In many engineering design and optimisation 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 optimisation problems for which data is not
known exactly. Gathering contributions from the world s leading
researchers in optimisation 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 optimisation, stochastic optimization
and risk measures;
- Part II focuses on numerical methods for solving randomly
perturbed convex programs and semi-infinite optimisation problems
by probabilistic techniques such as constraint sampling and
scenario-based optimisation;
- Part III details the theory and applications of randomised
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 reasearch
who are interested in decision-making, optimisation and stochastic
modelling."
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