Abstract
The problem of adaptive minimization of globally unknown functions under constraints on the independent variable is considered in a stochastic framework. The main contribution of this paper consists in the extension of the CAM algorithm to vector problems. By resorting to the ODE analysis for analyzing stochastic algorithms and singular perturbation methods, it is shown that the only possible convergence points in the vector case are the constrained local minima. Simulations for dimension 2 problems illustrate this result.
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