This work aims to investigate some applications of the conjugate duality for scalar and vector optimization problems to the construction of gap functions for variational inequalities and equilibrium problems. The basic idea of the approach is to reformulate variational inequalities and equilibrium problems into optimization problems depending on a fixed variable, which allows us to apply duality results from optimization problems. Based on some perturbations, first we consider the conjugate duality for scalar optimization. As applications, duality investigations for the convex partially separable optimization problem are discussed. Afterwards, we concentrate our attention on some applications of conjugate duality for convex optimization problems in finite and infinite-dimensional spaces to the construction of a gap function for variational inequalities and equilibrium problems. To verify the properties in the definition of a gap function weak and strong duality are used. The remainder of this thesis deals with the extension of this approach to vector variational inequalities and vector equilibrium problems. By using the perturbation functions in analogy to the scalar case, different dual problems for vector optimization and duality assertions for these problems are derived. This study allows us to propose some set-valued gap functions for the vector variational inequality. Finally, by applying the Fenchel duality on the basis of weak orderings, some variational principles for vector equilibrium problems are investigated.

This work is devoted to study efficient numerical methods for solving nonsmooth convex equilibrium problems in the sense of Blum and Oettli. First we consider the auxiliary problem principle which is a generalization to equilibrium problems of the classical proximal point method for solving convex minimization problems. This method is based on a fixed point property. To make the algorithm implementable we introduce the concept of $mu$-approximation and we prove that the convergence of the algorithm is preserved when in the subproblems the nonsmooth convex functions are replaced by $mu$-approximations. Then we explain how to construct $mu$-approximations using the bundle concept and we report some numerical results to show the efficiency of the algorithm. In a second part, we suggest to use a barrier function method for solving the subproblems of the previous method. We obtain an interior proximal point algorithm that we apply first for solving nonsmooth convex minimization problems and then for solving equilibrium problems. In particular, two interior extragradient algorithms are studied and compared on some test problems.

Many mathematical models generally consist of either a continuous system like that of a system of differential equations, or a discrete system such as a discrete game theoretic model; however, there exist phenomena in which neither modeling approach alone is sufficient for capturing the behaviour of the intended real world system. This leads to the need to explore the use of combinations of such discrete and continuous processes, namely the use of mathematical modeling with what are known as hybrid dynamical systems. In what follows, we provide a blueprint for one approach to study several classes of equilibrium problems in non-equilibrium states through the direct use of hybrid dynamical systems. The motivation of our work stems from the fact that the real world is rarely, if ever, in a state of perfect equilibrium and that the behaviour of equilibrium problems in non-equilibrium states is just as complex and interesting (if not more so) than standard equilibrium solutions. Our approach consists of an association of classes of traffic equilibrium problems, noncooperative games, minimization problems, and complementarity problems to a class of hybrid dynamical system called projected dynamical systems. The purposed connection between equilibrium problems and projected dynamical system is made possible through mutual connections to the robust framework of variational inequalities. The results of our work include theoretical contributions such as showing how evolution solutions (non-equilibrium solutions) can be analyzed from a theoretical point of view and how they relate to equilibrium solutions; computational methods for tracking and visualizing evolution solutions and the development of numerical algorithms for simulation; and applications such as the effect of population vaccination decisions in the spread of infectious disease, dynamic traffic networks, dynamic vaccination games, and nonsmooth electrical circuits.

This work aims to investigate some applications of the conjugate duality for scalar and vector optimization problems to the construction of gap functions for variational inequalities and equilibrium problems. The basic idea of the approach is to reformulate variational inequalities and equilibrium problems into optimization problems depending on a fixed variable, which allows us to apply duality results from optimization problems. Based on some perturbations, first we consider the conjugate duality for scalar optimization. As applications, duality investigations for the convex partially separable optimization problem are discussed. Afterwards, we concentrate our attention on some applications of conjugate duality for convex optimization problems in finite and infinite-dimensional spaces to the construction of a gap function for variational inequalities and equilibrium problems. To verify the properties in the definition of a gap function weak and strong duality are used. The remainder of this thesis deals with the extension of this approach to vector variational inequalities and vector equilibrium problems. By using the perturbation functions in analogy to the scalar case, different dual problems for vector optimization and duality assertions for these problems are derived. This study allows us to propose some set-valued gap functions for the vector variational inequality. Finally, by applying the Fenchel duality on the basis of weak orderings, some variational principles for vector equilibrium problems are investigated.

