Includes bibliographical references and index.

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 opera.

Print version record.

In English.

pt. I. Robust Linear Optimization -- Ch. 1. Uncertain Linear Optimization Problems and their Robust Counterparts -- Ch. 2. Robust Counterpart Approximations of Scalar Chance Constraints -- Ch. 3. Globalized Robust Counterparts of Uncertain LO Problems -- Ch. 4. More on Safe Tractable Approximations of Scalar Chance Constraints -- pt. II. Robust Conic Optimization -- Ch. 5. Uncertain Conic Optimization: The Concepts -- Ch. 6. Uncertain Conic Quadratic Problems with Tractable RCs -- Ch. 7. Approximating RCs of Uncertain Conic Quadratic Problems -- Ch. 8. Uncertain Semidefinite Problems with Tractable RCs -- Ch. 9. Approximating RCs of Uncertain Semidefinite Problems -- Ch. 10. Approximating Chance Constrained CQIs and LMIs -- Ch. 11. Globalized Robust Counterparts of Uncertain Conic Problems -- Ch. 12. Robust Classification and Estimation -- pt. III. Robust Multi-Stage Optimization -- Ch. 13. Robust Markov Decision Processes -- Ch. 14. Robust Adjustable Multistage Optimization -- pt. IV. Selected Applications -- Ch. 15. Selected Applications -- App. A. Notation and Prerequisites -- App. B. Some Auxiliary Proofs -- App. C. Solutions to Selected Exercises -- Index.

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