Motivated by the complexity of solving convex scenario problems in one-shot, two new algorithms for the sequential solution of sampled convex optimization problems are presented, for full constraint satisfaction, partial constraint satisfaction, respectively. A rigorous analysis of the theoretical properties of the algorithms is provided,, the related sample complexity is derived. Extensive numerical simulations for a non-trivial example testify the goodness of the proposed solution.
Sequential randomized algorithms for sampled convex optimization
F Dabbene;R Tempo;
2013
Abstract
Motivated by the complexity of solving convex scenario problems in one-shot, two new algorithms for the sequential solution of sampled convex optimization problems are presented, for full constraint satisfaction, partial constraint satisfaction, respectively. A rigorous analysis of the theoretical properties of the algorithms is provided,, the related sample complexity is derived. Extensive numerical simulations for a non-trivial example testify the goodness of the proposed solution.File in questo prodotto:
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