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Optimization under Uncertainty: Methods and Applications

Editat de Aris Daniilidis, Akhtar A. Khan, Christiane Tammer
en Limba Engleză Hardback – 3 mar 2027
Optimization under Uncertainty presents a comprehensive overview of recent theoretical and computational advances in optimization under uncertainty. It places particular emphasis on scalar, vector, and set-valued optimization problems affected by uncertain data. In addition, the volume showcases a wide range of impactful applications, including inverse and control problems, variational inequalities, equilibrium models, network systems, and real-world challenges in engineering and biomedical sciences.
 
Features
  • The book explores diverse applications, including inverse and control problems, medical imaging, equilibrium models, variational inequalities, and mechanical systems
  • Thematic review chapters offer readers an in-depth understanding of key developments in the field
  • Contributions from world-renowned researchers offer both expertise and high-quality insights.
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Specificații

ISBN-13: 9781041217213
ISBN-10: 1041217218
Pagini: 256
Ilustrații: 64
Dimensiuni: 178 x 254 mm
Ediția:1
Editura: CRC Press
Colecția Chapman and Hall/CRC

Public țintă

Academic and Undergraduate Core

Cuprins

1. Time Span for Dispersive Effect in Generalized KdV Equations  2. Smart Last-Mile Logistics: Designing Flexible Parcel Locker Networks under Heterogeneous Demand Patterns  3. Stochastic Elliptic Variational Problems as Abstract Variational Problems  4. UVIs for Trust-Based Equilibria in Crowd-Sourced Delivery  5. On the Stochastic Continuity for a Random Time-Dependent Spatial Price Equilibrium Problem  6. Optimization Strategies for the Inverse Eigenvector Centrality Problem  7. Parameter Identification for the Time-Dependent Oseen Model  8. Set-Valued Iterations and Stability  9. A Solution Concept for Two-Stage Robust Multiobjective Optimization  10. Error Estimates for Perturbed Variational Inequalities of the Second Kind  11. Identifiability and Identification in the Contact Problem

Notă biografică

Aris Daniilidis is Professor of Applied Mathematics at TU Wien, working in the fields of Variational Analysis, Optimization and Dynamical Systems. Before joining TU Wien, he held faculty positions at the Universitat Autònoma de Barcelona and the University of Chile. He is currently Head of the Research Unit VADOR (Variational Analysis, Dynamics and Operations Research) at TU Wien. He has held numerous visiting positions, mainly in France and Italy, including at École Polytechnique, Paris-Saclay, INSA Rennes, and the universities of Milan and Naples. He has authored approximately 80 research publications in leading international journals in mathematics and optimization. He serves on the editorial boards of several journals in optimization and mathematical analysis.
 
Akhtar A. Khan is a Professor of Applied Mathematics at the Rochester Institute of Technology, Rochester, NY, USA. His research interests encompass inverse problems, uncertainty quantification, optimization, and variational inequalities. He has authored over 120 research articles. Dr. Khan has been a visiting professor at several European universities and serves on the editorial boards of five leading journals. He is also a co-Chief Editor of the Journal of Applied and Numerical Optimization. In addition to his publication record, Dr. Khan has co-authored two monographs, co-edited three books, and co-edited 12 special issues of renowned journals. 
 
Christiane Tammer is a professor at Martin-Luther-University Halle-Wittenberg, Germany, working in the fields of variational methods and optimization. Before taking up this position, she was a Visiting Professor at the Royal Military College of Canada and at the Universities of Leipzig and Kaiserslautern. She has co-authored 6 monographs. MathSciNet lists over 170 papers. She is Editor in Chief of the journal Optimization, Co-Editor in Chief of the Journal Applied Set-Valued Analysis and Optimization and the journal Optimization Eruditorum. Furthermore, she is a member of the Editorial Board of several journals, the Scientific Committee of the Working Group on Generalized Convexity and the Academic Committee of the International Research Center for Mathematical Optimization, Yunnan Normal University, Kunming, China.

Descriere

Optimization under Uncertainty presents a comprehensive overview of recent theoretical and computational advances in optimization under uncertainty. It places particular emphasis on scalar, vector, and set-valued optimization problems affected by uncertain data.