Die vorliegende Arbeit befasst sich mit verallg. Nash-Gleichgewichtsproblemen im Zusammenhang mit Optimalsteuerungsproblemen mit (nichtlinearen) partiellen Differentialgleichungen. Ausgehend von der Existenzfrage von Nash-Gleichgewichten werden Bedingungen an Optimalsteuerungsprobleme mit nichtlinearen Lösungsoperatoren hergeleitet, welche die Konvexität des reduzierten Problems garantieren. Dazu nutzen wir die verallg. Konvexität von vektorwertigen Operatoren. Da keine expl. Darstellung des Lösungsoperators bekannt ist, werden hinreichende Bedingungen an die Operatorgleichung formuliert. Zusammen mit Anforderungen an das Zielfunktional wird so die Konvexität des reduzierten Problems garantiert. Das erlaubt auch Stationaritätssysteme im nichtglatten Fall herzuleiten. Eine zusätzliche Bedingung an die Lösung der Operatorgleichung koppelt die Strategien der Spieler. Das markiert den Übergang zu verallgemeinerten Nash-Spielen. Um diese Probleme anzugehen, wenden wir eine Penalty-Technik an. Damit wird die beschriebene Abhängigkeit vermieden und zum Zielfunktional transportiert. Damit wird eine Folge von Ersatzproblemen formuliert, deren Grenze das ursprüngliche Problem ist. Für die mathematische Beschreibung entwickeln wir eine erweiterte Γ-Konvergenz für Gleichgewichtsprobleme. Das Verhalten der Lagrange-Multiplikatoren im Stationaritätssystem wird unter Verwendung einer Pfadverfolgungstechnik analysiert und eine numerisch nutzbare Updatestrategie wird hergeleitet. Für ein praktisch anwendbares Lösungsverfahren ist eine Diskretisierung notwendig. Dazu verwenden wir eine Finite-Elemente-Methode. Die Herleitung der A-priori-Konvergenz basierend auf der zuvor verallgemeinerten Γ-Konvergenz wird für Gleichgewichtsprobleme mit gleichzeitiger Regularisierung etabliert. Im Blick auf durch Hindernisbedingungen erzeugte Kontaktmengen wenden wir uns auch adaptiven Finite-Elemente-Methoden zu. Unsere theoretischen Ergebnisse werden durch mehrere akademische Anwendungen illustriert. / The present work deals with generalized Nash equilibrium problems related to optimal control problems on (nonlinear) partial differential equations. Starting from the question of the existence of Nash equilibria, conditions for optimal control problems with nonlinear solution operators are derived that guarantee the convexity of the reduced problem. To do so, we discuss generalized convexity of vector-valued operators. As no explicit representation of the solution operator is known, conditions on the operator equation that imply this property are formulated. In combination with requirements for the objective functional, the convexity of the reduced problem can be guaranteed. This approach also allows us to derive stationarity systems even in the nonsmooth case. The presence of a condition on the solution of the operator equation couples the players' strategies. This marks the transition to generalized Nash games. To address these problems, we apply a penalty technique. Hence, the described dependency is avoided and transported to the objective. As the penalty functional is scaled with a parameter, a sequence of surrogate problems, whose limit is the original problem, is formulated. For its mathematical description, we introduce an extended Γ-convergence for equilibrium problems. The behavior of the Lagrangian multipliers in the stationarity system is analyzed using a path-following technique, and a numerically usable update strategy is derived. A discretization is necessary for a practically applicable solution method. For this, we use a finite element method. The derivation of the a priori convergence based on the previously generalized Γ-convergence is established for equilibrium problems with simultaneous regularization. With regard to the presence of contact sets induced by obstacle conditions, we also turn to adaptive finite element methods. Our theoretical results are illustrated by several academic applications.

