Summary
This chapter 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 chapter show that almost sure convergence can be established under relatively mild conditions.
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© 2006 Springer-Verlag London Limited
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Tadić, V.B., Meyn, S.P., Tempo, R. (2006). Randomized Algorithms for Semi-Infinite Programming Problems. In: Calafiore, G., Dabbene, F. (eds) Probabilistic and Randomized Methods for Design under Uncertainty. Springer, London. https://doi.org/10.1007/1-84628-095-8_9
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DOI: https://doi.org/10.1007/1-84628-095-8_9
Publisher Name: Springer, London
Print ISBN: 978-1-84628-094-8
Online ISBN: 978-1-84628-095-5
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