TY - GEN
T1 - Distributed reconstruction of nonlinear networks
T2 - 19th IFAC World Congress on International Federation of Automatic Control, IFAC 2014
AU - Pan, Wei
AU - Sootla, Aivar
AU - Stan, Guy Bart
N1 - Funding Information:
W. Pan gratefully acknowledge the support of Microsoft Research through the PhD Scholarship Program. A. Sootla and G.-B. Stan acknowledge the support of EPSRC through the project EP/J014214/1 and the EPSRC Science and Innovation Award EP/G036004/1.
Publisher Copyright:
© IFAC.
PY - 2014
Y1 - 2014
N2 - In this paper, we present a distributed algorithm for the reconstruction of large-scale nonlinear networks. In particular, we focus on the identification from time-series data of the nonlinear functional forms and associated parameters of large-scale nonlinear networks. In the previous work, a nonlinear network reconstruction problem was formulated as a nonconvex optimisation problem based on the combination of a marginal likelihood maximisation procedure with sparsity inducing priors. In this paper, we derive an iterative reweighted lasso algorithm to solve the initial nonconvex optimisation problem based on the concave-convex procedure (CCCP). Moreover, by exploiting the structure of the objective function of this algorithm, a distributed algorithm is designed. To this end, we apply the alternating direction method of multipliers (ADMM) to decompose the original problem into several subproblems. To illustrate the effectiveness of the proposed methods, we use our approach to identify a network of interconnected Kuramoto oscillators with different network sizes (500∼100,000 nodes).
AB - In this paper, we present a distributed algorithm for the reconstruction of large-scale nonlinear networks. In particular, we focus on the identification from time-series data of the nonlinear functional forms and associated parameters of large-scale nonlinear networks. In the previous work, a nonlinear network reconstruction problem was formulated as a nonconvex optimisation problem based on the combination of a marginal likelihood maximisation procedure with sparsity inducing priors. In this paper, we derive an iterative reweighted lasso algorithm to solve the initial nonconvex optimisation problem based on the concave-convex procedure (CCCP). Moreover, by exploiting the structure of the objective function of this algorithm, a distributed algorithm is designed. To this end, we apply the alternating direction method of multipliers (ADMM) to decompose the original problem into several subproblems. To illustrate the effectiveness of the proposed methods, we use our approach to identify a network of interconnected Kuramoto oscillators with different network sizes (500∼100,000 nodes).
UR - http://www.scopus.com/inward/record.url?scp=84929791890&partnerID=8YFLogxK
U2 - 10.3182/20140824-6-za-1003.02602
DO - 10.3182/20140824-6-za-1003.02602
M3 - Conference contribution
AN - SCOPUS:84929791890
T3 - IFAC Proceedings Volumes (IFAC-PapersOnline)
SP - 3208
EP - 3213
BT - 19th IFAC World Congress IFAC 2014, Proceedings
A2 - Boje, Edward
A2 - Xia, Xiaohua
PB - International Federation of Automatic Control (IFAC)
Y2 - 24 August 2014 through 29 August 2014
ER -