Comparing different subgradient methods for solving convex optimization problems with functional constraints

01/04/2021

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by   Thi Lan Dinh, et al.

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We provide a dual subgradient method and a primal-dual subgradient method for standard convex optimization problems with complexity 𝒪(ε^-2) and 𝒪(ε^-2r), for all r> 1, respectively. They are based on recent Metel-Takeda's work in [arXiv:2009.12769, 2020, pp. 1-12] and Boyd's method in [Lecture notes of EE364b, Stanford University, Spring 2013-14, pp. 1-39]. The efficiency of our methods is numerically illustrated in a comparison to the others.