A Distributed Primal Decomposition Scheme for Nonconvex Optimization

Camisa, Andrea; Notarstefano, Giuseppe

doi:10.1016/j.ifacol.2019.12.174

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…A locally convergent decentralized bi-level Sequential Convex Programming (SCP) method with dual decomposition as an inner method is presented in [7] for problems with convex objective, convex inequality constraints, and nonlinear equality constraints. For problems with convex cost and non-convex local constraints, a decentralized primal decomposition method is presented in [8] but no convergence guarantees are established. A distributed method for problems with non-convex objectives and convex compact constraint sets is presented in [9].…”

Section: Introductionmentioning

confidence: 99%

Decentralized non-convex optimization via bi-level SQP and ADMM

Stomberg¹,

Engelmann²,

Faulwasser³

2022

Preprint

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Non-convex optimization problems arise in many problems of practical relevance-for example in distributed nonlinear MPC or distributed optimal power flow. Only few existing decentralized optimization methods have local convergence guarantees for general nonconvex problems. We present novel convergence results for non-convex problems for a bi-level SQP method that solves the inner quadratic problems via ADMM. A decentralized stopping criterion borrowed from inexact Newton methods allows the early termination of ADMM as an inner algorithm to improve computational efficiency. The method shows competitive numerical performance to existing methods for an optimal power flow problem.

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Section: Introductionmentioning

confidence: 99%