In this paper, we propose a class of block coordinate proximal gradient (BCPG) methods for solving large-scale nonsmooth separable optimization problems. The proposed BCPG methods are based on the Bregman functions, which may vary at each iteration. These methods include many well-known optimization methods, such as the quasi-Newton method, the block coordinate descent method, and the proximal point method. For the proposed methods, we establish their global convergence properties when the blocks are selected by the Gauss–Seidel rule. Further, under some additional appropriate assumptions, we show that the convergence rate of the proposed methods is R-linear. We also present numerical results for a new BCPG method with variable kernels for a convex problem with separable simplex constraints.
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We would like to thank the associate editor and the two anonymous reviewers for their constructive comments, which improved this paper significantly. In particular, they encourage us to give the inexact block coordinate descent in Sect. 6 and propose a new method for the convex problem with separable simplex constraints in Sect. 7.
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Hua, X., Yamashita, N. Block coordinate proximal gradient methods with variable Bregman functions for nonsmooth separable optimization. Math. Program. 160, 1–32 (2016). https://doi.org/10.1007/s10107-015-0969-z
- Nonsmooth optimization
- Block coordinate proximal gradient methods
- Linear convergence
- Error bounds
Mathematics Subject Classification