Abstract
This chapter develops algorithms for parameter optimization under multiple functional (inequality) constraints. Both the objective as well as the constraint functions depend on the parameter and are suitable long-run averages. The Lagrangian relaxation technique is used together with multi-timescale stochastic approximation and algorithms based on gradient and Newton SPSA/SF ideas where the afore-mentioned parameter is updated on a faster timescale as compared to the Lagrange parameters are presented.
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© 2013 Springer-Verlag London
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Bhatnagar, S., Prasad, H., Prashanth, L. (2013). Algorithms for Constrained Optimization. In: Stochastic Recursive Algorithms for Optimization. Lecture Notes in Control and Information Sciences, vol 434. Springer, London. https://doi.org/10.1007/978-1-4471-4285-0_10
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DOI: https://doi.org/10.1007/978-1-4471-4285-0_10
Publisher Name: Springer, London
Print ISBN: 978-1-4471-4284-3
Online ISBN: 978-1-4471-4285-0
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