A parallel algorithm for a class of convex programs
SIAM Journal on Control and Optimization
Parallel and distributed computation: numerical methods
Parallel and distributed computation: numerical methods
Mathematical Programming: Series A and B
A proximal-based decomposition method for convex minimization problems
Mathematical Programming: Series A and B
On convergence of an augmented Lagrangian decomposition method for sparse convex optimization
Mathematics of Operations Research
Alternating Projection-Proximal Methods for Convex Programming and Variational Inequalities
SIAM Journal on Optimization
A Lagrangian Dual Method with Self-Concordant Barriers for Multi-Stage Stochastic Convex Programming
Mathematical Programming: Series A and B
Smooth minimization of non-smooth functions
Mathematical Programming: Series A and B
Excessive Gap Technique in Nonsmooth Convex Minimization
SIAM Journal on Optimization
Mathematical Programming: Series A and B
Faster and Simpler Algorithms for Multicommodity Flow and Other Fractional Packing Problems
SIAM Journal on Computing
Decentralized Resource Allocation in Dynamic Networks of Agents
SIAM Journal on Optimization
Improved dual decomposition based optimization for DSL dynamic spectrum management
IEEE Transactions on Signal Processing
Foundations and Trends® in Machine Learning
Distributed Spectrum Management Algorithms for Multiuser DSL Networks
IEEE Transactions on Signal Processing - Part I
On the $O(1/n)$ Convergence Rate of the Douglas-Rachford Alternating Direction Method
SIAM Journal on Numerical Analysis
Path-following gradient-based decomposition algorithms for separable convex optimization
Journal of Global Optimization
Hi-index | 0.00 |
A new algorithm for solving large-scale convex optimization problems with a separable objective function is proposed. The basic idea is to combine three techniques: Lagrangian dual decomposition, excessive gap and smoothing. The main advantage of this algorithm is that it automatically and simultaneously updates the smoothness parameters which significantly improves its performance. The convergence of the algorithm is proved under weak conditions imposed on the original problem. The rate of convergence is $O(\frac {1}{k})$ , where k is the iteration counter. In the second part of the paper, the proposed algorithm is coupled with a dual scheme to construct a switching variant in a dual decomposition framework. We discuss implementation issues and make a theoretical comparison. Numerical examples confirm the theoretical results.