An Augmented Lagrangian Coordination-Decomposition Algorithm for Solving Distributed Non-Convex Programs

A novel augmented Lagrangian method for solving non-convex programs with nonlinear cost and constraint couplings in a distributed framework is presented. The proposed decomposition algorithm is made of two layers: The outer level is a standard multiplier method with penalty on the nonlinear equality constraints, while the inner level consists of a block-coordinate descent (BCD) scheme. Based on standard results on multiplier methods and recent results on proximal regularised BCD techniques, it is proven that the method converges to a KKT point of the non-convex nonlinear program under a semi-algebraicity assumption. Efficacy of the algorithm is demonstrated on a numerical example.

Published in:
Proceedings of the 2014 American Control Conference
Presented at:
2014 American Control Conference, Portland, Oregon, USA, June 4-6, 2014

 Record created 2014-03-10, last modified 2018-01-28

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