Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Distributed Reconstruction of Nonlinear Networks: An ADMM Approach   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. Recently, 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. Using a convex-concave procedure (CCCP), an iterative
reweighted lasso algorithm was derived to solve the initial nonconvex
optimisation problem. By exploiting the structure of the objective function of
this reweighted lasso algorithm, a distributed algorithm can be 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).

33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
23
The categories of this paper are: solv-int
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc
28
The categories of this paper are: alg-geom
21
The categories of this paper are: adap-org
13
The categories of this paper are: quant-ph
15
The categories of this paper are: nucl-ex
7
The categories of this paper are: hep-th
37
The categories of this paper are: supr-con
12
The categories of this paper are: nucl-th
34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
20
The categories of this paper are: acc-phys