This paper studies the development of Monte Carlo methods to solve semi-infinite, nonlinear programming problems. An equivalent stochastic optimization problem is proposed, which leads to a class of randomized algorithms based on stochastic approximation. The main results of the paper show that almost sure convergence can be established under relatively mild conditions.
|Translated title of the contribution||Randomized Algorithms for Semi-Infinite Programming Problems|
|Title of host publication||Probabilistic and Randomized Methods for Design under Uncertainty|
|Editors||G. Calafiore, F. Dabene|
|Pages||243 - 263|
|Number of pages||21|
|Publication status||Published - 2006|