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In this paper, we propose a solution to the sensor management problem over multiple time instances that balances the accuracy of the sensor network estimation with its utilization. We show how this problem reduces to a binary optimization problem for which we give a convex relaxation based solution that involves the minimization of a regularized ℓ∞ reweighted ℓ1 norm. We show experimentally the behavior...
In local modelling, function estimates are computed from observations in a local neighborhood of the point of interest. A central question is how to choose the size of the neighborhood. Often this question has been tackled using asymptotic (in the number of observations) arguments. The recently introduced direct weight optimization approach is a non-asymptotic approach, minimizing an upper bound on...
In this paper, we propose a new quasi-filled function approach to solve box constrained discrete global optimization problem. This approach enables the objective function to move from one local optimizer to a better one at each iteration until a global optimizer or an approximate global optimizer is obtained. The properties of the proposed quasi-filled function are investigated and a solution algorithm...
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