0O
A01=Aharon Ben-Tal
A01=Arkadi Nemirovski
A01=Laurent El Ghaoui
Accuracy and precision
Additive model
Almost surely
Approximation
Approximation algorithm
Author_Aharon Ben-Tal
Author_Arkadi Nemirovski
Author_Laurent El Ghaoui
Best
Big O notation
Candidate solution
Category=PBUH
Central limit theorem
Chaos theory
Coefficient
Computational complexity theory
Convex optimization
Curse of dimensionality
Decision problem
Decision rule
Degeneracy (mathematics)
Diagram (category theory)
Duality (optimization)
Dynamic programming
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
Feasible region
For All Practical Purposes
Identity matrix
Inequality (mathematics)
Infimum and supremum
Law of large numbers
Linear dynamical system
Linear inequality
Linear map
Linear matrix inequality
Linear programming
Loss function
Markov chain
Mathematical optimization
Maxima and minima
Norm (mathematics)
Normal distribution
NP-hardness
Optimal control
Optimization problem
Pairwise
Parameter
Parametric family
Probability
Probability distribution
Quantity
Relative interior
Robust control
Robust decision-making
Robust optimization
Sensitivity analysis
Simple set
Singular value
Slack variable
Special case
Spherical model
Spline (mathematics)
Stochastic
Stochastic optimization
Stochastic programming
Strong duality
Theorem
Time complexity
Uncertainty
Upper and lower bounds
Variable (mathematics)
Virtual displacement
Weak duality
With high probability
worst and average case
Product details
- ISBN 9780691143682
- Weight: 1304g
- Dimensions: 178 x 254mm
- Publication Date: 30 Aug 2009
- Publisher: Princeton University Press
- Publication City/Country: US
- Product Form: Hardback
Secure
checkout
Fast Shipping
Easy returns
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.
Aharon Ben-Tal is professor of operations research at the Technion, Israel Institute for Technology. Laurent El Ghaoui is associate professor of electrical engineering and operations research at the University of California, Berkeley. Arkadi Nemirovski is professor of industrial and systems engineering at Georgia Institute of Technology.
