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In this talk we consider optimization algorithms using tensor-train manifolds as a local search sets in successive iteration steps. We also discuss efficient implementations of the cross- tensor-train procedures and recent developments of theory explaing why these algorithms work so nice in practice. Besides that, we consider some applications, in particular the new advanced algorithms for numerical solution of the Smoluchowsky-type equations.