TY - JOUR
T1 - Multi-modality in augmented Lagrangian coordination for distributed optimal design
AU - Tosserams, S.
AU - Etman, L.F.P.
AU - Rooda, J.E.
PY - 2010
Y1 - 2010
N2 - This paper presents an empirical study of the convergence characteristics of augmented Lagrangian coordination (ALC) for solving multi-modal optimization problems in a distributed fashion. A number of test problems that do not satisfy all assumptions of the convergence proof for ALC are selected to demonstrate the convergence characteristics of ALC algorithms. When only a local search is employed at the subproblems, local solutions to the original problem are often attained. When a global search is performed at subproblems, global solutions to the original, non-decomposed problem are found for many of the examples. Although these findings are promising, ALC with a global subproblem search may yield only local solutions in the case of non-convex coupling functions or disconnected feasible domains. Results indicate that for these examples both the starting point and the sequence in which subproblems are solved determines which solution is obtained. We illustrate that the main cause for this behavior lies in the alternating minimization inner loop, which is inherently of a local nature. © 2009 The Author(s).
AB - This paper presents an empirical study of the convergence characteristics of augmented Lagrangian coordination (ALC) for solving multi-modal optimization problems in a distributed fashion. A number of test problems that do not satisfy all assumptions of the convergence proof for ALC are selected to demonstrate the convergence characteristics of ALC algorithms. When only a local search is employed at the subproblems, local solutions to the original problem are often attained. When a global search is performed at subproblems, global solutions to the original, non-decomposed problem are found for many of the examples. Although these findings are promising, ALC with a global subproblem search may yield only local solutions in the case of non-convex coupling functions or disconnected feasible domains. Results indicate that for these examples both the starting point and the sequence in which subproblems are solved determines which solution is obtained. We illustrate that the main cause for this behavior lies in the alternating minimization inner loop, which is inherently of a local nature. © 2009 The Author(s).
U2 - 10.1007/s00158-009-0371-7
DO - 10.1007/s00158-009-0371-7
M3 - Article
SN - 1615-147X
VL - 40
SP - 329
EP - 352
JO - Structural and Multidisciplinary Optimization
JF - Structural and Multidisciplinary Optimization
IS - 1-6
ER -