Fast inexact decomposition algorithms for large-scale separable convex optimization
Fast inexact decomposition algorithms for large-scale separable convex optimization
In this paper, we propose a new inexact dual decomposition algorithm for solving separable convex optimization problems. This algorithm is a combination of three techniques: dual Lagrangian decomposition, smoothing and excessive gap. The algorithm has low computational complexity since it consists in only one primal step and two dual steps …