TY - JOUR
T1 - An augmented Lagrangian method for distributed optimization
AU - Chatzipanagiotis, Nikolaos
AU - Dentcheva, Darinka
AU - Zavlanos, Michael M.
N1 - Publisher Copyright:
© 2014, Springer-Verlag Berlin Heidelberg and Mathematical Optimization Society.
PY - 2015/8/24
Y1 - 2015/8/24
N2 - We propose a novel distributed method for convex optimization problems with a certain separability structure. The method is based on the augmented Lagrangian framework. We analyze its convergence and provide an application to two network models, as well as to a two-stage stochastic optimization problem. The proposed method compares favorably to two augmented Lagrangian decomposition methods known in the literature, as well as to decomposition methods based on the ordinary Lagrangian function.
AB - We propose a novel distributed method for convex optimization problems with a certain separability structure. The method is based on the augmented Lagrangian framework. We analyze its convergence and provide an application to two network models, as well as to a two-stage stochastic optimization problem. The proposed method compares favorably to two augmented Lagrangian decomposition methods known in the literature, as well as to decomposition methods based on the ordinary Lagrangian function.
KW - Alternating direction method
KW - Convex optimization
KW - Diagonal quadratic approximation
KW - Monotropic programming
KW - Network optimization
KW - Stochastic programming
UR - http://www.scopus.com/inward/record.url?scp=84937951735&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84937951735&partnerID=8YFLogxK
U2 - 10.1007/s10107-014-0808-7
DO - 10.1007/s10107-014-0808-7
M3 - Article
AN - SCOPUS:84937951735
SN - 0025-5610
VL - 152
SP - 405
EP - 434
JO - Mathematical Programming
JF - Mathematical Programming
IS - 1-2
ER -