
Robust Optimization.
Title:
Robust Optimization.
Author:
Ben-Tal, Aharon.
ISBN:
9781400831050
Personal Author:
Physical Description:
1 online resource (565 pages)
Series:
Princeton Series in Applied Mathematics
Contents:
Cover -- Title -- Copyright -- Contents -- Preface -- PART I. ROBUST LINEAR OPTIMIZATION -- Chapter 1. Uncertain Linear Optimization Problems and their Robust Counterparts -- 1.1 Data Uncertainty in Linear Optimization -- 1.2 Uncertain Linear Problems and their Robust Counterparts -- 1.3 Tractability of Robust Counterparts -- 1.4 Non-Affine Perturbations -- 1.5 Exercises -- 1.6 Notes and Remarks -- Chapter 2. Robust Counterpart Approximations of Scalar Chance Constraints -- 2.1 How to Specify an Uncertainty Set -- 2.2 Chance Constraints and their Safe Tractable Approximations -- 2.3 Safe Tractable Approximations of Scalar Chance Constraints: Basic Examples -- 2.4 Extensions -- 2.5 Exercises -- 2.6 Notes and Remarks -- Chapter 3. Globalized Robust Counterparts of Uncertain LO Problems -- 3.1 Globalized Robust Counterpart-Motivation and Definition -- 3.2 Computational Tractability of GRC -- 3.3 Example: Synthesis of Antenna Arrays -- 3.4 Exercises -- 3.5 Notes and Remarks -- Chapter 4. More on Safe Tractable Approximations of Scalar Chance Constraints -- 4.1 Robust Counterpart Representation of a Safe Convex Approximation to a Scalar Chance Constraint -- 4.2 Bernstein Approximation of a Chance Constraint -- 4.3 From Bernstein Approximation to Conditional Value at Risk and Back -- 4.4 Majorization -- 4.5 Beyond the Case of Independent Linear Perturbations -- 4.6 Exercises -- 4.7 Notes and Remarks -- PART II. ROBUST CONIC OPTIMIZATION -- Chapter 5. Uncertain Conic Optimization: The Concepts -- 5.1 Uncertain Conic Optimization: Preliminaries -- 5.2 Robust Counterpart of Uncertain Conic Problem: Tractability -- 5.3 Safe Tractable Approximations of RCs of Uncertain Conic Inequalities -- 5.4 Exercises -- 5.5 Notes and Remarks -- Chapter 6. Uncertain Conic Quadratic Problems with Tractable RCs -- 6.1 A Generic Solvable Case: Scenario Uncertainty.
6.2 Solvable Case I: Simple Interval Uncertainty -- 6.3 Solvable Case II: Unstructured Norm-Bounded Uncertainty -- 6.4 Solvable Case III: Convex Quadratic Inequality with Unstructured Norm-Bounded Uncertainty -- 6.5 Solvable Case IV: CQI with Simple Ellipsoidal Uncertainty -- 6.6 Illustration: Robust Linear Estimation -- 6.7 Exercises -- 6.8 Notes and Remarks -- Chapter 7. Approximating RCs of Uncertain Conic Quadratic Problems -- 7.1 Structured Norm-Bounded Uncertainty -- 7.2 The Case of ∩-Ellipsoidal Uncertainty -- 7.3 Exercises -- 7.4 Notes and Remarks -- Chapter 8. Uncertain Semidefinite Problems with Tractable RCs -- 8.1 Uncertain Semidefinite Problems -- 8.2 Tractability of RCs of Uncertain Semidefinite Problems -- 8.3 Exercises -- 8.4 Notes and Remarks -- Chapter 9. Approximating RCs of Uncertain Semidefinite Problems -- 9.1 Tight Tractable Approximations of RCs of Uncertain SDPs with Structured Norm-Bounded Uncertainty -- 9.2 Exercises -- 9.3 Notes and Remarks -- Chapter 10. Approximating Chance Constrained CQIs and LMIs -- 10.1 Chance Constrained LMIs -- 10.2 The Approximation Scheme -- 10.3 Gaussian Majorization -- 10.4 Chance Constrained LMIs: Special Cases -- 10.5 Notes and Remarks -- Chapter 11. Globalized Robust Counterparts of Uncertain Conic Problems -- 11.1 Globalized Robust Counterparts of Uncertain Conic Problems: Definition -- 11.2 Safe Tractable Approximations of GRCs -- 11.3 GRC of Uncertain Constraint: Decomposition -- 11.4 Tractability of GRCs -- 11.5 Illustration: Robust Analysis of Nonexpansive Dynamical Systems -- Chapter 12. Robust Classification and Estimation -- 12.1 Robust Support Vector Machines -- 12.2 Robust Classification and Regression -- 12.3 Affine Uncertainty Models -- 12.4 Random Affine Uncertainty Models -- 12.5 Exercises -- 12.6 Notes and remarks -- PART III. ROBUST MULTI-STAGE OPTIMIZATION.
Chapter 13. Robust Markov Decision Processes -- 13.1 Markov Decision Processes -- 13.2 The Robust MDP Problems -- 13.3 The Robust Bellman Recursion on Finite Horizon -- 13.4 Notes and Remarks -- Chapter 14. Robust Adjustable Multistage Optimization -- 14.1 Adjustable Robust Optimization: Motivation -- 14.2 Adjustable Robust Counterpart -- 14.3 Affinely Adjustable Robust Counterparts -- 14.4 Adjustable Robust Optimization and Synthesis of Linear Controllers -- 14.5 Exercises -- 14.6 Notes and Remarks -- PART IV. SELECTED APPLICATIONS -- Chapter 15. Selected Applications -- 15.1 Robust Linear Regression and Manufacturing of TV Tubes -- 15.2 Inventory Management with Flexible Commitment Contracts -- 15.3 Controlling a Multi-Echelon Multi-Period Supply Chain -- Appendix A. Notation and Prerequisites -- A.1 Notation -- A.2 Conic Programming -- A.3 Efficient Solvability of Convex Programming -- Appendix B. Some Auxiliary Proofs -- B.1 Proofs for Chapter 4 -- B.2 S-Lemma -- B.3 Approximate S-Lemma -- B.4 Matrix Cube Theorem -- B.5 Proofs for Chapter 1 -- Appendix C. Solutions to Selected Exercises -- C.1 Chapter 1 -- C.2 Chapter 2 -- C.3 Chapter 3 -- C.4 Chapter 4 -- C.5 Chapter 5 -- C.6 Chapter 6 -- C.7 Chapter 7 -- C.8 Chapter 8 -- C.9 Chapter 9 -- C.10 Chapter 12 -- C.11 Chapter 14 -- Bibliography -- Index -- A -- B -- C -- D -- E -- F -- G -- I -- L -- M -- N -- P -- R -- S -- T -- U -- W.
Abstract:
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.
Local Note:
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2017. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
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