Probabilistic and Randomized Methods for Design Under Uncertainty

In many engineering design and optimisation problems, the presence of uncertainty in the data is a critical issue. Different fields of engineering use different ways to describe this uncertainty and adopt a variety of techniques to devise designs that are partly insensitive or even robust to uncertainty.

This book examines uncertain systems for which data is not known exactly. Written by the world's leading researchers in optimisation and robust control; the interactions between these two fields are discussed, as well as 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;

Part II focuses on numerical methods for solving randomly perturbed convex programs and semi-infinite optimisation problems by probabilistic techniques;

Part III details the theory and applications of randomised techniques to the analysis and design of robust control systems.

This book will be of interest to researchers, academics and postgraduate students in this field as well as professionals working in operations research who are interested in the subject matter.