On safe tractable approximations of chance constraints |
| |
Authors: | Arkadi Nemirovski |
| |
Institution: | H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, 765 Ferst Dr., NW, Atlanta, GA 30332, United States |
| |
Abstract: | A natural way to handle optimization problem with data affected by stochastic uncertainty is to pass to a chance constrained version of the problem, where candidate solutions should satisfy the randomly perturbed constraints with probability at least 1 − ?. While being attractive from modeling viewpoint, chance constrained problems “as they are” are, in general, computationally intractable. In this survey paper, we overview several simulation-based and simulation-free computationally tractable approximations of chance constrained convex programs, primarily, those of chance constrained linear, conic quadratic and semidefinite programming. |
| |
Keywords: | Uncertainty modeling Convex programming Optimization under uncertainty Chance constraints Robust Optimization |
本文献已被 ScienceDirect 等数据库收录! |
|