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2023 | Book

Variational Methods in Partially Ordered Spaces

Authors: Alfred Göpfert, Hassan Riahi, Christiane Tammer, Constantin Zǎlinescu

Publisher: Springer International Publishing

Book Series : CMS/CAIMS Books in Mathematics

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About this book

In mathematical modeling of processes occurring in logistics, management science, operations research, networks, mathematical finance, medicine, and control theory, one often encounters optimization problems involving more than one objective function so that Multiobjective Optimization (or Vector Optimization, initiated by W. Pareto) has received new impetus. The growing interest in vector optimization problems, both from the theoretical point of view and as it concerns applications to real world optimization problems, asks for a general scheme which embraces several existing developments and stimulates new ones.

This book aims to provide the newest results and applications of this quickly growing field. Basic tools of partially ordered spaces are discussed and applied to variational methods in nonlinear analysis and to optimization problems.

The book begins by providing simple examples that illustrate what kind of problems can be handled with the methods presented. The book then deals with connections between order structures and topological structures of sets, discusses properties of nonlinear scalarization functions, and derives corresponding separation theorems for not necessarily convex sets. Furthermore, characterizations of set relations via scalarization are presented.

Important topological properties of multifunctions and new results concerning the theory of vector optimization and equilibrium problems are presented in the book. These results are applied to construct numerical algorithms, especially, proximal-point algorithms and geometric algorithms based on duality assertions.

In the second edition, new sections about set less relations, optimality conditions in set optimization and the asymptotic behavior of multiobjective Pareto-equilibrium problems have been incorporated. Furthermore, a new chapter regarding scalar optimization problems under uncertainty and robust counterpart problems employing approaches based on vector optimization, set optimization, and nonlinear scalarization was added.

Throughout the entire book, there are examples used to illustrate the results and check the stated conditions.

This book will be of interest to graduate students and researchers in pure and applied mathematics, economics, and engineering. A sound knowledge of linear algebra and introductory real analysis should provide readers with sufficient background for this book.

Table of Contents

Frontmatter
Chapter 1. Examples
Abstract
A cone in a vector space is an algebraic notion, but its use in theory and applications of optimization demands that one considers cones in topological vector spaces and studies, besides algebraic properties such as pointedness and convexity, also analytical ones such as those of being closed, normal, Daniell, nuclear, or having a base with special properties. A short overview of such properties is given in this chapter. The examples of cones show interesting results on cones in infinite-dimensional vector spaces, which are important in vector optimization, set-valued optimization, control theory, equilibrium problems, related variational analysis, risk theory and optimization under uncertainty. In comparison with the first edition, hints are added \(\text {w}.\!\text {r}.\!\text {t}\). preferences that are point-dependent (variable domination structure) or not describable by cones, to uncertainty, robustness and to the overview of cones. Furthermore, we are using the software FLO (Facility Location Optimizer) for solving multi-objective location problems.
Alfred Göpfert, Hassan Riahi, Christiane Tammer, Constantin Zǎlinescu
Chapter 2. Functional Analysis over Cones

This chapter covers important items from convex analysis and its modern extensions. In this chapter, we discuss properties of linear topological spaces, order structures, cones, and separation properties for not necessarily convex sets and continuity notions for sets, (extended) multifunctions, and (extended) vector-valued functions. Compared to the first edition, this chapter is considerably extended, mentioning Asplund spaces, set less relations, characterization of set less relations via translation invariant functions, and radial epi-differentiability of extended vector-valued functions.

Alfred Göpfert, Hassan Riahi, Christiane Tammer, Constantin Zǎlinescu
Chapter 3. Optimization in Partially Ordered Spaces
Abstract
In this chapter, we introduce solution concepts for vector- as well as set-valued optimization problems and derive characterizations by nonlinear translation invariant functions. Furthermore, we show existence results for solutions of vector optimization problems, well-posedness of vector optimization problems, continuity properties, vector equilibrium problems, vector variational inequalities, duality assertions, minimal-point theorems, and variational principles for vector-valued functions of Ekeland’s type as well as saddle point assertions. Compared with the first edition, we added characterizations of solutions of set-valued optimization problems via nonlinear translation invariant functions.
Alfred Göpfert, Hassan Riahi, Christiane Tammer, Constantin Zǎlinescu
Chapter 4. Generalized Differentiation and Optimality Conditions
Abstract
We introduce fundamental tools from variational analysis and derive necessary optimality conditions for solutions of not necessarily convex vector as well as set-valued optimization problems using concepts of generalized differentiation and scalarization techniques by nonlinear translation invariant functions.
Alfred Göpfert, Hassan Riahi, Christiane Tammer, Constantin Zǎlinescu
Chapter 5. Applications

In this chapter, we study applications in approximation theory, primal-dual algorithms for solving approximation as well as locational problems and \(\varepsilon \)-minimum principles for deterministic and for stochastic multiobjective control problems.

Alfred Göpfert, Hassan Riahi, Christiane Tammer, Constantin Zǎlinescu
Chapter 6. Scalar Optimization under Uncertainty
Abstract
In this chapter, we consider scalar problems under uncertainty and introduce three general approaches (vector approach, set approach, and nonlinear scalarization) to robustness and stochastic programming. These approaches permit a unified treatment of a large variety of models from robust optimization and stochastic programming, respectively. In this chapter, we review several classical concepts, both from robust optimization and from stochastic programming, and interpret them in the light of vector optimization (see Section 3.​1), set optimization (see Sections 2.​3 and 3.​2) and using nonlinear scalarizing functionals (see Section 2.​4)—whenever this is possible and leads to meaningful characterizations. Under relatively mild assumptions, it turns out that solutions that are optimal for robust optimization or stochastic programming models are typically obtained as (weakly) efficient solutions of an appropriately formulated deterministic vector optimization counterpart problem.
Alfred Göpfert, Hassan Riahi, Christiane Tammer, Constantin Zǎlinescu
Backmatter
Metadata
Title
Variational Methods in Partially Ordered Spaces
Authors
Alfred Göpfert
Hassan Riahi
Christiane Tammer
Constantin Zǎlinescu
Copyright Year
2023
Electronic ISBN
978-3-031-36534-8
Print ISBN
978-3-031-36533-1
DOI
https://doi.org/10.1007/978-3-031-36534-8

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