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 |
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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 |