Robust optimization is still a relatively new approach to
optimization problems affected by uncertainty, but it has already
proved so useful in real applications that it is difficult to
tackle such problems today without considering this powerful
methodology. Written by the principal developers of robust
optimization, and describing the main achievements of a decade of
research, this is the first book to provide a comprehensive and
up-to-date account of the subject.
Robust optimization is designed to meet some major challenges
associated with uncertainty-affected optimization problems: to
operate under lack of full information on the nature of
uncertainty; to model the problem in a form that can be solved
efficiently; and to provide guarantees about the performance of the
solution.
The book starts with a relatively simple treatment of uncertain
linear programming, proceeding with a deep analysis of the
interconnections between the construction of appropriate
uncertainty sets and the classical chance constraints
(probabilistic) approach. It then develops the robust optimization
theory for uncertain conic quadratic and semidefinite optimization
problems and dynamic (multistage) problems. The theory is supported
by numerous examples and computational illustrations.
An essential book for anyone working on optimization and
decision making under uncertainty, "Robust Optimization" also makes
an ideal graduate textbook on the subject.
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