Necessary Conditions in Multiobjective Optimization With Equilibrium Constraints

In this paper we study multiobjective optimization problems with equilibrium constraints (MOECs) described by generalized equations in the form 0 is an element of the set G(x,y) + Q(x,y), where both mappings G and Q are set-valued. Such models particularly arise from certain optimization-related problems governed by variational inequalities and first-order optimality conditions in nondifferentiable programming. We establish verifiable necessary conditions for the general problems under consideration and for their important specifications using modern tools of variational analysis and generalized differentiation. The application of the obtained necessary optimality conditions is illustrated by a numerical example from bilevel programming with convex while nondifferentiable data

Methods Of Variational Analysis In Pessimistic Bilevel Programming

Bilevel programming problems are of growing interest both from theoretical and practical points of view. These models are used in various applications, such as economic planning, network design, and so on. The purpose of this dissertation is to study the pessimistic (or strong) version of bilevel programming problems in finite-dimensional spaces. Problems of this type are intrinsically nonsmooth (even for smooth initial data) and can be treated by using appropriate tools of modern variational analysis and generalized differentiation developed by B. Mordukhovich. This dissertation begins with analyzing pessimistic bilevel programs, formulation of the problems, literature review, practical application, existence of the optimal solutions, reformulation and related to the other programming. The mainstream in studying optimization problems consists of obtaining necessary optimality conditions for optimality, and the main focus of this dissertation is to obtain necessary optimality conditions for pessimistic bilevel programming problems. Optimality conditions for the optimistic version of bilevel programming are extensively discussed in the literature. However, there are just a few papers devoted to the pessimistic version of bilevel programming problems and most of these papers concern the existence of optimal solutions. This dissertation is devoted to establish, by a variety of techniques from convex and nonsmooth analysis, several versions of first order necessary and sufficient optimality conditions for pessimistic bilevel programming problems. To achieve our goal, we first use the implicit programming techniques, and depending on the continuous, Lipschitz, and Fréchet differentiable selections, we obtain necessary optimality conditions. The value function technique plays a central role in sensitivity analysis, controllability, and even in establishing necessary optimality conditions. We consider constructions or estimations of the subdifferential of value functions and come up with the optimality conditions using minimax programming approach treating the cases: convex data, differentiable (strict) data, and Lipschitz data separately. We also use the duality programming approach and obtain optimality conditions extending the convex case to the nonconvex case. In the last chapter, we produce the necessary and sufficient optimality conditions for pessimistic bilevel programming with the rational reaction (optimal solutions set of the lower level problem) set of finite cardinality, but not singleton. We present then some classes of pessimistic bilevel programs for which there are finite rational responses

Solving ill-posed bilevel programs

This paper deals with ill-posed bilevel programs, i.e., problems admitting multiple lower-level solutions for some upper-level parameters. Many publications have been devoted to the standard optimistic case of this problem, where the difficulty is essentially moved from the objective function to the feasible set. This new problem is simpler but there is no guaranty to obtain local optimal solutions for the original optimistic problem by this process. Considering the intrinsic non-convexity of bilevel programs, computing local optimal solutions is the best one can hope to get in most cases. To achieve this goal, we start by establishing an equivalence between the original optimistic problem an a certain set-valued optimization problem. Next, we develop optimality conditions for the latter problem and show that they generalize all the results currently known in the literature on optimistic bilevel optimization. Our approach is then extended to multiobjective bilevel optimization, and completely new results are derived for problems with vector-valued upper- and lower-level objective functions. Numerical implementations of the results of this paper are provided on some examples, in order to demonstrate how the original optimistic problem can be solved in practice, by means of a special set-valued optimization problem

KKT reformulation and necessary conditions for optimality in nonsmooth bilevel optimization

For a long time, the bilevel programming problem has essentially been considered as a special case of mathematical programs with equilibrium constraints (MPECs), in particular when the so-called KKT reformulation is in question. Recently though, this widespread believe was shown to be false in general. In this paper, other aspects of the difference between both problems are revealed as we consider the KKT approach for the nonsmooth bilevel program. It turns out that the new inclusion (constraint) which appears as a consequence of the partial subdifferential of the lower-level Lagrangian (PSLLL) places the KKT reformulation of the nonsmooth bilevel program in a new class of mathematical program with both set-valued and complementarity constraints. While highlighting some new features of this problem, we attempt here to establish close links with the standard optimistic bilevel program. Moreover, we discuss possible natural extensions for C-, M-, and S-stationarity concepts. Most of the results rely on a coderivative estimate for the PSLLL that we also provide in this paper

On generalized semi-infinite optimization and bilevel optimization

The paper studies the connections and differences between bilevel problems (BL) and generalized semi-infinite problems (GSIP). Under natural assumptions (GSIP) can be seen as a special case of a (BL). We consider the so-called reduction approach for (BL) and (GSIP) leading to optimality conditions and Newton-type methods for solving the problems. We show by a structural analysis that for (GSIP)-problems the regularity assumptions for the reduction approach can be expected to hold generically at a solution but for general (BL)-problems not. The genericity behavior of (BL) and (GSIP) is in particular studied for linear problems