Minimizing Functions with Bounded Variation of Subgradients

In many applications it is possible to justify a reasonable bound for possible variation of subgradients of objective function rather than for their uniform magnitude. In this paper we develop a new class of efficient primal-dual subgradient schemes for such problem classes.

Related Organizations

University College London
United Kingdom
Université Catholique de Louvain
Belgium

Keywords

convex optimization, subgradient methods, non-smooth optimization, blackbox methods, lower complexity bounds

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