On safe tractable approximations of chance constraints
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.
chance constraints convex programming uncertainty modeling optimization under uncertainty robust optimization