In this paper, we analyze the convergence of the alternating direction method of multipliers (ADMM) for minimizing a nonconvex and possibly nonsmooth objective function, φ(x 0 , . . . , x p , y), subject to coupled linear equality constraints. Our ADMM updates each of the primal variables x 0 , . . . , x p , y, followed by updating the dual variable. We separate the variable y from x i 's as it has a special role in our analysis. The developed convergence guarantee covers a variety of nonconvex functions such as piecewise linear functions, ℓ q quasi-norm, Schatten-q quasi-norm (0 < q < 1), and SCAD. It also allows nonconvex constraints such as compact manifolds (e.g., spherical, Stiefel, and Grassman manifolds) and linear complementarity constraints. Also, the x 0 -block can be almost any lower semi-continuous function. By applying our analysis, we show, for the first time, that several ADMM algorithms applied to solve nonconvex models in statistical learning, optimization on manifold, optimization over complementarity constraints, and matrix decomposition are guaranteed to converge. Our results provide sufficient conditions for ADMM to converge on (convex or nonconvex) monotropic programs with three or more blocks, as they are special cases of our model. ADMM has been regarded as a variant to the augmented Lagrangian method (ALM). We present a simple example to illustrate how ADMM converges but ALM diverges with bounded penalty parameter β. Indicated by this example and other analysis in this paper, ADMM might be a better choice than ALM for some nonconvex nonsmooth problems, because ADMM is not only easier to implement, it is also more likely to converge for the concerned scenarios.
Wang et al. REF analyzed the convergence of ADMM for minimizing a nonconvex and possibly nonsmooth objective function with coupled linear constraints.
Global Convergence of ADMM in Nonconvex Nonsmooth Optimization
Journal of Scientific Computing, 2018
Mathematics Computer Science