Applied mathematics in water supply network management
Automatica (Journal of IFAC) - IFAC-IEEE special issue on meeting the challenge of computer science in the industrial applications of control
Smooth minimization of non-smooth functions
Mathematical Programming: Series A and B
Multi-agent model predictive control for transportation networks: Serial versus parallel schemes
Engineering Applications of Artificial Intelligence
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
SIAM Journal on Imaging Sciences
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
SIAM Journal on Imaging Sciences
Hi-index | 22.14 |
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(1k^2), where k is the iteration number, which significantly improves the convergence rates of existing duality-based distributed optimization algorithms that achieve O(1k). The performance of the developed algorithm is evaluated on randomly generated optimization problems arising in distributed model predictive control (DMPC). The evaluation shows that, when the problem data is sparse and large-scale, our algorithm can outperform current state-of-the-art optimization software CPLEX and MOSEK.