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Settings and notions are as in previous abstracts of W. D. L. Appling. This paper contains an investigation of the relationship between a class of non-linear functions defined on PAB and certain subspaces of PAB in particular Appling's linear C-sets, Solomon leader's finitely additive Lp spaces, and one of the projective limit spaces studied by Davis, Murray, and Weber.

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Les travaux de cette thèse portent sur les prédérivées d'applications multivoques et la théorie de l'optimisation. Dans un premier temps, nous établissons des résultats d'existence de différents types de prédérivées pour certaines classes d'applications. Spécialement, pour des applications multivoques possédant certaines propriétés de convexité. Par la suite, nous appliquons ces résultats dans le cadre de la théorie de l'optimisation multivoque en établissant des conditions nécessaires et des conditions suffisantes d'optimalité. Sous des hypothèses de convexité, nous établissons des résultats naturels propres aux minimiseurs en optimisation convexe. Ensuite, nous appliquons quelques uns de nos résultats théoriques à un modèle de l'économie du bien-être en établissant notamment une équivalence entre les allocations optimales faibles de Pareto du modèle économique et les minimiseurs faibles d'un problème d'optimisation multivoque associé. D'autre part, en utilisant certaines notions d'intérieur généralisé existant dans la littérature, nous discutons dans un cadre unifié divers concepts de minimiseurs relaxés. En vue d'étudier leur stabilité, nous introduisons une topologie sur des espaces vectoriels ordonnés dont découle une notion de convergence nous permettant de définir deux concepts de convergence variationnelle qui sont ensuite utilisés pour établir la stabilité supérieure et la stabilité inférieure des ensembles de minimiseurs relaxés considérés dans ce travail. / This work is devoted to the study of prederivatives of set-valued maps and the theory of optimization. First, we establish results regarding the existence of several kinds of prederivatives for some classes set-valued maps. Specially for set-valued maps enjoying convexity properties. Subsequently, we apply our results in the framework of set optimization by establishing both necessary and sufficient optimality conditions, involving such prederivatives, for set optimization problems. Under convexity assumptions, we prove some natural results fitting the paradigm of minimizers in convex optimization. Then, we apply some of our theoretical results to a model of welfare economics by establishing in particular an equivalence between the weak Pareto optimal allocations of the model and the weak minimizes of a set optimization problem associated. Taking adventadge of several generalized interiority notions existing in the literature, we discuss in a unified way corresponding notions of relaxed minimizers In order to establish stability results, we introduce a topology on vector ordered spaces from which we derive a concept of convergence that we use to define two concepts of variational convergence that allow us to study both the upper and the lower stability of sets of relaxed minimizers we consider.

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Thesis (Ph.D.)--McMaster University, 2005. / Advisor: Ian Hambleton. Includes bibliographical references (leaves 74-75). Also available via World Wide Web.

This thesis deals with the topic of min-max formulations of robust model predictive control problems. The sets involved in guaranteeing robust feasibility of the min-max program in the presence of state constraints are of particular interest, and expanding the applicability of well understood solvers of linearly constrained quadratic min-max programs is the main focus. To this end, a generalisation for the set of uncertainty is considered: instead of fixed bounds on the uncertainty, state- and input-dependent bounds are used. To deal with state- and input dependent constraint sets a framework for a particular class of set-valued maps is utilised, namely parametrically convex set-valued maps. Relevant properties and operations are developed to accommodate parametrically convex set-valued maps in the context of robust model predictive control. A quintessential part of this work is the study of fundamental properties of piecewise polyhedral set-valued maps which are parametrically convex, we show that one particular property is that their combinatorial structure is constant. The study of polytopic maps with a rigid combinatorial structure allows the use of an optimisation based approach of robustifying constrained control problems with probabilistic constraints. Auxiliary polytopic constraint sets, used to replace probabilistic constraints by deterministic ones, can be optimised to minimise the conservatism introduced while guaranteeing constraint satisfaction of the original probabilistic constraint. We furthermore study the behaviour of the maximal robust positive invariant set for the case of scaled uncertainty and show that this set is continuously polytopic up to a critical scaling factor, which we can approximate a-priori with an arbitrary degree of accuracy. Relevant theoretical statements are developed, discussed and illustrated with examples.