Consistency Constraint Allocation in Augmented Lagrangian Coordination

Author:

Allison James T.1,Papalambros Panos Y.2

Affiliation:

1. The Mathworks, Inc., Natick, MA 01760

2. Department of Mechanical Engineering, University of Michigan, G.G. Brown Building, Ann Arbor, MI 48109

Abstract

Many engineering systems are too complex to design as a single entity. Decomposition-based design optimization methods partition a system design problem into subproblems, and coordinate subproblem solutions toward an optimal system design. Recent work has addressed formal methods for determining an ideal system partition and coordination strategy, but coordination decisions have been limited to subproblem sequencing. An additional element in a coordination strategy is the linking structure of the partitioned problem, i.e., the allocation of constraints that guarantee that the linking variables among subproblems are consistent. There may exist many alternative linking structures for a decomposition-based strategy that can be selected for a given partition, and this selection should be part of an optimal simultaneous partitioning and coordination scheme. This article develops a linking structure theory for a particular class of decomposition-based optimization algorithms, augmented Lagrangian coordination (ALC). A new formulation and coordination technique for parallel ALC implementations is introduced along with a specific linking structure theory, yielding a partitioning and coordination selection method for ALC that includes consistency constraint allocation. This method is demonstrated using an electric water pump design problem.

Publisher

ASME International

Subject

Computer Graphics and Computer-Aided Design,Computer Science Applications,Mechanical Engineering,Mechanics of Materials

Reference18 articles.

1. Constructive Combinatorics

2. Optimal Partitioning and Coordination Decisions in Decomposition-Based Design Optimization;Allison;ASME J. Mech. Des.

3. Braun, R. D. , 1996, “Collaborative Optimization: An Architecture for Large-Scale Distributed Design,” Ph.D. thesis, Stanford University, Stanford, CA.

4. Target Cascading in Optimal System Design;Kim;ASME J. Mech. Des.

5. An Augmented Lagrangian Decomposition Method for Quasiseparable Problems in MDO;Tosserams;Struct. Multidiscip. Optim.

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