In many engineering design and optimization problems, the presence of uncertainty in the data is a central and critical issue. Different fields of engineering use different ways to describe this uncertainty and adopt a variety of techniques to devise designs that are at least partly insensitive or robust to uncertainty.
Probabilistic and Randomized Methods for Design under Uncertainty examines uncertain systems in control engineering and general decision or optimization problems for which data is not known exactly. Gathering contributions from the worldβs leading researchers in optimization and robust control; this book highlights the interactions between these two fields, and focuses on new randomised and probabilistic techniques for solving design problems in the presence of uncertainty:
- Part I describes general theory and solution methodologies for probability-constrained and stochastic optimization problems, including chance-constrained optimization, stochastic optimization and risk measures;
- Part II focuses on numerical methods for solving randomly perturbed convex programs and semi-infinite optimization problems by probabilistic techniques such as constraint sampling and scenario-based optimization;
- Part III details the theory and applications of randomized techniques to the analysis and design of robust control systems.
Probabilistic and Randomized Methods for Design under Uncertainty will be of interest to researchers, academics and postgraduate students in control engineering and operations research as well as professionals working in operations research who are interested in decision-making, optimization and stochastic modeling.