Description
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 systemsThematic review chapters offer readers an in-depth understanding of key developments in the fieldContributions from world-renowned researchers offer both expertise and high-quality insights.