Accelerated gradient methods and dual decomposition in distributed model predictive control
We propose a distributed optimization algorithm for mixed L_1/L_2-norm optimization based on accelerated gradient methods using dual decomposition. The algorithm achieves convergence rate O(1/k^2), where k is the iteration number, which significantly improves the convergence rates of existing duality-based distributed optimization algorithms that achieve O(1/k). The performance of the developed al