Linearly convergent decentralized consensus optimization with the alternating direction method of multipliers
Wei Shi, Qing Hua Ling, Kun Yuan, Gang Wu, Wotao Yin · 2013
In the decentralized consensus optimization problem, a network of agents minimizes the summation of their local objective functions on a common set of variables, allowing only information exchange among neighbors. The alternating direction method of multipliers (ADMM) has been shown to be a powerful tool for solving the problem with empirically fast convergence. This paper establishes the linear convergence rate of the ADMM in decentralized consensus optimization. The theoretical convergence rate is a function of the network topology, properties of the local objective functions, and the algorithm parameter. This result not only gives a performance guarantee for the ADMM but also provides a guideline to accelerate its convergence rate for the decentralized consensus optimization problems.