Abstract
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 |
|---|---|
| Original language | English |
| Title of host publication | Probabilistic and Randomized Methods for Design under Uncertainty |
| Editors | G. Calafiore, F. Dabene |
| Publisher | Springer Verlag |
| Pages | 243 - 263 |
| Number of pages | 21 |
| ISBN (Print) | 184628094X |
| Publication status | Published - 2006 |
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