Abstract
The problem of adaptive minimization of globally unknown functionals under constraints on the independent variable is considered in a stochastic framework. The CAM algorithm for vector problems is proposed. By resorting to the ODE analysis for analysing stochastic algorithms and singular perturbation methods, it is shown that the only possible convergence points are the constrained local minima. Simulation results in 2 dimensions illustrate this result.
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Coito, F.J., Lemos, J.M. (2005). Adaptive Optimization with Constraints: Convergence and Oscillatory Behaviour. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds) Pattern Recognition and Image Analysis. IbPRIA 2005. Lecture Notes in Computer Science, vol 3523. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11492542_3
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DOI: https://doi.org/10.1007/11492542_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26154-4
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