TY - JOUR
T1 - Distributed consensus-based solver for semi-definite programming
T2 - An optimization viewpoint
AU - Li, Weijian
AU - Zeng, Xianlin
AU - Hong, Yiguang
AU - Ji, Haibo
N1 - Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2021/9
Y1 - 2021/9
N2 - This paper aims at the distributed computation for semi-definite programming (SDP) problems over multi-agent networks. Two SDP problems, including a non-sparse case and a sparse case, are transformed into distributed optimization problems, respectively, by fully exploiting their structures and introducing consensus constraints. Inspired by primal–dual and consensus methods, we propose two distributed algorithms for the two cases with the help of projection and derivative feedback techniques. Furthermore, we prove that the algorithms converge to their optimal solutions, and moreover, their convergences rates are evaluated by the duality gap.
AB - This paper aims at the distributed computation for semi-definite programming (SDP) problems over multi-agent networks. Two SDP problems, including a non-sparse case and a sparse case, are transformed into distributed optimization problems, respectively, by fully exploiting their structures and introducing consensus constraints. Inspired by primal–dual and consensus methods, we propose two distributed algorithms for the two cases with the help of projection and derivative feedback techniques. Furthermore, we prove that the algorithms converge to their optimal solutions, and moreover, their convergences rates are evaluated by the duality gap.
KW - Consensus-based algorithm
KW - Distributed optimization
KW - Semi-definite programming
KW - Sparsity
UR - http://www.scopus.com/inward/record.url?scp=85107660145&partnerID=8YFLogxK
U2 - 10.1016/j.automatica.2021.109737
DO - 10.1016/j.automatica.2021.109737
M3 - Article
AN - SCOPUS:85107660145
SN - 0005-1098
VL - 131
JO - Automatica
JF - Automatica
M1 - 109737
ER -