Probabilistic bounds (via large deviations) for the solutions of stochastic programming problems

Abstract

Several exponential bounds are derived by means of the theory of large deviations for the convergence of approximate solutions of stochastic optimization problems. The basic results show that the solutions obtained by replacing the original distribution by an empirical distribution provides an effective tool for solving stochastic programming problems. © 1995 J.C. Baltzer AG, Science Publishers.