Efficiency of random search methods on huge-scale optimization problems
|Affiliation:||Catholic University of Louvain|
|Room:||Mathematics & Computer Building (MC) 5158|
In this talk we discuss new methods for solving huge-scale optimization problems. For problems of this size, even the simplest full-dimensional vector operations are very expensive. Hence, we analyze an optimization technique with random partial update of the decision variables. For our method we prove the global estimates of the rate of convergence. Surprisingly enough, for certain classes of the objective functions, our results are better than the standard worst-case bounds for deterministic methods.
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