47,981 research outputs found

    On classes of set optimization problems which are reducible to vector optimization problems and its impact on numerical test instances

    Get PDF
    Set optimization with the set approach has recently gained increasing interest due to its practical relevance. In this problem class one studies optimization problems with a set-valued objective map and defines optimality based on a direct comparison of the images of the objective function, which are sets here. Meanwhile, in the literature a wide range of theoretical tools as scalarization approaches and derivative concepts as well as first numerical algorithms are available. These numerical algorithms require on the one hand test instances where the optimal solution sets are known. On the other hand, in most examples and test instances in the literature only set-valued maps with a very simple structure are used. We study in this paper such special set-valued maps and we show that some of them are such simple that they can equivalently be expressed as a vector optimization problem. Thus we try to start drawing a line between simple set-valued problems and such problems which have no representation as multiobjective problems. Those having a representation can be used for defining test instances for numerical algorithms with easy verifiable optimal solution set

    Set optimization - a rather short introduction

    Full text link
    Recent developments in set optimization are surveyed and extended including various set relations as well as fundamental constructions of a convex analysis for set- and vector-valued functions, and duality for set optimization problems. Extensive sections with bibliographical comments summarize the state of the art. Applications to vector optimization and financial risk measures are discussed along with algorithmic approaches to set optimization problems

    A coarse solution of generalized semi-infinite optimization problems via robust analysis of marginal functions and global optimization

    Get PDF
    Die Arbeit beschĂ€ftigt sich ĂŒberwiegend mit theoretischen Untersuchungen zur Bestimmung grober Startlösungen fĂŒr verallgemeinerte semi-infinite Optimierungsaufgaben (GSIP) mit Methoden der globalen Optimierung. GSIP Probleme besitzen im Gegensatz zu den gewöhnlichen semi-infiniten Optimierungsaufgaben (SIP) die Eigenschaft, dass die Indexmenge, die die Restriktionen beschreibt, natĂŒrlich ĂŒberabzĂ€hlbar ist, wie bei (SIP) aber darĂŒber hinaus von den Problemvariablen abhĂ€ngig ist, d.h. die Indexmenge ist eine Punkt-Menge Abbildung. Solche Probleme sind von sehr komplexer Struktur, gleichzeitig gibt es große Klassen von naturwissenschaftlich - technischen, ökonomischen Problemen, die in (GSIP) modelliert werden können. Im allgemeinem ist die zulĂ€ssige Menge von einem (GSIP) weder abgeschlossen noch zusammenhĂ€ngend. Die Abgeschlossenheit von der zulĂ€ssigen Menge ist gesichert durch die Unterhalbstetigkeit der Index-Abbildung. Viele Autoren machen diese Voraussetzung, um numerische Verfahren fĂŒr (GSIP) herzuleiten. Diese Arbeit versucht erstmals, ohne Unterhalbstetigkeit der Index-Abbildung auszukommen. Unter diese schwĂ€cheren Voraussetzungen kann die zulĂ€ssige Menge nicht abgeschlossen sein und (GSIP) kann auch keine Lösung besitzen. Trotzdem kann man eine verallgemeinerte Minimalstelle oder eine Minimalfolge fĂŒr (GSIP) bestimmen. FĂŒr diese Zwecke werden zwei numerische ZugĂ€nge vorgeschlagen. Im ersten Zugang wird der zulĂ€ssige Bereich des (GSIP) durch eine (gewöhnliche) parametrische semi- infinite Approximationsaufgabe beschrieben. Die Marginalfunktion der parametrischen Aufgabe ist eine exakte Straffunktion des zulĂ€ssigen Bereiches des (GSIP). Im zweiten Zugang werden zwei Straffunktionen vorgestellt. Eine verwendet die semi-infinite Restriktion direkt als einen "Max"-Straffterm und die zweite entsteht durch das "lower level Problem" des (GSIP). In beiden ZugĂ€nge mĂŒssen wir uns mit unstetigen Optimierungsaufgaben beschĂ€ftigen. Es wird gezeigt, dass die entstehende Straffunktionen oberrobust (i.A. nicht stetig) sind und damit auch hier stochastische globale Optimierungsmethoden prinzipiell anwendbar sind. Der Hauptbeitrag dieser Arbeit ist die Untersuchung von Robustheiteigenschaften von Marginalfunktionen und Punkt-Menkg-Abbildung mit bestimmte Strukturen. Dieser kann auch als eine Erweiterung der Theorie der Robusten Analysis von Chew & Zheng betrachtet werden. Gleichzeitig wird gezeigt, dass die fĂŒr halbstetigen Abbildungen und Funktionen bekannten Aussagen bis auf wenige Ausnahmen in Bezug auf das Robustheitskonzept ĂŒbertragen werden können. Am Ende zeigen einige numerische Beispiele, dass die vorgeschlagenen ZugĂ€nge prinzipiell brauchbar sind.The aim of this work is to determine a coarse approximation to the optimal solution of a class of generalized semi-infinite optimization problems (GSIP) through a global optimization method by using fairly discontinuous penalty functions. Where the fairness of the discontinuities is characterized by the notions of robust analysis and standard measure theory. Generalized semi-infinite optimization problems have an infinite number of constraints, where the usually infinite index set of the constraints varies with respect to the problem variable; i.e. we have a set-valued map as and index set, in contrast to standard semi-infinite optimization (SIP) problems. These problems have very complex problem structures, at the same time, there are several classes of scientific, engineering, econimic, etc., problems which could be modelled in terms of (GSIP)s. Under general assumptions, the feasible set of a (GSIP) might not be closed nor connected. In fact, the feasible set is a closed set if the index map is lower semi-continuous. Several authors assume the lower semi-continuity of the index map for the derivation of numerical algorithms for (GSIP). However, in this work no exclusive assumption has been made to preserve the above nicer structures. Thus, the feasible set may not be closed and (GSIP) may not have a solution. However, one may be interested to determine a generalized minimizer or a minimizing sequence of GSIP. For this purpose, two penalty approaches have been proposed. In the first approach (mainly conceptual), there is defined a discontinuous penalty function based on the marginal function of a certain auxiliary parametric semi-infinite optimization problem (PSIP). In the second approach (based on discretization), we define two penalty functions: one based on the marginal function of the lower level problem and, a second, based on the feasible set of (GSIP). The relationships of these penalty problems with the (GSIP) have been investigated through minimizing sequences. In the two penalty approaches we need to deal with discontinuous optimization problems. The numerical treatment of these discontinuous optimization problems can be done by using the Integral Global Optimization Method (IGOM); in particular, through the software routine called BARLO (of Hichert). However, to use BARLO or IGOM we need to verify certain robustness properties of the objective functions of the penalty problems. Hence, one major contribution of this work is a study of robustness properties of marginal value functions and set-valued maps with given structures - extending the theory of robust analysis of Chew and Zheng. At the same time, an effort has been made to find out corresponding robustness results to some standard continuity notions of functions and set-valued maps. To show the viability of the proposed approach, numerical experiments are made using the penalty-discretization approach

    Solving ill-posed bilevel programs

    No full text
    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
    • 

    corecore