First-order Methods for Affinely Constrained Composite Non-convex Non-smooth Problems: Lower Complexity Bound and Near-optimal Methods
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Many recent studies on first-order methods (FOMs) focus on composite non-convex non-smooth optimization with linear and/or nonlinear function constraints. Upper (or worst-case) complexity bounds have been established for these methods. However, little can be claimed about their optimality as no lower bound is known, except for a few special smooth non-convex cases. In this paper, we make the first attempt to establish lower complexity bounds of FOMs for solving a class of composite non-convex non-smooth optimization with linear constraints. Assuming two different first-order oracles, we establish lower complexity bounds of FOMs to produce a (near) ϵ-stationary point of a problem (and its reformulation) in the considered problem class, for any given tolerance ϵ>0. In addition, we present an inexact proximal gradient (IPG) method by using the more relaxed one of the two assumed first-order oracles. The oracle complexity of the proposed IPG, to find a (near) ϵ-stationary point of the considered problem and its reformulation, matches our established lower bounds up to a logarithmic factor. Therefore, our lower complexity bounds and the proposed IPG method are almost non-improvable.
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