Die vorliegende Arbeit beschaeftigt sich mit Gleichgewichtsproblemen unter Gleichgewichtsrestriktionen, sogenannten EPECs (Englisch: Equilibrium Problems with Equilibrium Constraints). Konkret handelt es sich um gekoppelte Zwei-Ebenen-Optimierungsprobleme, bei denen Nash- Gleichgewichte fuer die Entscheidungen der oberen Ebene gesucht sind. Ein Ziel der Arbeit besteht in der Formulierung dualer Stationaritaetsbedingungen zu solchen Problemen. Als Anwendung wird ein oligopolistisches Wettbewerbsmodell fuer Strommaerkte betrachtet. Zur Gewinnung qualitativer Hypothesen ueber die Struktur der betrachteten Modelle (z.B. Inaktivitaet bestimmter Marktteilnehmer) aber auch fuer moegliche numerische Zugaenge ist es wesentlich, EPEC-Loesungen explizit bezueglich der Eingangsdaten des Problems zu formulieren. Der Weg dorthin erfordert eine Strukturanalyse der involvierten Optimierungsprobleme (constraint qualifications, Regularitaet), die Herleitung von Stabilitaetsresultaten bestimmter mengenwertiger Abbildungen und die Nutzung von Transformationsformeln fuer die sogenannte Ko-Ableitung. Weitere Schwerpunkte befassen sich mit der Beziehung zwischen verschiedenen dualen Stationaritaetstypen (S- und M-Stationaritaet) sowie mit stochastischen Erweiterungen der betrachteten Problemklasse, sogenannten SEPECs. / This thesis is concerned with equilibrium problems with equilibrium constraints or EPECs. Concretely, we consider models composed by coupling together two-level optimization problems, the upper-level solutions to which are non-cooperative (Nash-Cournot) equilibria. One of the main goals of the thesis involves the formulation of dual stationarity conditions to EPECs. A model of oligopolistic competition for electricity markets is considered as an application. In order to profit from qualitative hypotheses concerning the structure of the considered models, e.g., inactivity of certain market participants at equilibrium, as well as to provide conditions useful for numerical procedures, the ablilty to formulate EPEC solutions in relation to the input data of the problem is of considerable importance. The way to do this requires a structural analysis of the involved optimization problems, e.g., constraints qualifications, regularity; the derivation of stability results for certain multivalued mappings, and the usage of transformation formulae for so-called coderivatives. Further important topics address the relationship between various dual stationarity types, e.g., S- and M-stationarity, as well as the extension of the considered problem classes to a stochastic setting, i.e., stochastic EPECs or SEPECs.

Made available in DSpace on 2015-04-22T22:16:06Z (GMT). No. of bitstreams: 1 daiana santos.pdf: 1011976 bytes, checksum: 0d1f23d4c01774fbad224c1c0fbe0359 (MD5) Previous issue date: 2013-09-23 / FAPEAM - Fundação de Amparo à Pesquisa do Estado do Amazonas / In this dissertation, we present a proximal point method for solving problems balance in Hilbert spaces proposed by Alfredo Iusem and Wilfredo Sosa in [1]. We analyzed the convergence of this mehtod for troubleshooting balance. We verified the sequence generated by the method of classical proximal point and generated sequence the proximal point method to balance problems are the same. These results were obtained using variations of monotonicity of the function that defines the balance problem. In the final analysis is made on the weakening of the hypothesis assumed by function. / Nesta dissertação, apresentamos um método de ponto proximal para resolução de problemas de equilíbrio em espaços de Hilbert proposto por Alfredo Iusem e Wilfredo Sosa em [1]. Analisamos a convergência deste método para soluções de problemas de equilíbrio. Verificamos que a sequência gerada pelo método de ponto proximal clássico e a sequência gerada pelo método de ponto proximal para problemas de equilíbrio coincidem. Esses resultados foram obtidos usando variações de monotonicidade sobre a função que define o problema de equilíbrio. Uma análise final é feita sobre o enfraquecimento das hipóteses assumidas pela função.