium Problems via Image Space Analysis

By means of some new results on generalized systems, vector quasi-equilibrium problems with a variable ordering relation are investigated from the image perspective. Lagrangian-type optimality conditions and gap functions are obtained under mild generalized convexity assumptions on the given problem. Applications to the analysis of error bounds for the solution set of a vector quasi-equilibrium problem are also provided. These results are refinements of several authors’ works in recent years and also extend some corresponding results in the literature.     

Image Space Analysis for Constrained Inverse Vector Variational Inequalities via Multiobjective Optimization

In this paper, we employ the image space analysis to study constrained inverse vector variational inequalities. First, sufficient and necessary optimality conditions for constrained inverse vector variational inequalities are established by using multiobjective optimization. A continuous nonlinear function is also introduced based on the oriented distance function and projection operator. This function is proven to be a weak separation function and a regular weak separation function under different parameter sets. Then, two alternative theorems are established, which lead directly to sufficient and necessary optimality conditions of the inverse vector variational inequalities. This provides a partial answer to an open question posed in Chen et al. (J Optim Theory Appl 166:460–479, 2015).     

拓扑向量空间中非光滑向量极值问题的最优性条件与对偶

本文提出了向量值函数的锥D-s凸，锥D-s拟凸，s右导数及锥D-s伪凸等新概念，探讨了锥D-s凸函数的有关性质，建立了带约束非光滑向量极值问题(VP)的最优性必要条件与涉及锥D-s凸(拟凸，伪凸)函数的约束极值问题(VP)的最优性充分条件，给出了原问题(VP)与其Mond-Weir型对偶问题的弱对偶与强对偶结论，揭示了(VP)的局部锥D-(弱)有效解与整体锥D-(弱)有效解，(VP)的锥D-弱有效解与锥D-有效解的关系，所得结果拓广了凸规划及部分广义凸规划的有关结论．     

Optimality Conditions and Lagrange Duality for Vector Extremum Problems with Set Constraint

The necessary and sufficient optimality conditions for vector extremum problems with set constraint in an ordered linear topological space are given. Finally, Lagrange duality is established.     

Images,Fixed Points and Vector Extremum Problems

Based on recent results on image space analysis, the paper aims at describing a fixed point approach to vector optimization problems. Possible extensions to the bi-level vector optimization are discussed.     

关于一类向量极值问题弱有效解的充分必要条件

本文研究带集合约束的向量极值问题。运用局部凸Hausdorff拓扑向量空间中广义次似凸映射的择一定理和其他一些结论，得到了关于集合约束向量极值问题弱有效解的几个充分必要条件．     

Vector Topical Function,Abstract Convexity and Image Space Analysis

In this paper, we introduce a new type of vector topical function. It contains some other categories of topical functions as special cases and can be interpreted as weak separation functions in image space analysis. We establish its envelope result and investigate its properties in the frame of abstract convexity. Then, we present the corresponding conjugation and subdifferential, and observe the relationships among these concepts. Finally, as applications, we obtain some dual results for some vector optimization, where the object is expressed as the difference of vector topical functions.     

Minimizers of a Class of Constrained Vectorial Variational Problems: Part I.

In this paper, we prove the existence of minimizers of a class of multiconstrained variational problems. We consider systems involving a nonlinearity that does not satisfy compactness, monotonicity, neither symmetry properties. Our approach hinges on the concentration-compactness approach. In the second part, we will treat orthogonal constrained problems for another class of integrands using density matrices method.     

Problems, Models and Complexity. Part II: Application to the DLSP

In Part I of this study, we suggest to identify an operations research (OR) problem with the equivalence class of models describing the problem and enhance the standard computer-science theory of computational complexity to be applicable to this situation of an often model-based OR context. The Discrete Lot-sizing and Scheduling Problem (DLSP) is analysed here in detail to demonstrate the difficulties which can arise if these aspects are neglected and to illustrate the new theoretical concept. In addition, a new minimal model is introduced for the DLSP which makes this problem eventually amenable to a rigorous analysis of its computational complexity.     

A Hybrid Differential Dynamic Programming Algorithm for Constrained Optimal Control Problems. Part 2: Application

In the first part of this paper series, a new solver, called HDDP, was presented for solving constrained, nonlinear optimal control problems. In the present paper, the algorithm is extended to include practical safeguards to enhance robustness, and four illustrative examples are used to evaluate the main algorithm and some variants. The experiments involve both academic and applied problems to show that HDDP is capable of solving a wide class of constrained, nonlinear optimization problems. First, the algorithm is verified to converge in a single iteration on a simple multi-phase quadratic problem with trivial dynamics. Successively, more complicated constrained optimal control problems are then solved demonstrating robust solutions to problems with as many as 7 states, 25 phases, 258 stages, 458 constraints, and 924 total control variables. The competitiveness of HDDP, with respect to general-purpose, state-of-the-art NLP solvers, is also demonstrated.     

Problems, Models and Complexity. Part I: Theory

The meaning of the term problem in operations research (OR) deviates from the understanding in the theoretical computer sciences: While an OR problem is often conceived to be stated or represented by model formulations, a computer-science problem can be viewed as a mapping from encoded instances to solutions. Such a computer-science problem turns out to be rather similar to an OR model formulation. This ambiguity may cause difficulties if the computer-science theory of computational complexity is applied in the OR context. In OR, a specific model formulation is commonly used in the analysis of the underlying problem and the results obtained for this model are simply lifted to the problem level. But this may lead to erroneous results, if the model used is not appropriate for such an analysis of the problem.To resolve this issue, we first suggest a new precise formal definition of the term problem which is identified with an equivalence class of models describing it. Afterwards, a new definition is suggested for the size of a model which depends on the assumed encoding scheme. Only models which exhibit a minimal size with respect to a reasonable encoding scheme finally turn out to be suitable for the model-based complexity analysis of an OR problem. Therefore, the appropriate choice (or if necessary the construction) of a suitable representative of an OR problem becomes an important theoretical aspect of the modelling process.